Drones doi: 10.3390/drones10090700

Authors: Mingyu Lee Soohyun Kim Jeongho Kim Kyounghun Kim Youngghyu Sun Joonho Seon Jin Young Kim

Computing services can be extended to infrastructure-limited areas through unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC). However, service provision continues to be challenging when spatially clustered ground users (GUs) are initially unknown and their intermittent workloads expire within finite validity windows. Therefore, conventional trajectory controllers assuming known GU locations and requests are unsuitable under sensing-limited conditions, where out-of-range workloads remain hidden between visits. Partially observable control has been adopted to address these challenges; however, existing frameworks are insufficient, as the discovery of unknown service regions and the repeated revisits required to re-observe hidden demand are not jointly considered. In this paper, a deadline-paced patrol with goal-conditioned multi-agent reinforcement learning (DPP-GCMARL) framework is proposed. In the proposed framework, a rule-based deadline-paced patrol (DPP) layer converts sensing-derived cell memory into deconflicted target cells using staleness normalized by the collection deadline. The assigned targets are then mapped to continuous movement commands by a learned goal-conditioned movement (GCM) layer. Simulation results show that DPP-GCMARL improves the end-to-end completed-workload ratio compared with multi-agent proximal policy optimization (MAPPO). It also increases exploration-coupled fairness and the worst-cluster collection ratio while reducing the mean travel per UAV. These results substantiate the proposed framework’s potential for deadline-constrained MEC service in infrastructure-limited environments.

Drones doi: 10.3390/drones10090698

Authors: Pasquale Ferrentino Rui Wu Stefano Nuzzo Luca Girardi Joost Brancart Bram Vanderborght Stefano Mintchev

Traditional UAVs often suffer severe structural damage during high-speed collisions. Recent research has explored sensing, control, and design strategies to enhance collision resilience. This work presents fully passive, soft continuum-lattice arms that combine visco-hyperelastic materials with nonlinear lattice geometries to trigger controlled buckling of the beams and absorb impact energy. Visco-hyperelastic parameters are identified from quasi-static and dynamic tensile tests on the material and then implemented in finite element models to optimize lattice porosity and distribution for force mitigation under quasi-static and dynamic loading conditions. Experimental validation of the simulations for the optimized beam configuration shows good agreement, with MAE 6.7% and RMSE 7% in quasi-static loading and MAE 12.1% and RMSE 14% in dynamic loading. Controlled drop tests on a quadrotor prototype demonstrate superior impact energy absorption and force mitigation compared with a bulk material design (up to a 99% increase in specific energy absorption and up to a 65% reduction in peak impact force). Furthermore, the latticed UAV is flown voluntarily and then dropped onto the ground, maintaining low thrust losses of 0.42 ± 0.26% and structural integrity after impact.

Drones doi: 10.3390/drones10090699

Authors: Kaijun Xu Yilin Hong Hongda Luo Yong Yang

Urban low-altitude unmanned aerial vehicle (UAV) planning is inherently spatiotemporal because route feasibility and cost depend on segment arrival times. Search-stage surrogates may therefore favor paths that fail execution-level checks as moving obstacles, temporary no-fly zones, wind-dependent energy use, and building-clearance constraints evolve. We address this search–execution mismatch with the Risk-Aware Artificial Lemming Algorithm (RA-ALA), a three-layer framework governed by a common arrival-time-recursive evaluator. Sequential temporal propagation aligns candidate generation with final assessment, while an energy-weighted A* (Energy-A*) warm start guides continuous waypoint search. The Top-K stage then re-evaluates path variants before feasibility-first selection and conditional recovery. Under prespecified algorithm-specific budgets across 10 High-complexity environments, RA-ALA achieved the highest observed evaluator-feasible rate (24/30, 80.0%), 20 percentage points higher than Energy-A* and space–time Energy-A* (ST-EA*). After Holm adjustment, these contrasts were nonsignificant, while differences against Informed-RRT* and Greedy were supported. Within jointly feasible environments, RA-ALA retained competitive composite scores. Same-cohort descriptive ablation associated Top-K removal with higher composite scores and more infeasible outputs. These results support RA-ALA as a simulation-tested route-generation framework under the modeled constraints, without establishing isolated-operator superiority or real-flight readiness. Vehicle dynamics, sensing, tracking, communications, and flight validation remain outside this scope.

Drones doi: 10.3390/drones10090697

Authors: Zican Jiang Yongjun Li Qin Tian Kai Zhang Yu Li Jianguo Liu

Terahertz (THz) integrated sensing and communication (ISAC) is a pivotal paradigm for enabling high-rate, ultra-reliable air-to-ground (A2G) connectivity in sixth-generation (6G) unmanned aerial vehicle (UAV) networks. From a physical propagation and environment-aware transmission perspective, current THz channel models inadequately characterize dynamic obstacle scattering, neglect sensing-communication shared clusters, and lack closed-loop frameworks leveraging sensing to assist communication. To bridge these gaps, this paper proposes a novel 3D non-stationary geometry-based stochastic model (GBSM) for THz UAV A2G MIMO channels operating at 300 GHz. The model explicitly incorporates obstacle-induced scattering with Radar Cross Section (RCS)-dependent properties, employing single-point models for small obstacles and multi-point models for large obstacles to capture multipath structures, Doppler effects, molecular absorption, and dynamic cluster evolution. Furthermore, we develop a shared cluster identification framework utilizing delay-angle similarity metrics combined with the Hungarian algorithm to match background scatterers with target-induced paths. Building upon this physical model, a closed-loop sensing-assisted adaptive transmission scheme is established, integrating obstacle-trajectory-driven Kalman channel prediction, affected subchannel avoidance, and waterfilling power allocation. Simulation results demonstrate that the proposed framework accurately characterizes physical THz propagation environments and significantly improves system capacity and reliability, demonstrating the efficacy of environment-level sensing-assisted communication in dynamic THz UAV scenarios.

Drones doi: 10.3390/drones10090696

Authors: Yuhua Cong Yujia Li Huijuan Zhu Zhisheng Wang

This paper addresses low-altitude navigation of multiple UAVs through a shared two-dimensional environment with narrow passages, blocked cells, and predicted moving obstacles while preserving an assigned task order. The planner receives ordered task bundles from an allocator and checks static obstacles, moving-obstacle timing, sampled minimum inter-UAV separation, deadlines, risk budgets, and an energy proxy. It generates risk-weighted candidate path segments, repairs timing conflicts with waits and local detours, verifies service and terminal occupancy, and returns a certificate that records whether a segment is executable, its total travel cost, risk exposure, and energy proxy, or the reason for failure. We compare no feedback, context no-good, typed-failure, quantitative, and combined feedback under medium-load and high-stress test suites. Quantitative feedback lowers risk per completed task in both suites after correction for multiple comparisons. Failure-type feedback adds no detectable benefit, and completion-rate differences do not remain significant after the same correction. Fixed-bundle simulations show that the proposed planner can preserve scheduled executability while reducing threat exposure relative to a spatiotemporal-priority baseline. Single-UAV flights demonstrate waypoint execution, reference tracking, and avoidance of designated regions.

Drones doi: 10.3390/drones10090695

Authors: Bowen Xu Peinan He Xu Wang Yixiao Zhang Yuanjie Zhao

The journal retracts the article titled “A Multimodal Data Fusion Algorithm for Urban Low-Altitude UAV Perception” [...]

Drones doi: 10.3390/drones10090694

Authors: Pengcheng Duan Yueneng Yang Yunbao Fan Xiangen Tang

As unmanned aerial vehicle (UAV) missions expand from aerial inspection and environmental sensing to physical interaction and autonomous manipulation, rotary-wing UAVs equipped with flexible manipulators offer a promising platform for contact-rich operations in complex environments. Among these tasks, stable near-ground hovering and the grasping of ground targets are particularly challenging because they involve ground-effect aerodynamics, rigid–flexible coupling, and mode transitions caused by contact and load transfer. This review provides a structured, task-oriented critical synthesis of advances in this interdisciplinary field. First, system configurations are classified by aerial-platform architecture, manipulator type, mounting arrangement, and end-effector design, and the suitability of rigid-link, compliant, continuum, and soft manipulation mechanisms for near-ground grasping is assessed. Next, modeling approaches for rotor ground effect, coupled rigid–flexible dynamics, hybrid contact and load-transfer dynamics, model identification, and model reduction are reviewed. Trajectory planning, coordinated stabilization, impedance control, hybrid force/position control, and switching control are then compared across free flight, contact establishment, and payload-carrying hover. Although the reviewed literature provides a substantial theoretical foundation for aerial manipulation, continuum robotics, and multirotor ground effect, direct evidence remains limited for methods that jointly address near-ground aerodynamics, large flexible deformation, and contact-induced load transfer across the complete near-ground grasping sequence with integrated experimental validation. Based on these evidence gaps, this review identifies multiphysics reduced-order modeling and event-driven hybrid control as author-synthesized directions for future investigation.

Drones doi: 10.3390/drones10090693

Authors: Zdravko Terze Dario Zlatar Marko Kasalo Marijan Andrić

The nonlinear nature of quadrotor dynamics requires high-fidelity reconstruction of vehicle position and attitude. In order to mitigate singularities associated with global three-parameter representations—such as Euler angles—the quadrotor attitude is conventionally parameterized via unit quaternions and reconstructed by integrating quaternion differential equations. However, the standard quaternion integration procedure is structure non-preserving. It is based on a set of linear differential equations that subsequently enforces the unitary norm of the quaternion through additional algebraic equations. To this end, the paper presents the utilization of the recently introduced structure-preserving attitude and position update algorithms, based on Lie groups, that overcome the drawbacks of the standard procedures. The numerical test cases involve UAV performing five different maneuvers. It is shown that conventional algorithms may suffer from instabilities and errors in kinematical update of position and attitude, which can be circumvented by using the geometric structure-preserving (Lie group-based) algorithms presented here.

Drones doi: 10.3390/drones10090692

Authors: Zixuan Zhang Jingyu Li Yongsheng Qi Jianqiang Su

Image disturbances caused by platform ego-motion are coupled with true target motion under the dynamic viewpoints of unmanned ground vehicles (UGVs), resulting in trajectory-prediction drift, association mismatches, and persistent contamination of identity information by unreliable observations. This paper proposes ERM-Track, a disturbance-aware and reliability-guided online multi-object tracking framework. I2DF-Mamba uses a causal dual-stream Mamba encoder to integrate IMU, joint-state, and trajectory histories and predicts a trajectory-specific image-disturbance distribution. The predicted disturbance mean and uncertainty are mapped explicitly from normalized/log-scale disturbance coordinates to the detection-observation space. CF-TUR then estimates observation reliability through reliable–contaminated posterior fusion and causal evidence accumulation and generates separate bounded write gains for the motion state and identity memory. On the 6488-frame sealed holdout set of the additionally annotated CEAR data, ERM-Track obtains 64.34% HOTA, 66.28% AssA, and 73.42% IDF1, with 28 identity switches. Three-seed backbone replacement experiments show that Mamba provides the highest mean HOTA among the evaluated causal encoders. Post hoc isotonic calibration reduces ECE from 0.4752 to 0.0478, with only marginal changes in the tracking metrics. The complete pipeline reaches 69.50 FPS on an RTX 4080 workstation and an onboard mean latency of approximately 29 ms on a Jetson Orin NX, corresponding to a reciprocal processing rate of approximately 34.5 FPS.

Drones doi: 10.3390/drones10090691

Authors: Yidong Liu Dali Ding Huan Zhou Mulai Tan Qi Zhang Panpan Han

To address the challenge of dynamic target allocation in multi-unmanned aerial vehicle (UAV) aerial games, this paper proposes a hierarchical solution method that combines matrix game dimensionality reduction with the Eigen-Crossover Differential Evolution (ECDE) algorithm. Assuming that both parties’ UAV platforms are identical, we first model the aerial game as a two-player zero-sum mixed-strategy matrix game, and introduce target value factors and mutual-confrontation cross term to characterize the coupling between target value and antagonism. Second, by performing fast dimensionality reduction on the utility matrix through convex hull vertex enumeration, we effectively compress the strategy space while preserving the strategic information relevant to the original game equilibrium. Finally, we designed ECDE to find the Nash equilibrium in a dimensionality-reduction game. This algorithm uses elite covariance eigendecomposition to rotate the crossover operation into the natural coordinates of the fitness landscape, and combines parameter adaptation with population size reduction strategies to balance search efficiency and convergence stability. Experimental results show that in the CEC2022 test suite, ECDE achieved the top overall ranking among 13 compared algorithms for both D = 10 and D = 20; in the 3v2 adversarial scenario, it achieved an average decision time of only 0.026 s and a Nash equilibrium solution accuracy of the 10−15 order of magnitude, while maintaining efficient solution capabilities across matrices of varying sizes. The proposed method meets real-time decision-making requirements while ensuring solution accuracy, demonstrating engineering value.

Drones doi: 10.3390/drones10090690

Authors: Bin Qi Jianxiong Pan Linan Wang Yanxue Zhang Ruilang Li Neng Ye

Satellite downlinks reliably connect remote unmanned aerial vehicle (UAV) swarms, but broad coverage exposes information-bearing signals to unauthorized receivers. This paper proposes a multi-beam cooperative time-varying directional modulation (TVDM) framework for secure satellite downlinks. Two asymmetrically partitioned subarrays form cooperative beams whose pointing states and beam-dependent symbol mappings are randomly updated at the symbol rate, thereby introducing controlled randomness for physical-layer security. In each interval, the source symbol is mapped to two transmit symbols whose superposition remains in the correct phase-shift-keying decision region at legitimate UAVs, while the equivalent constellation varies at unauthorized locations. A relaxed transparent-transmission constraint based on constructive decision-region margins enables conventional detection without instantaneous TVDM-state estimation. Mutual information (MI) defines the pointwise multi-UAV secrecy capacity (SC), main-lobe insecure area, and sidelobe leakage. A mixed discrete–continuous problem jointly optimizes the trajectory radius, relative pointing phase, mapping parameters, and subarray partition to reduce insecure coverage and sidelobe leakage. A block alternating algorithm combines Monte Carlo MI evaluation, projected Armijo updates, and finite partition search to coordinate continuous and discrete variables. Simulations using representative low-Earth-orbit satellite parameters show that the proposed design reduces the central insecure-interval length by 82.7% relative to conventional beamforming.

Drones doi: 10.3390/drones10090689

Authors: Xiaoxue Yang Jiahao Lv Yuanshun Wang Bo Li

This article addresses real-time local conflict resolution for a self-planning UAV operating in a three-dimensional dynamic environment with surrounding UAVs. To this end, we develop an SBA–DWA hybrid planning framework in which an improved sticky bacteria algorithm (SBA) is embedded in the dynamic window approach (DWA) to enhance real-time velocity selection for the self-planning UAV. First, a chemotaxis operator with projection is developed to strictly constrain bacterial positions within the convex dynamic window. Furthermore, an anisotropic Gaussian adhesion potential field is proposed to adaptively guide the current population search using historical optimal velocity commands, achieving cross-step memory transfer. Then, a dynamic pruning mechanism is designed to ensure that historical memory does not lead UAVs into infeasible or hazardous regions. The proposed scheme guarantees that the single-step planning latency satisfies stringent real-time requirements. Comparative simulation results demonstrate that the proposed method reduces path length by approximately 30% and planning time by approximately 31% compared with the standard DWA, while achieving a larger minimum inter-vehicle clearance in dense dynamic scenarios.

Drones doi: 10.3390/drones10090688

Authors: Haoyu Mei Chengtao Xu Ruozhe Li Xueshan Luo

In disaster response and other infrastructure-limited settings, UAV-mounted access points can rapidly restore service availability for mobile ground users as demand and fleet availability evolve. Existing single-slot coverage formulations, however, can mask prolonged individual outages and do not jointly represent heterogeneous service priorities, finite battery capacities, and periodic recharging. We study persistent geometricmulti-UAV service coverage, where a user is available for service when it lies inside a UAV footprint. We propose Priority- and Outage-Guided Safe QMIX (POGS-QMIX), a hybrid hierarchical framework in which a centralized online coordinator forms conflict-reduced UAV–user targets from fleet-wide priority and outage information, while parameter-shared QMIX agents independently choose target-conditioned low-level actions. The framework couples class-balanced outage memory, assignment, dense target-progress feedback, and a return-energy action mask. The evaluation includes learning and non-learning baselines, greedy-versus-Hungarian assignment, multi-seed statistics, sensitivity studies, operating-condition studies, and energy-stress tests. In the default scenario, POGS-QMIX obtains high-priority coverage 0.547±0.009 and maximum high-priority outage 38.0±4.7 slots over five independent seeds.

Drones doi: 10.3390/drones10090687

Authors: Haolun Sun Xiangke Guo Xiangwei Bu Gang Wang

In the complex problem of air-to-ground mission planning, multi-UAV systems face significant challenges such as system complexity and heterogeneity, insufficient target observability, and difficulties in collaborating information sharing. To address these issues, this paper proposes a novel learnable sparse attention graph architecture (SAGA). This architecture deeply integrates graph reasoning and policy optimization within the MAPPO framework and includes three innovative mechanisms: (i) a GATv2-based graph neural network encoder that performs multi-round distributed consensus on the communication graph among UAVs via a multi-head attention mechanism, enabling selective aggregation of tactical information; (ii) an edge predictor that learns to prune low-value communication links, generating a sparse and mission-adaptive communication topology; and (iii) an L1 sparsity penalty term that further enhances communication efficiency. In a self-developed simulation environment for heterogeneous multi-UAV mission planning, comprehensive comparative experiments were conducted against the following baseline reinforcement learning algorithms: MADDPG, MATD3, QMIX, MAPPO, TarMAC, DGN, and G2ANet. The experimental results show that SAGA achieves reward values of 390 and 1100 in small-scale and large-scale scenarios, and outperforms the best-performing baseline algorithm by more than 20% across all operational performance metrics. Generalization experiments validate the model’s robust transfer capability under unknown defense deployment modes. Ablation experiments further confirmed the individual contributions of the three components. This study provides an innovative and effective method for mission planning of heterogeneous multi-UAV systems in partially observable adversarial environments.

Drones doi: 10.3390/drones10090686

Authors: Ziran Guo Zhi Zhu Mingxuan Li Boquan Zhang Tao Wang

This study proposes Uncertainty-Aware Staged Trust-Adaptive Residual Physics-Informed Machine Learning (U-STAR-PIML), a recursive one-step dynamics model for fixed-wing unmanned aerial vehicles, evaluated with the JSBSim C172x as a surrogate simulation benchmark. It combines a learnable compact six-degree-of-freedom prior, history-dependent temporal residual correction, trust-aware state-dependent residual gating, hard kinematics, a heteroscedastic one-step uncertainty head, and staged optimization. The protocol separates one-step accuracy, recursive rollout, predictive-interval behavior, and physically distinct distribution shifts. Across five training seeds, the full U-STAR-PIML model (E5) achieves a mean one-step root-mean-square error (RMSE) of 0.005515±0.000009 and the lowest mean rollout-position RMSE of 18.61±2.15 m; the data-driven baseline has the lowest 20 s all-state RMSE of 2.923±0.623. Recursive rankings remain seed-sensitive, without a universal winner. For the representative E5 cross-condition evaluation, the exact wind vector used in the JSBSim simulation is supplied to the model at every prediction step, i.e., perfect wind information is assumed. Under this assumption, wind out-of-distribution (OOD) conditions cause the largest degradation, with rollout-position RMSE reaching 72.76 m; wind-estimation error is not evaluated. An external zero-shot evaluation on 10 independent IDF-DS Ranger 2400 real-flight logs reduces pooled one-step all-state RMSE from 0.12374 for Persistence to 0.03379, although improvements are not uniform across dynamic state groups. The uncertainty head yields 95% empirical coverage of 96.68–100%, with conservative over-coverage under most conditions. These results support simulation-based prediction and an initial cross-airframe transfer diagnostic but do not establish same-airframe sim-to-real transfer, recursive real-flight stability, or operational validity.

Drones doi: 10.3390/drones10090685

Authors: Gabriel André Araújo Ruben Santos João J. Martins André Dias José Almeida

Reliable localization without GPS is a prerequisite for autonomous unmanned aerial vehicles (UAVs) operating inside warehouses, where a lightweight monocular camera paired with an inertial measurement unit (IMU) and onboard computer are the minimal sensing and processing an onboard platform can carry. This paper presents a tightly coupled monocular point-feature visual-inertial odometry (VIO) system for that setting, realized on a GTSAM fixed-lag factor graph with inverse-depth landmarks, on-manifold IMU preintegration, and an online loop-closure pose graph. The system is developed as the initial estimation stage of an autonomous stock-management UAV under development for indoor logistics warehouses. The decisive design element is the bootstrap: the metric, gravity-aligned initialization of a monocular estimator is well conditioned only under a translation-rich trajectory, a condition the near-zero-baseline pickup and takeoff transient that opens every indoor flight violates. Building on the visual-inertial alignment of VINS-Mono, we harden this step with a pre-bundle-adjust conditioning gate and a continuous-window initialization that refines the whole bootstrap window inside the smoother instead of freezing a single seed. On all eleven EuRoC MAV sequences, indoor flight tests recorded onboard a micro air vehicle in an industrial hall and two instrumented rooms, one fixed configuration per operating environment converges on every sequence, including three that otherwise diverge by tens to thousands of meters, and, driven by the same feature stream as locally run VINS-Mono and PL-VINS baselines, attains the better pure-odometry accuracy on nine of the eleven, with ATE RMSE of 0.12–0.37 m on the Machine Hall, a margin a paired signed-rank test confirms against VINS-Mono and leaves unconfirmed against PL-VINS at this sample size. We identify the stock fixed-lag marginalization as the principal consistency limitation and outline First-Estimates-Jacobian marginalization as the route to a more consistent estimator, establishing a characterized point-only baseline on one public benchmark as the starting point for subsequent on-platform work.

Drones doi: 10.3390/drones10090684

Authors: Yuhua Cong Yujia Li Xian Zhu Zhisheng Wang

This study addresses dynamic multi-UAV task allocation under online task release, static obstacles, deadlines, limited energy, and vehicle failures. We propose an event-triggered path-based bid assignment method (ET-PBBA). At each mission event, an A-star module estimates the static-obstacle travel cost of feasible UAV–task pairs, while a normalized score combines reward, path length, predicted energy, and secondary load–energy pressure. A one-pass sorted scan then produces an event-local one-to-one assignment. Paired-seed mechanism tests show that event-time activation raises completion rate by 0.046 in the main scenario and 0.065 under failure stress relative to 10 s periodic allocation. The path-dependent physical-cost block provides the dominant improvement over reward-only ranking, whereas the balance term is not significant in the ordinary scenarios. For 10 UAVs and 50 tasks, ET-PBBA reduces average completion time by 5.2% and 4.1% and total energy by 4.3% and 3.0% relative to auction and CBBA, respectively, although Hungarian remains the stronger centralized reference. Under failure stress, completion reaches 0.790 and deadline violations decrease to 9.73. Single-UAV replay completes all 50 nominal waypoints with 9.16 cm mean error, providing auxiliary tracking evidence rather than validation of nonlinear or online multi-UAV execution.

Drones doi: 10.3390/drones10090683

Authors: Zheng Xie Chenxin Tu Yiding Zhan Gang Liu Xiaowei Cui Mingquan Lu

Unmanned aerial vehicle (UAV) swarms increasingly operate in GNSS-denied environments, where cooperative localization provides relative positioning by fusing onboard odometry with inter-agent range measurements, for which ultra-wideband (UWB) two-way ranging is a common infrastructure-free choice. Beyond accuracy, safe swarm autonomy needs a trustworthy measure of positioning uncertainty, since collision-avoidance and formation-keeping decisions derive their safety margins from the reported covariance. Under sustained agile flight, both are hard to achieve at once: motion within each time-division multiple access (TDMA) polling round induces a ranging bias well above the UWB noise floor, and reusing shared information across the network drives the reported covariance below the true error. To address these two problems jointly rather than in isolation, we propose a modular architecture coupling an online maximum-likelihood polynomial least-squares (MPLS) ranging front-end with a fading split covariance intersection (SCI) cooperative back-end through a per-link variance interface: online MPLS compensates the motion-induced bias and reports a calibrated, time-varying variance that fading SCI consumes as measurement noise while its continuous-time fading factor bounds the reused-information covariance. Monte Carlo simulation over anchored and anchor-free 16-node swarms shows the two effects to be empirically decoupled, with front-end ranging quality governing positioning accuracy and back-end correlation handling governing estimator consistency. The method attains sub-meter positioning accuracy in both settings and, without per-scenario tuning, keeps consistency—quantified by the average normalized estimation error squared (ANEES)—within a trusted band; comparably accurate extended Kalman filter and covariance-intersection baselines fall outside it, becoming overconfident and overconservative, respectively. It thus delivers the trustworthy uncertainty that safety-critical swarm decisions require in GNSS-denied flight.

Drones doi: 10.3390/drones10090682

Authors: Mohammad Alja’afreh Ali Karime

Reliable short-term position forecasting may provide anticipatory state information for collision-risk assessment, communication management, and prediction-assisted control in Internet of Things (IoT)-enabled unmanned aerial vehicles (UAVs); these downstream functions are not evaluated directly here. This study reformulates UAV position prediction as a flight-wise, multi-horizon, three-dimensional forecasting problem and tests whether position, velocity, gravity-resolved acceleration, and quaternion-orientation histories improve predictive accuracy while measuring model-level edge-inference cost rather than end-to-end system latency. The dataset contains 3100 AirSim flights with high-rate kinematic, inertial, attitude, pressure, and magnetic-field measurements under variable horizontal wind. The reported generalization is flight-disjoint within one AirSim domain; route/scenario disjointness and transfer to physical UAVs are not established. Signals are converted to a common navigation frame, gravity-resolved, low-pass filtered, resampled to 50 Hz, and partitioned by flight identifier before normalization and window construction. Each learned model receives 2 s of history and predicts the complete next 1 s trajectory, with errors evaluated at 0.1, 0.5, and 1.0 s. The sensor-enriched LSTM (LSTM-PVAQ) is compared under matched conditions with persistence, constant-velocity, constant-acceleration, extended Kalman filter, reduced-feature LSTM, GRU, temporal convolutional network (TCN), and compact Transformer baselines. LSTM-PVAQ achieved 3D RMSE values of 0.043, 0.168, and 0.371 m at 0.1, 0.5, and 1.0 s, respectively. At 1 s, its RMSE was 21.7% lower than LSTM-PV, 13.1% lower than GRU-PVAQ, 9.3% lower than TCN-PVAQ, and 16.8% lower than Transformer-PVAQ. Its one-second ADE and FDE were 0.216 and 0.339 m. On a Raspberry Pi 5 CPU using one FP32 thread and batch size one, median neural forward-pass latency was 0.88 ms, well below the 20 ms model-update interval. The results show that gravity-resolved inertial and orientation histories improve multi-horizon prediction, while TCN-PVAQ remains an attractive lower-latency alternative.

Drones doi: 10.3390/drones10090681

Authors: Fengqin You Panlong Wu Shizhong Pei Wentao Ma

To address the coexistence of random measurement delays and intermittent data loss in multi-UAV cooperative navigation under weak communication scenarios, an adaptive cooperative localization method based on online diagnosis is proposed. A timestamp-driven diagnosis mechanism first identifies the physical reachability and temporal validity of cooperative measurements and classifies the channel state as normal, delayed, or lost. The estimator then adaptively switches between timestamp-diagnosed multi-step augmented filtering for delayed packets and Gray Wolf Optimizer (GWO)-optimized Gated Recurrent Unit (GRU) virtual measurement reconstruction for complete dropouts. In a four-UAV hybrid-anomaly simulation with continuous random delays and a 10 s communication blackout, the proposed method reduces the mean east and north RMSE by 69.1% and 75.2%, respectively, and lowers the mean horizontal-channel RMSE from 2.200 m to 0.608 m. Ablation experiments isolate the contribution of each module (diagnosis and multi-step delayed filtering and GWO-GRU virtual reconstruction), and 20 paired Monte Carlo runs with paired significance tests confirm the improvement. Relative to the strongest delay-aware baseline, the full-trajectory mean accuracy is comparable, and the main advantage of the proposed method is its robustness during complete communication outages and fast post-outage recovery. The simulation scenarios are driven by flight data collected with the DJI Matrice 350 RTK platform; the communication anomalies are included in the simulation, and all evaluations are performed offline. These results indicate that the proposed diagnosis-driven scheduling strategy can improve cooperative localization robustness when delay and data loss occur simultaneously.

Drones doi: 10.3390/drones10090680

Authors: Nam Tran Hung Duc Nguyen Peter King Minh Tran

Autonomous surface vessels (ASVs) operating in narrow and restricted waterways must follow a planned path while avoiding nearby static hazards and maintaining safe clearance from boundaries. This paper presents a LiDAR-based deep reinforcement learning framework for path following and static obstacle avoidance of an underactuated ASV. The vessel receives local pose information from a localization system and surrounding environment through a 2D LiDAR scan, which is converted into compact sector features using feasibility-inspired pooling method. A Soft Actor-Critic (SAC) policy is trained in simulation to output continuous rudder and propulsion commands, based on LiDAR features, estimated motion states, and path-relative errors. The policy is evaluated over 500 randomized simulation episodes ranging from 0–4 obstacles. The trained policy achieved an overall success rate of 95.0%, with an average cross-track error of 0.66 m. Obstacle and border collision rates are 3.80% and 1.20%, respectively; indicating that the policy can perform path tracking and collision avoidance in constrained layouts. A single field trial was then conducted in each of three fixed obstacle layouts using the model-scale Bluefin vessel. In these trials the policy executed on the physical platform and avoided static obstacles, with minimum obstacle clearances of 0.50–1.17 m. However, the field trajectories exhibit larger oscillations and longer path lengths than simulation, with an average RMS cross-track error of 1.14 m compared to 0.69 m in simulation.

Drones doi: 10.3390/drones10090679

Authors: Junyang Zhao Han Yu Zhili Zhang Yaru Li Huixin Zhu Xingxu Yan Jiayi Wang

Unmanned aerial vehicles (UAVs) have become essential platforms for remote sensing in challenging environments such as high-dynamic-range (HDR) scenes and low-texture areas. However, conventional frame-based visual–inertial simultaneous localization and mapping (SLAM) systems often suffer from motion blur and overexposure during high-speed UAV flight, leading to state estimation failure. To address numerical instability in marginalization, weak scene adaptability, and insufficient outlier suppression in event-based stereo visual–inertial SLAM systems for aerial applications, we propose HESVI, a hybrid marginalization and adaptive heterogeneous kernel state estimation method for UAV remote sensing. Our method first establishes a focal-length-driven cross-modal inverse depth consistency constraint to couple image and event inverse depths, providing high-quality priors for optimization. Such lightweight prior generation is designed with the limited onboard computing resources of UAV platforms in mind. A hybrid marginalization strategy is then introduced, employing block-parallel tall–skinny QR (TSQR) acceleration based on Householder reflections alongside dynamic Tikhonov regularization and first-estimates Jacobian (FEJ) linearization to balance computational efficiency and numerical stability. Furthermore, an adaptive heterogeneous Cauchy kernel maps differentiated thresholds to image and event features according to their average effective tracking lengths, enabling dynamic outlier suppression. Experiments on the VECtor, MVSEC, and HKU datasets demonstrate that HESVI achieves the best absolute trajectory error (ATE) on the vast majority of the evaluated sequences, with average ATE reductions of 47.2%, 41.2%, and 26.8% over PL-EVIO, ESIO, and ESVIO, where each average is computed only over the sequences on which the corresponding baseline runs successfully. The method also exhibits excellent performance in complex remote-sensing scenarios and generalization tests. HESVI effectively enhances the numerical stability, scene adaptability, and localization accuracy of event-based stereo visual–inertial SLAM systems in challenging UAV remote-sensing environments.

Drones doi: 10.3390/drones10090678

Authors: Görkem Gök Anıl Sezgin Merve Açıkgenç Ulaş Hakan Güler Nuray Beyza Avcı Betül Bektaş Ekici Nihal Arda Akyıldız Mustafa Ulaş Aytuğ Boyacı

While there are evident advantages to deploying UAVs for structural inspection applications within reinforced-concrete structures where human access may be hazardous or restricted, UAV deployment is still inhibited within structures due to a lack of GPS, payload and power limitations, signal attenuation, limited transmission opportunities, and low bandwidth for communication. Within the confines of the present project, a low-cost two-layer six-rotor architecture was developed; a single Pixhawk PX4 manages stabilized flight, while a Raspberry Pi 4 manages mission-level operations. The complete prototype integrated a relative-pose/nearby-object estimator, hybrid 433 MHz and Wi-Fi/MQTT communications, avionics-side energy management, mission continuity via SQLite, and human-in-the-loop fail-safe support. Prototype experiments revealed reduced horizontal drift compared to both tested comparison configurations, continued operation under constrained communication conditions, and a 62.0% reduction in avionics-side power consumption, excluding propulsion. This was followed by a complementary PX4–Gazebo evaluation probing horizontal and vertical proximity responses, six-sector LiDAR processing, and stale-data watchdog functionality. Across 45 repeated simulation runs and 3000 retained sector-level observations, no simulated collisions occurred during the horizontal-approach, vertical-proximity, or watchdog tests. No false sector assignments were observed, and the overall mean absolute error was 0.0285 m. From this limited demonstration, the architecture appears satisfactory at the prototype and simulated-subsystem levels. Further physical testing will be needed prior to operational deployment.

Drones doi: 10.3390/drones10090677

Authors: Shiyang Li Jisong Lv Yuchen Lu Yuxuan Zhang

Variable-rate pesticide spraying and fertilizer application require coordinated operation of heterogeneous agricultural machines, particularly in irregular fields where task demands, vehicle mobility, payload capacity, energy consumption, and resupply requirements vary spatially. However, most existing studies optimize aerial spraying or ground fertilization separately and do not jointly consider prescription-map demands, air–ground synchronization, pesticide-drift risk, and agricultural vehicle constraints. This study formulates collaborative UAV spraying and UGV fertilization as a multi-objective mixed-integer nonlinear programming problem with three objectives: minimizing system makespan, weighted energy consumption, and pesticide-drift penalty. A prescription-map-guided bi-level planning framework is proposed. At the upper level, the problem-specific TNSAOO solver determines UAV and UGV task sequences and collaborative resupply-point activation. At the lower level, adaptive Theta* and row-constrained Hybrid A* generate UAV spraying and UGV fertilization trajectories, respectively, while prescription-dependent application commands are assigned along active operation segments and a time-window mechanism detects and corrects residual air–ground conflicts. The framework was evaluated using 30 real farmland boundaries and 90 randomized prescription scenarios. Mean geometric coverage rates reached 98.82% for UAV spraying and 98.95% for UGV fertilization, while the mean prescription-compliance errors were 6.21% and 2.13%, respectively. In addition, 96.7% of the batch runs contained no more than one detected air–ground conflict, with a mean corrective waiting time of 1.07 s. Compared with traditional independent operation, collaborative planning reduced mean system makespan by 9.32%, weighted energy consumption by 7.21%, modeled drift penalty by 3.62%, and total path length by 5.79%. In the multi-objective comparison, TNSAOO obtained a mean hypervolume of 0.597 and a mean inverted generational distance of 0.375, showing competitive Pareto-search performance relative to established comparison algorithms, particularly NSGA-II. Additional terrain and drift sensitivity analyses produced systematic changes in energy, completion time, and modeled drift risk under controlled parameter perturbations. These findings demonstrate the simulation-based feasibility of jointly planning heterogeneous variable-rate spraying and fertilization under a shared prescription map. Physical field experiments remain necessary to validate spray deposition, fertilizer-distribution uniformity, terrain effects, and model calibration under environmental uncertainty.

Drones doi: 10.3390/drones10090676

Authors: Wei Li Xin Chen Xuebing Li

The cooperative tracking of multiple ground targets by multiple UAVs remains challenging under partial observability, limited communication, and obstacle constraints, owing to complex target association, difficult task handover, strong coupling among low-level continuous control decisions, and unstable critic value estimation. To address these issues, this paper proposes a hierarchical-guidance and Gaussian-process-corrected multi-agent proximal policy optimization method, termed HGP-MAPPO. Built upon the centralized-training and decentralized-execution paradigm, HGP-MAPPO introduces low-frequency task-guidance signals derived from target-association information, task handover and recovery cues, task priorities, and desired observation geometry. These guidance signals are incorporated as conditional inputs into the low-level actor–critic framework, thereby reducing the policy learning difficulty in jointly handling target tracking, occlusion recovery, obstacle avoidance, and smooth control. Moreover, to alleviate local estimation bias in the neural-network critic under complex partially observable conditions, a Gaussian-process-based residual correction mechanism is designed. Specifically, the posterior mean is used to compensate for value residuals, while the posterior uncertainty adaptively regulates the correction intensity, improving the stability of value evaluation and policy optimization. A sparse inducing-point approximation is adopted to control the training-stage computational cost, while the Gaussian-process module is removed during decentralized execution and, therefore, introduces no additional online inference overhead. Experiments are conducted in standard-obstacle and densely obstructed multi-UAV multi-target tracking scenarios, with DDPG-MHSA, MAPPO, MADDPG, and MATD3 adopted as baselines. The experimental results demonstrate that HGP-MAPPO achieves faster training convergence, higher average episode rewards, and improved target retention rates. It also effectively reduces UAV–target distance fluctuations and the mean absolute temporal-difference (TD) error. Ablation studies further confirm the contributions of task-guidance signals, Gaussian-process residual correction, and uncertainty-aware weighting to cooperative tracking performance and training stability.

Drones doi: 10.3390/drones10090675

Authors: Rajeev Sinha John Atkinson Minija Praveen Brandon Downer Krista Scharnak MaryRose Foley

Unmanned aerial vehicles (UAVs), also referred to as unmanned aerial pesticide application systems (UAPASs), are increasingly used for crop protection applications because of their operational efficiency and precision. However, UAV spraying is typically conducted at very-low volumes (VLVs) (10–20 L ha−1), resulting in highly concentrated spray solutions that may alter formulation behavior relative to conventional ground applications. In this study, a total of nineteen commercially available herbicide, insecticide, and fungicide formulations representing multiple formulation classes were evaluated under UAV-relevant (10 L ha−1) and conventional ground application conditions. Tank-mix compatibility, sprayability, droplet size distribution, driftable fines, dynamic surface tension (DST), and droplet spreading were assessed. Tank-mix incompatibility was most frequently observed in mixtures containing emulsifiable concentrate (EC) formulations, with five of 11 commonly used tank mixes exhibiting severe incompatibility at UAV rates despite compatibility at conventional application volumes. Formulations containing suspended actives, including suspension emulsions (SEs), suspension concentrates (SCs), oil dispersions (ODs), and water-dispersible granules (WDGs), showed the greatest risk of filter and screen clogging, whereas EC and soluble liquid (SL) formulations exhibited acceptable sprayability. UAV-rate spray solutions generally produced comparatively finer droplet spectra than ground-rate solutions, increasing driftable fines by up to 36.6% depending on formulation type. DST decreased by 2.5–35.2% under UAV conditions, with the largest reductions observed for SC and EC formulations. Reduced DST was associated with increased droplet spreading, particularly for fungicide formulations, where droplet spreading increased up to 718.8% relative to ground-rate preparations. These results demonstrate that formulation behavior can differ substantially under VLV conditions and that formulation-specific evaluation of compatibility, sprayability, atomization characteristics, and surface-tension-dependent behavior is required when products are deployed through UAV spray systems.

Drones doi: 10.3390/drones10090673

Authors: Seuk Seo Hyungeun Park Geonwoo Park Seung Jae Lee

Fully actuated multirotor UAVs (FA-mUAVs) have attracted attention for aerial physical interaction because they generate translational forces without tilting the vehicle, thereby supporting interaction precision and payload-transportation stability. However, this capability relies on an accurate center-of-mass (CoM)-dependent thrust-to-wrench allocation geometry. When the CoM shifts due to payload variation or physical interaction, the nominal allocation geometry produces incorrect moment arms and recurrent unintended torques. Existing robust and robust-adaptive approaches compensate for these effects through the input channel while leaving the allocation geometry unchanged. Conversely, allocation-level CoM correction addresses the geometric source of the mismatch but does not by itself provide transient robustness during estimator convergence or against non-CoM disturbances. This paper proposes a transient-buffered allocation-geometry adaptation framework that addresses these two limitations within a single architecture. The framework combines an input-channel robust layer with allocation-level CoM adaptation: the robust layer buffers instantaneous mismatch and residual disturbances, while the adaptation layer processes the filtered compensation signal generated by the robust layer through the CoM-dependent regression to estimate the CoM and update the allocator, without assuming a unique separation of CoM-induced and non-CoM components. Stability analysis establishes boundedness and phase-dependent convergence of the CoM-estimation error. Real-flight experiments show that adding the CoM-estimation/allocation-update path to a retained disturbance-observer (DOB) robust baseline improves attitude regulation and reduces rotational compensation demand.

Drones doi: 10.3390/drones10090674

Authors: Victoria M. Scholl Jennifer M. Cramer Alexandra D. Evans Evan M. Cox Raymond F. Kokaly

The U.S. Geological Survey collects remote sensing data to support national scientific assessments of natural resources, hazards, and landscape change. Spectrometers and spectroradiometers are essential for gathering point-based spectral measurements used in applications such as uncrewed aerial systems (UAS) multispectral image calibration, validation, and analysis. Evaluating how different instruments perform in laboratory and field environments helps determine whether they provide consistent, interoperable measurements. Such verification can expand access to spectral ground data during UAS operations by allowing scientists to use alternative instruments when budgets, logistics, or field conditions limit options. We propose and test a methodological framework for evaluating spectrometers for measurement consistency during UAS multispectral vegetation mapping applications. There are three central evaluation components to the framework: laboratory, field, and relative to UAS multispectral imagery. By evaluating the instruments in both relatively controlled and uncontrolled environments, we thoroughly examine measurement consistency and when/why measurements may differ. We opportunistically selected two instruments for a case study in a coastal marsh setting: a compact laboratory spectrometer we modified for field use and a field-ready spectroradiometer. The instruments produced consistent measurements in both environments. We found differences between the field spectra and UAS spectra that likely reflect the perspectives of ground vs. aerial data and indicate that further radiometric calibration may be needed.

Drones doi: 10.3390/drones10090672

Authors: Youpei La Xiezhao Pan Hao Huan

RF-based UAV recognition is important for low-altitude security because it enables passive sensing from electromagnetic emissions without requiring target visibility or illumination. Compared with active sensing, passive RF sensing offers a wider observation range and is less constrained by line-of-sight conditions. Although deep-learning methods have improved RF-based UAV recognition, most existing approaches focus primarily on label prediction and do not explicitly model time–frequency structural characteristics, which limits their robustness in practical RF environments where UAV signals are often weak and may overlap with co-channel interference. This paper presents a multi-representation signal fusion (MRSF) framework for robust RF-based UAV recognition through structure-guided time–frequency modeling. MRSF represents each RF signal using an original time–frequency map, a residual-enhanced map, and a binarized structural map to capture global spectral patterns, enhanced signal structures, and connected spectral morphology. These representations are fused through a heterogeneous three-branch stem for backbone classification, and the binarized structural map further supports connected-region analysis to estimate occupied bandwidth, temporal duration, and signal period in non-cooperative scenarios where protocol-level parameters are unavailable. On the RFUAV dataset, MRSF achieves an average accuracy of 96.05% across target SNR levels from −20 to 20 dB and maintains 88.29% accuracy at −20 dB, demonstrating strong robustness under severe low-SNR conditions. These results show that, for UAV RF emissions acquired within the effective coverage of the front-end receiver, MRSF improves robust UAV recognition while providing interpretable spectrum-structure descriptors beyond class labels.

Drones doi: 10.3390/drones10090671

Authors: Bong-Do Pyeon Jae-Sung Bae

This study presents the design and flight demonstration of an aerobatic RC aircraft equipped with a morphing-wing using a compliant mechanism-based flexible structure. Unlike conventional hinged control surfaces, the proposed morphing wing provides an aerodynamic surface through elastic deformation, which can reduce aerodynamic discontinuities compared to conventional hinged wings. The flexible morphing rib was designed to ensure structural flexibility by combining fishbone and corrugated structures, and four flexible ribs constitute one module, making up four modules of the entire wing. Flight tests were conducted to evaluate the roll maneuverability of the UAV using roll rate and a nondimensional roll performance index. Experimental results showed that the morphing-wing UAV achieved stable and sufficient roll control performance under limited control input conditions, while demonstrating maneuverability comparable to or better than that of a conventional hinged control surface when the aileron deflection angle was limited. These results confirm the feasibility of compliant morphing wings as an alternative to traditional mechanical control surfaces and demonstrate their potential application to future high-efficiency UAV systems.

Drones doi: 10.3390/drones10090670

Authors: Fuqi Yang Jikun Ye Hao You Lei Shao Lei Zhang

To address time-varying communication topology, actuator response delay, and limited inter-UAV communication resources in the multi-UAV approach of a maneuvering target, this paper proposes a cooperative rendezvous/tracking control law combining a prescribed-time extended state observer (PTESO) with a dynamic event-triggered mechanism (DET). A three-state PTESO is designed whose observation error converges, within a prescribed time independent of the initial error, into a compact set related to the disturbance upper bound. Along the line-of-sight (LOS) direction, the remaining flight times of the UAVs are driven to consensus within a prescribed time toward a specified arrival instant via a threshold-adaptive DET; along the LOS normal direction, prescribed-time convergence of the elevation and azimuth angle errors is achieved through a time-varying-gain sliding surface. The guidance gains are designed from the worst-case algebraic connectivity of the candidate topology set, ensuring uniform validity under arbitrary switching. After accounting for first-order autopilot inertial dynamics, the command tracking error is proven uniformly ultimately bounded. Simulations of four UAVs cooperatively approaching a maneuvering non-cooperative object under periodic topology switching and actuator delay show an arrival-time deviation below 0.01 s, a terminal position error under 0.08 m, and 75–91 average inter-UAV triggers, outperforming existing prescribed-time/fixed-time cooperative guidance methods.

Drones doi: 10.3390/drones10090669

Authors: Ting Xiong Jianning Zhan Qi Deng Xiaohui Wang Chao Fan Xueshi Liu Tao Zhang

Deployable autonomy remains a key challenge for unmanned aerial vehicles (UAVs) operating in open-ended missions. Large language models (LLMs) and their multimodal variants, which can process visual and other sensory inputs, have introduced new capabilities for semantic perception, task reasoning, and language-conditioned control. However, these capabilities do not by themselves produce flight-ready autonomy. We structure our analysis around a Perception–Cognition–Action (P–C–A) framework. At each layer, we identify the capabilities contributed by LLM-based components and examine how they connect to existing flight modules through input specifications, output representations, architectural coupling patterns, and safety mechanisms. Across the surveyed systems, LLMs extend UAV autonomy beyond fixed perception categories, scripted task plans, and pre-programmed controllers. However, field deployment depends on whether model outputs can be transformed into representations that downstream modules can parse, verify, and safely execute. Without adequate validation, captions, task plans, code, waypoints, and control commands may become failure points that propagate across the P–C–A loop. Our analysis highlights structured output contracts, independent safety barriers, and deterministic fallback mechanisms as key design elements for the reliable integration of LLM capabilities into UAV platforms.

Drones doi: 10.3390/drones10090668

Authors: Runze Mao Teng Wu Shengjun Wei Changzhen Hu

Existing RF-based UAV detection methods achieve high accuracy in identifying UAV presence and model type, yet they largely characterize UAVs through static signal attributes, offering limited insight into what an identified UAV is actually doing. This gap constrains their practical value for airspace monitoring and threat assessment. This paper presents a multi-feature RF-based framework for UAV behavioral state recognition and short-horizon behavior prediction, built upon a set of newly defined behavioral indicators, namely, spectral dynamics, signal-power-based motion trend, and communication density, integrated through a dedicated time-series modeling module. To support this study, we construct UAV-BehaviorRF, a new dataset with fine-grained behavioral annotations collected via a scripted multi-state flight protocol across eight UAV models. Experiments on UAV-BehaviorRF and the public DroneRFa dataset show that the proposed framework achieves accurate behavioral state recognition and reliable state-transition prediction, while remaining robust under interference and real-world conditions and maintaining real-time processing suitable for resource-constrained deployment.

Drones doi: 10.3390/drones10090667

Authors: José Varela-Aldás Renato M. Toasa Viviana Moya William Chamorro Oscar Gonzales-Zurita

Model predictive control (MPC) has become a central receding-horizon strategy for robotic systems subject to constraints, uncertainty and multiple objectives. In unmanned aerial vehicles (UAVs), its integration with visual servoing makes it possible to formulate tracking problems directly in the image space, keep targets inside the field of view, anticipate vehicle dynamics and coordinate safety, actuation and perception constraints. This scoping review characterizes the available evidence on MPC applied to visual-servo tracking in UAVs. The process identified 20 core studies, 11 strong complementary studies, 12 borderline studies and 18 non-ideal records for the defined scope. The core literature reveals four dominant lines: tracking of targets and visual features, landing or docking on moving platforms, perception-aware control with visibility constraints, and robust, distributed or learning-augmented MPC extensions. The findings show a recent consolidation of methodological directions, although experimental maturity remains uneven across the field, with a predominance of nonlinear MPC, predictive image-based visual servoing, field-of-view constraints and validation in simulation or UAV platforms. Persistent gaps remain in reproducibility, standardized comparison, visual latency, depth uncertainty, outdoor validation and formalization of perceptual costs.

Drones doi: 10.3390/drones10090666

Authors: Tao Sun Chen Chen Chun Chang Xinyu Xue Wei Gu

Unmanned aerial vehicle (UAV) remote sensing can reveal spatial variability in maize, but its value depends on whether observations support timely and reliable management. This structured critical review synthesizes UAV applications from stand establishment and canopy development to water and nutrient assessment, stress monitoring, yield prediction, and decision support. The evidence corpus comprised 82 sources, including 51 core maize–UAV studies, evaluated by growth stage, validation strength, and operational endpoint. RGB, multispectral, hyperspectral, thermal, and three-dimensional methods provide complementary information for plant counting, canopy traits, treatment-related water and nitrogen responses, visible stress mapping, and within-experiment yield variation. Most evidence, however, comes from experimental or site-specific settings. Independent testing across sites, years, cultivars, and production environments remains uncommon, as do physiological confirmation of interacting stresses and translation of diagnostic maps into machinery-ready operations. UAV sensing should therefore be viewed as complementary to satellite observations and field scouting, with its advantage determined by target, scale, timing, and decision requirements. Future research should prioritize phenology-aware acquisition, independent field validation, uncertainty relative to management thresholds, interoperable prescription and machinery data, and closed-loop evaluation of input use, crop response, yield, and economic return. These steps are needed to move from high-resolution mapping toward reproducible precision management in maize.

Drones doi: 10.3390/drones10090665

Authors: Peng Chen Tianxu Li Wei Xia Kun Zhu

Multi-UAV Cooperative Target Search (MCTS) is a critical task in low-altitude sensing applications, requiring agents to efficiently explore unknown environments under complex constraints. However, traditional search methods are mostly unscalable and perform poorly in dynamic multi-UAV environments. As a promising alternative, Reinforcement Learning (RL) has emerged to overcome these limitations by enabling agents to learn adaptive policies directly from environmental interactions. A key limitation is that current RL methods lack efficient exploration, which is a critical bottleneck preventing UAVs from finding more targets. To address this limitation, we propose a novel method named AEQMIX, which integrates trajectory entropy maximization into QMIX, an advanced Multi-Agent Reinforcement Learning (MARL) method, to encourage efficient exploration. We formulate the MCTS problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and design a multi-objective reward function. To mitigate the intractability of density estimation in high-dimensional spaces, we employ a nonparametric particle-based entropy estimator to quantify the spatial diversity of UAV trajectories. This entropy estimate is utilized as an intrinsic reward, incentivizing agents to maximize the distance between their trajectories and those of their neighbors. Extensive simulations demonstrate that AEQMIX significantly outperforms baseline reinforcement learning and traditional optimization methods in terms of search rate, coverage efficiency, and collision avoidance. Compared with DNQMIX, AEQMIX improves the search rate and coverage rate by 9.52% and 11.54%, respectively, while reducing the average collision count by 70.59% in the (40 × 40) environment.

Drones doi: 10.3390/drones10090664

Authors: Cheng Qian Bin Fu Zhenhao Wang Zhuoheng Ding

Joint UAV–target assignment and route construction must account for service windows, payload limits, flight range, and the distance required to return to the depot. We study this problem in a static simulator and evaluate constraint-aware hierarchical assignment and routing (C-HAR), a deterministic constructive heuristic. At each decision step, C-HAR removes UAV–target pairs that fail the current feasibility checks and ranks the remaining pairs using target value, incremental travel, time-window slack, and predicted route imbalance. The evaluation uses 40 shared synthetic instances, independent route replay, Wilson intervals for audited event rates, and paired randomization tests. C-HAR obtained a feasible completed value of 0.941 ± 0.014, with no audited time-window or resource violations among 542 serviced targets. Its feasible-value difference from the equally screened Feasible-Greedy comparator was not statistically significant. The simulator uses fully observed, noise-free states; here, “deterministic” describes the decision rule and state update for a fixed generated instance, not the uncertainty of a physical UAV system. No trained neural policy or flight experiment is evaluated.

Drones doi: 10.3390/drones10090663

Authors: Yuan Wang Mingqian Yang Zelong Yu Hanming Xu Rentong Xue Yixiang Cai Yu Zhang

This paper develops a distributed control framework for leader–follower formation tracking in multi-agent unmanned aerial vehicle (UAV) systems. Feedforward compensation converts the networked tracking task into local error stabilization problems, and an Lp zero-sum game is used to construct a finite-time robust feedback law. The disturbance-free closed loop is proven to converge in finite time, whereas under nonzero disturbances, the result is a certified Lp attenuation bound rather than exact finite-time convergence. A single-critic adaptive dynamic programming architecture approximates the value function, and an offline sampled data training procedure avoids injecting probing noise into the physical plant. In the reported planar outer-loop simulation, the local errors settle within 4.3 s, compared with 8.2 s for the quadratic L2 baseline, and the reported cumulative disturbance attenuation indicator decreases from 2.74 to 0.48. The current validation uses a fully actuated translational outer-loop abstraction; extensions to underactuated six-degree-of-freedom dynamics, saturation, and hardware experiments are left for future work Lp.

Drones doi: 10.3390/drones10090662

Authors: Tao Yue Hong Huang Bo Song Yun Chen Zhili Chen

Tiny fire spot detection in UAV images under complex forest backgrounds remains challenging due to tiny target size, sparse distribution, weak feature responses, and background interference. This paper proposes a Multi-Scale Feature Enhancement Network (MSF-YOLO) for tiny fire spot detection. Specifically, a lightweight C2f-ARG module is designed by integrating Ghost feature generation and channel recalibration mechanisms to enhance weak fire spot representation while reducing redundant features. A C2f-LGPA module is designed to model local fine-grained information and global contextual dependencies, improving target discrimination under complex forest environments. Additionally, a P2 tiny-object detection branch is incorporated to preserve spatial details and enhance the perception capability of tiny targets. A UAV forest fire spot detection dataset was constructed, and extensive experiments were conducted. Experimental results demonstrate that MSF-YOLO achieves a Recall of 79.27, representing an improvement of 5.09% over the baseline YOLOv8s. The mAP@0.5 and mAP@0.5:0.95 values are improved by 3.99% and 5.35%, respectively. Moreover, compared with eight improved YOLO-based small-object detectors, MSF-YOLO achieves superior overall detection performance, with a 1.35% improvement in mAP@0.5 over the best-performing comparison method. The proposed MSF-YOLO effectively addresses the challenge of early-stage tiny fire spot detection in forest fire.

Drones doi: 10.3390/drones10090661

Authors: Yuanqiang Sun Xueqing Zhang Menglin Wang Mengqi Qiu You Li Tonghe Cui Xuan Zhu

The proliferation of UAVs operating in complex interference environments has intensified the demand for collaborative spectrum management to mitigate interference and maximize network capacity. This paper proposes a heterogeneous graph neural network (HGNN) framework for collaborative spectrum management in hierarchical UAV communication networks. The proposed architecture consists of three layers: a Terminal Transmission and Control Layer for local spectrum monitoring, a Sub-Domain Transmission and Control Layer for regional interference localization, and a Global Control Layer for network-wide spectrum optimization. Each layer incorporates multiple sensing UAVs that communicate exclusively with their own layer’s Transmission and Control UAV (T&C UAV), which aggregates and processes data from its subordinate sensing nodes and forwards the result upward through the T&C UAV chains. A hierarchical heterogeneous graph neural network with intra-layer and inter-layer message passing mechanisms was designed to capture the complex spatial–temporal dependencies in the spectrum environment under non-uniform interference conditions. The simulation results demonstrate that the proposed HGNN framework achieves steady-state utility gains of approximately 7.1% and 1.4% over the SL-GNN in 48-node and 81-node scenarios, respectively, along with corresponding Interference Suppression Ratio (ISR) improvements of 1.5 dB and 2.9 dB against the SL-GNN.

Drones doi: 10.3390/drones10090660

Authors: Ahmad B. Alkhodre Mouhamad Alim Al-Amine Yazed Alsaawy

This paper presents a systematic reward engineering methodology for training a Proximal Policy Optimization (PPO) quadrotor navigation policy in the Webots simulator, using a hierarchical architecture in which a PID controller handles low-level stabilization and a PPO policy issues velocity commands. We document the complete evolution of a composite ten-term reward function across seven versions (v5 through v11) and retrain the key versions with multiple independent training seeds. The multi-seed study revises the single-seed history: penalty-dominated configurations (v8, v10) fail across all seeds, while the strongest historical version proves seed-sensitive (v11: 32.2 +/− 15.8%). An ablation removing the continuous distance-shaping term from v11 yields 0% success across seven seeds, identifying that term as necessary for convergence. We further isolate a previously hidden co-factor: with the library-default exploration-noise initialization (sigma_0 = 1.0), sampled actions saturate the bounded action space, the exploration variance receives no learning gradient, and curriculum progression deadlocks regardless of reward design; initializing sigma_0 = 0.37 restores gradient flow. With this correction and a deterministic evaluation-gated curriculum, the final configuration is evaluated across the full curriculum rather than at a single operating point: across five independent training seeds under a deterministic protocol, it attains 95.0% ± 6.2% navigation success at Stage 0 conditions (2 m targets, no obstacles), 89.6% ± 6.9% at Stage 1 conditions (4 m, one obstacle), and 48.4% ± 10.8% at Stage 2 conditions (7 m, three obstacles). Reporting this difficulty curve, rather than a single headline value, exposes a substantial generalization gap whose dominant failure mode is obstacle collision (45–52% of episodes at Stage 2). Matched retraining of Soft Actor-Critic and TD3 baselines under identical reward and curriculum conditions yields one completed seed each both baselines show non-monotonic difficulty curves, and at Stage 2 conditions, TD3 (64.0%) exceeds PPO (48.4% ± 10.8%) while SAC (43.0%) falls just below it, whereas at Stage 0, PPO (95.0%) leads both, so the ranking is operating point-dependent on the current single-seed evidence. We conclude that a continuous shaping gradient and the exploration-noise initialization, interacting with the curriculum advancement criterion, determine convergence in continuous control deep reinforcement learning, and that reward polarity by itself does not.

Drones doi: 10.3390/drones10090658

Authors: Fengjie Xie Guojin Zhang Yuhua Jia

Truck–multi-drone collaborative delivery can reduce last-mile costs, but fleet sizing and routing are often optimized separately, making it difficult to match resources with demand under a delivery-period constraint. This study addresses the scenario of collaborative delivery involving multiple trucks and multiple drones by constructing a two-stage optimization framework that integrates fleet sizing and route planning. In the first stage, queueing models and continuous approximation are employed to determine the initial configuration of trucks and drones based on demand intensity and delivery cycle constraints. The second stage introduces continuous drone delivery and cross-vehicle retrieval to enhance the flexibility of truck–drone collaboration; while optimizing collaborative routes, the framework adjusts the allocation of trucks and drones—adding or reducing resources based on route feasibility and equipment utilization—thereby achieving the joint optimization of transport capacity and collaborative routes with the objective of minimizing total system costs. A node–resource–flow-separated three-chain encoding and an adaptive large neighborhood search–simulated annealing algorithm are designed to solve the model. Multi-scale numerical experiments show that, compared with four simplified fleet-sizing strategies, the proposed framework achieves average cost savings of 15.4–15.5% for medium- and large-scale instances. The results reveal an economic saturation point of the delivery period that shifts with node scale and a non-monotonic relationship between fleet size and coordination efficiency. The framework supports demand-driven fleet configuration and provides operational guidance for cost-effective truck–drone last-mile delivery.

Drones doi: 10.3390/drones10090659

Authors: Qin Rao Yuqi Gao Jihong Zhu Xiaming Yuan

Accurate radar tracking of highly maneuverable unmanned aerial vehicles (UAVs) is a key enabling technology for low-altitude airspace surveillance, counter-UAS defense, and UAS traffic management (UTM). Once a non-cooperative UAV has been detected, estimating its motion state must cope with nonlinear polar-coordinate observations, unknown maneuver-mode switching, and multi-scale state variations driven by agile drone flight, making it difficult for classical IMM/UKF filters and deep sequence models to preserve local maneuver response and long-term temporal consistency. We propose MSGate, a multi-scale gated temporal network organized along an “observation-representation-fusion-constraint” pipeline. A non-learnable physical front end maps polar measurements into a Cartesian observation trajectory of the same dimension as the UAV state. Multi-scale gated convolution and RoPE-Transformer encoding extract local maneuver responses and long-range dependencies. A shared gated dual-path decoder fuses the two paths adaptively at each time step and channel, and velocity-smoothness and position-velocity kinematic consistency terms regularize the predicted trajectory. On the real-UAV datasets UZH-FPV, EuRoC MAV, and NeuroBEM, under a unified range-azimuth observation protocol, MSGate attains the lowest average position and velocity errors (Pos-RMSE 0.0486m; Vel-RMSE 0.1767m/s), outperforming the strongest time-series baseline TimeMixer, and generalizes to a separate nano-quadrotor dataset (NanoBench). MSGate provides an accurate, maneuver-robust solution for radar state estimation of highly maneuverable UAVs.

Drones doi: 10.3390/drones10090657

Authors: Mustafa Fenerci Seda Arık Hatipoğlu Mehmet Konar

This paper presents a companion-computer chain that converts Pixhawk-class MAVLink telemetry into operator advice on an NVIDIA Jetson Nano. A twelve-channel sliding window (L=50, 10 s at 5 Hz) feeds a reference long short-term memory autoencoder and an edge-evaluated dense TensorFlow Lite model, which were trained with a session-level split of eight field sorties (train 14,429/validation 7281/test 6976 windows). Reconstruction mean squared error is mapped by validation-calibrated thresholds and a safe-inaction-first decision engine with K=3 action persistence. On a per-sortie fault-injection benchmark, One-Class SVM attains the highest pooled F1 score (0.713); the edge-evaluated dense TensorFlow Lite model reaches F1 0.524 (detector false-alarm rate 5.8%) at a 1.57 ms median on-device cost. Decision replay reports a 0.68% escalation false-alarm rate with the on-board gyro-x plausibility gate enabled, and 2.72% with the gate off. The study is a replay-profiled prototype on a single hexacopter; labelled evaluation uses synthetic injection, not concurrent live-load flight scoring.

Drones doi: 10.3390/drones10090656

Authors: Marc Melgosa Jovana Kuljanin Jairo Lopez David de la Torre Albert Sánchez-Segura Cristina Barrado

Cities are increasingly addressing mobility challenges by restricting road traffic, particularly traditional road vehicles that generate greenhouse gas emissions. As an alternative, unmanned aircraft systems (UASs) are emerging as a promising solution for future mobility, offering fast, quiet, cost-effective and environmentally friendly operations. For last-mile delivery, small drones operating at low altitudes are considered especially promising and are already being deployed in some urban areas. According to the EU Drone Strategy 2.0, drone services could generate a market of 14.5 billion and create 145,000 jobs in Europe by 2030. As this sector is still in its early stages, there is a significant uncertainty about the optimal organisation of urban air traffic. In this paper we present a realistic prognosis of how unmanned traffic over a city will utilise the urban very-low-level airspace and assess it using two concepts of operations, with non-structured or structured airspace, both aiming to facilitate the coexistence of competitors sharing the same urban low level airspace. The two concepts are evaluated with delivery operations at scale for a large and densely populated European city. Results for a normalised scenario of 3500 daily operations show that structured airspace produces more conflicts than non-structured airspace (e.g., 316 vs. 54, respectively), and that only for the non-structured airspace can all conflicts be solved with a simple strategic altitude reassignment (vs. 10% of unresolved conflicts for structured airspace). The artificial organisation of slim urban airspace may limit the scalability of business delivery while not reducing the conflict rate.

Drones doi: 10.3390/drones10090655

Authors: Xiao Wang Yuanyuan Jiao Yuxia Zhang Jiawu Peng Xiaogang Pan

The mission planning problem of using ground-fixed communication base stations to guide Unmanned Aerial Vehicle (UAV) swarms back under communication denial is addressed. The core challenge is to optimally match limited resources with massive UAV demands under constraints such as time windows, base station exclusivity, and relay continuity, which we formulate as an NP-hard combinatorial optimization problem. We first build a static model maximizing comprehensive benefits, incorporating base station heterogeneity and a super-linear congestion penalty for load balancing. We then extend it to a rolling horizon dynamic framework. Through task state partitioning and frozen resource inheritance, this extension decomposes the long-term optimization into sequential finite-horizon subproblems, enabling online decisions as UAV information is gradually revealed. To solve these models, we propose CMSA-MSWOA, which integrates elite opposition-based learning and Lévy flights to navigate the fragmented feasible solution space. Simulation results show 100% guidance coverage across scales from 100 to 500 UAVs in static scenarios, with the benefit advantage over the best benchmark growing from 10.0% to 65.5% as scale increases. In dynamic scenarios, the rolling framework satisfies all constraints and achieves full coverage. While our framework performs robustly in simulations, the current evaluation assumes idealized communication conditions; validation under more complex interference and external testing remains future work. Overall, our model and algorithm offer a useful simulation-based closed-loop framework for resource scheduling in denial environments, providing a foundation for further validation under more realistic field conditions.

Drones doi: 10.3390/drones10090654

Authors: Lorenzo Brocchini Chenxi Wang Antonio Pratelli Daniele Conte Alessandro Farina

Unmanned aerial vehicles (UAVs) are increasingly considered as enabling technologies for last-mile delivery, emergency medical response, and smart-city applications. However, drone-based logistics, emergency communication, sensing activities, and future aerial mobility are often addressed as separate research domains. This article proposes a four-dimensional planning framework for drone-enabled mobility, integrating goods, information, sensing, and human mobility within a unified conceptual structure. The framework is developed through a literature-informed conceptual analysis and previous applied research experiences related to drone-assisted logistics and emergency communication. Goods mobility includes parcel delivery, medical logistics, emergency supply transport, and hybrid operational models involving trucks, public transport, depots, and micro-hubs. Information mobility refers to the use of drones as mobile communication tools for emergency warnings, citizen interaction, drone-to-infrastructure communication, and infomobility services. Sensing mobility concerns traffic monitoring, environmental observation, disaster mapping, crowd monitoring, and infrastructure inspection. Human mobility is considered as an emerging extension related to urban air mobility (UAM), electric vertical take-off and landing (eVTOL) systems, and low-altitude aerial corridors. Cross-cutting issues such as energy autonomy, solar-assisted drones, multimodal integration, safety, communication, regulation, sustainability, and public acceptance are discussed. The proposed framework provides a structured basis for assessing drones as components of sustainable, resilient, and multimodal mobility systems.

Drones doi: 10.3390/drones10090653

Authors: Dominik Jerinić Tomislav Radišić Karolina Krajček Nikolić Mario Muštra

The integration of unmanned aerial vehicles (UAVs) into shared airspace requires on-board safety mechanisms for self-separation without cooperation between platforms or centralised air-traffic services. This paper proposes the UAV Safety Bubble (USB), which is a six-layer geometric model that defines an adaptive safety envelope around the UAV. Each layer corresponds to a distinct physical contribution: platform dimensions, positioning uncertainty, communication performance, wind disturbance, detection-processing latency, and avoidance manoeuvrability. The model was evaluated through 5300 closed-loop Monte Carlo simulation instances across six conflict scenarios: static-obstacle avoidance; head-to-head encounters; 90°, 30°, and 60° approaches between two dynamic UAVs; and a non-reacting-intruder case. Platform, sensing, communication, and environmental parameters were sampled from uniform distributions across operational ranges. Under these simulation assumptions, no instance produced an intrusion into the fifth layer (USB5), which was the model’s operational separation boundary. The smallest clearance between the USB5 outer edge and the obstacle’s centre of mass was 35.36 m, recorded in the 90° approach scenario, and the one-sided 95% upper bound on the composite intrusion probability over the 1000 independent parameter sets was 0.299%. The adaptive envelope achieved this at per-scenario median route-length overheads of 15–46% relative to nominal straight-line routes. The USB complements downstream planners by providing an adaptive no-go region. The avoidance manoeuvres evaluated are restricted to the horizontal plane; full 3D avoidance is left for future work.

Drones doi: 10.3390/drones10090652

Authors: Jiaqi Li Qinghua Zeng Songnan Duan Junjie Wu Yilin Li Yudi Sun Qian Gao

Object detection in unmanned aerial vehicle (UAV) imagery is severely challenged by extremely small object scales, cluttered backgrounds, and pronounced foreground–background imbalance, which jointly degrade the accuracy of general-purpose detectors. This paper presents HSF-Net, a small-object detection network built upon YOLOv11s through three complementary enhancements. First, a Hierarchical Edge Enhancement Module (HEEM) employs orthogonal strip-convolution decomposition with zero-initialized residual fusion to enhance and re-weight the fine-scale edge and texture cues that are progressively attenuated in deep convolutional backbones. Second, a Sparse-Aware Feature Modulation (SAFM) module replaces concatenation-based fusion in the top-down neck pathway, coupling a sparse foreground gate with channel-wise scale modulation to confine cross-scale aggregation to object-bearing regions. Third, the detection head is restructured from {P3, P4, P5} to {P2, P3, P4}, introducing a high-resolution pathway for tiny targets while removing the original low-resolution P5 branch. On VisDrone, HSF-Net attains 46.4% mAP50 and 28.5% mAP50–95, exceeding the baseline by 7.1 and 4.8 percentage points, respectively, while reducing the parameter count from 9.4 million to 3.6 million. The model achieves an end-to-end throughput of 131.3 FPS on an NVIDIA RTX 3090, although the high-resolution P2 branch increases the computational cost to 39.5 GFLOPs. After dataset-specific training and evaluation on the markedly different TinyPerson benchmark, HSF-Net outperforms YOLOv11s by 5.2 percentage points in mAP50, indicating that its relative performance advantage persists under a substantially different target-scale distribution and maritime background.

Drones doi: 10.3390/drones10090651

Authors: Gálata Martínez-Alonso Enrique Aldao Fernando Veiga-López Higinio González-Jorge

Uncrewed aerial vehicles (UAVs) represent a promising alternative for building facade cleaning, improving efficiency while reducing the risks associated with working at height. However, such operations are particularly challenging due to the reaction forces generated by fluid discharge, which can compromise UAV stability and reduce cleaning accuracy. To mitigate this issue, this work proposes an integrated methodology for the preliminary assessment of UAV-based water-jet facade cleaning operations. First, a Computational Fluid Dynamics (CFD) model is developed to characterize jet-induced forces as a function of nozzle parameters and stand-off distance. The proposed force estimation model is evaluated experimentally at one low-pressure operating condition through flight tests using a commercial water-jet cleaning system. These disturbance forces are then incorporated into an over-actuated UAV dynamic model, together with a disturbance-aware compensation strategy, enabling the evaluation of vehicle stability and trajectory tracking under the simulated operating conditions. In parallel, a trajectory generation and operational planning framework is proposed, allowing systematic coverage of facades while maintaining the prescribed stand-off distances. The proposed framework is evaluated through nine simulation scenarios considering different flow rates and disturbance levels. The results demonstrate accurate trajectory tracking, with RMS errors below 3.4 cm across all cases, while the proposed disturbance compensation strategy reduces the RMS tracking error by approximately 67% compared with a non-compensated configuration. The results highlight the potential of UAV-based water-jet systems for autonomous and efficient facade maintenance, while also illustrating the importance of accounting for jet-induced disturbances in the control design.

Drones doi: 10.3390/drones10090650

Authors: Metin Ozturk

Integrating uncrewed aerial vehicles (UAVs) and high-altitude platform stations (HAPS) into terrestrial cellular wireless networks is central to emerging sixth-generation (6G) communication systems. However, the energy budgets of both the ground network and the power-constrained aerial platforms are a serious concern. Cell switching, which selectively places lightly loaded small base stations (SBSs) into sleep mode, is an important energy-saving mechanism, but existing feasibility criteria are typically based either on maintaining a target data rate or merely preserving coverage for the users originally served by the switched-off SBSs (i.e., displaced users). These two approaches represent opposite ends of the quality-energy tradeoff: rate-based policies require a high signal-to-interference-plus-noise ratio (SINR), limiting energy savings, whereas coverage-based policies permit more aggressive sleeping at the expense of quality of service (QoS). This work proposes a novel semantic-aware cell switching policy whose feasibility criterion is a semantic-service requirement rather than a bit-centric target: an SBS is put into a sleep mode if and only if its displaced users still satisfy an assumed semantic-service SINR criterion. The policy is tested in a multi-tier heterogeneous network comprising an always-on macro base station (MBS), switchable SBSs, a tier of UAV base stations (UAV-BSs), and a HAPS-mounted International Mobile Telecommunications (IMT) base station (HIBS), and it is solved by a greedy algorithm. For the deployment and parameter set considered, and with the HIBS present, the semantic criterion deactivates the entire SBS layer while every user continues to satisfy the assumed semantic-service SINR criterion, whereas the rate criterion deactivates none.

Drones doi: 10.3390/drones10090649

Authors: Gengsong Li Yi Liu Qibin Zheng Kun Liu

Dynamic events require coordinated task allocation (TA) and path planning (PP) for multiple unmanned aerial vehicles (UAVs) to maintain executable mission progress. Existing coupled methods often use path information only as a precomputed cost or downstream refinement result, limiting its reuse after dynamic changes. This paper formulates dynamic task allocation and path planning (DTAPP) as a dynamic multi-objective optimization problem considering remaining target value, mission makespan, path feasibility, and execution-state inheritance. A three-role dynamic multi-swarm crow search algorithm (3R-DMCSA) is proposed, in which exploiter, explorer, and diversifier role-based swarms share a crow search-based update structure, feasibility-aware comparison, and leader-selection structure but use TA- and PP-specific encodings, objective preferences, initialization, and dynamic responses. A path matrix connects the layers by storing candidate paths and their attributes, which are fed back to TA, and supporting rolling-horizon leading flight-segment refinement. Experiments involving three dynamic urban scenarios compare the method with five baselines and evaluate its path-matrix feedback and rolling-horizon refinement. Compared with the strongest baseline, our approach improves mission-value acquisition by 10.6%, 16.2%, and 32.0% in the three scenarios, while maintaining near-complete target coverage and reliable flight-segment execution. Path-matrix feedback improves mission-value acquisition by 5.2–26.1% over the configuration without PP-to-TA path feedback, while rolling-horizon segment refinement reduces replanning latency by 48.8–70.5% compared with refining all planned segments without significantly compromising mission performance.

Drones doi: 10.3390/drones10090648

Authors: Peter Yacoub Mohamed Malek Kaouach Esraa Khatab Omar Shalash

Search-and-rescue (SAR) operations in disaster environments require drone swarms to coordinate efficiently despite incomplete information and potential communication failures. Existing stigmergy-based approaches provide low-bandwidth coordination but rely on fixed rules, whereas multi-agent reinforcement learning (MARL) can learn adaptive behaviors but often struggles with coordination under partial observability. To address these limitations, this paper proposes a Hybrid stigmergy–MARL framework that introduces directional pheromone-gradient observations, enabling each drone to infer the direction of likely victims and unexplored regions using locally available information. The proposed framework combines reinforcement learning with four virtual pheromone layers representing coverage history, victim likelihood, environmental risk, and communication quality. Victim detection is modeled through an abstract short-range thermal/visual sensing mechanism, while environmental information is shared through pheromone-based environmental memory to reduce dependence on direct communication. The simulated environment consists of a 40 × 40 grid, where each grid cell represents a discrete two-dimensional location. Victims occupy a single grid cell, and obstacles are modeled as static two-dimensional impassable cells. Experimental results show that the proposed approach achieved 98.9% area coverage and 93.3% victim detection, compared with 81.8% coverage and 71.7% victim detection for the RL-only baseline. Ablation experiments confirmed that directional gradient observations are the primary contributor to these improvements, while communication-loss experiments demonstrated robust performance even under complete communication outage. These findings indicate that directional pheromone-gradient observations provide an effective and communication-efficient mechanism for decentralized swarm coordination, improving search effectiveness and operational robustness in post-disaster SAR scenarios.

Drones doi: 10.3390/drones10090647

Authors: Yi Cui Tongxin Zeng Bin Li

Trajectory planning is a key enabling technology for UAV swarms operating in complex and obstacle-rich environments. This paper proposes a distributed nonlinear model predictive control (NMPC)-based trajectory planning method for UAV swarms, where terminal target reaching, prescribed formation maintenance, obstacle avoidance, and inter-UAV collision avoidance are incorporated into a unified predictive optimization framework. To reduce the online computational burden, a control parameterization strategy is introduced to describe the control input using M control segments, thereby reducing the dimension of the online decision variables. Furthermore, an exact-penalty-based constraint transcription method is developed to transform the original constrained optimal control problem into a lower-complexity finite-dimensional nonlinear programming problem, while efficiently handling velocity constraints, obstacle avoidance constraints, and inter-UAV collision avoidance constraints. Simulation results for a three-UAV swarm in a cluttered environment demonstrate that the proposed method can generate dynamically feasible and collision-free trajectories, while enabling the swarm to reach the assigned target positions and preserve the desired formation within a certain formation error. Furthermore, real UAV swarm flight experiments were conducted to further validate the practical feasibility and online applicability of the proposed distributed NMPC framework for UAV swarm trajectory planning.

Drones doi: 10.3390/drones10090646

Authors: Chenhao Lu Kun Jiang Ying Guo Ancheng Li Wenqing Zhao Zhengwen Zou Xinyue Ren Guangzu Liu

This paper investigates a wireless ad hoc network node registration and access system based on a hybrid Frame Slotted ALOHA and Time Division Multiple Access mechanism for Unmanned Aerial Vehicle (UAV) charging station cluster management scenarios. Based on combinatorial mathematics and the inclusion–exclusion principle, the exact conditional probability distribution of the number of successfully accessed nodes within a single frame is derived, and a closed-form analytical expression for the total system access delay as a function of node population is established, revealing the existence of an optimal operating point. Building upon this foundation, a Dynamic Access Probability (DAP) mechanism is introduced, which adaptively regulates the number of nodes participating in contention per frame, effectively suppressing collisions and significantly reducing the overall system access delay. To address the difficulty of obtaining the real-time node count in practical systems, a Probability Model-based Maximum Likelihood Estimation algorithm is further proposed, which takes the number of successfully accessed nodes observable in each frame as input to infer the current number of unaccessed nodes. Simulation results demonstrate that the PMLE algorithm achieves superior performance in both estimation accuracy and stability, effectively supporting the accurate operation of the DAP mechanism.

Drones doi: 10.3390/drones10090645

Authors: Jian Deng Honghai Zhang Mingzhuang Hua Bingjie Liang

To effectively address the challenges of complex spatiotemporal conflicts, dynamic obstacle avoidance, and coordinated multi-objective optimization in preemptive multi-UAV logistics delivery within complex airspace, this study proposes a Hybrid Improved Multi-Objective Cuckoo Search algorithm (HI-MOCS) for preemptive multi-UAV cooperative logistics scheduling and planning. To overcome the limitations of conventional MOCS, including a low proportion of feasible solutions under complex constraints, susceptibility to local optima, and uneven distribution of multi-objective solution sets, a multi-constraint physical model and a multidimensional evaluation framework are established for preemptive scheduling. A positive knowledge-transfer mechanism based on the co-evolution of primary and auxiliary populations is developed, in which constraint-violation information is used to guide infeasible solutions toward the feasible region. A hybrid heuristic population initialization strategy combining emergency-order priority and spatial scanning rules is introduced to increase the proportion of high-quality feasible solutions in the initial population. In addition, a nonlinear dynamic adaptive parameter-adjustment strategy is designed to balance global exploration and local exploitation, while an iterative truncation-based environmental selection mechanism using the shortest-distance criterion is employed to improve the distribution quality of the Pareto solution set. The experimental results show that, in the benchmark scenario, HI-MOCS achieves an average increase of 33.26% in the total order completion rate and an average reduction of 15.34% in emergency response time compared with 11 multi-objective optimization algorithms, while also exhibiting favorable performance in terms of flight distance per completed order. The fleet-size analysis shows that the 15-UAV configuration achieves the lowest best mean fitness. The safety-distance analysis indicates that, compared with the other safety-distance settings, the 30 m setting increases the total order completion rate by an average of 26.55%, while reducing emergency response time and flight distance per completed order by 27.36% and 33.72%, respectively. The task-scale analysis shows that the 50-order scenario achieves the lowest best mean fitness. Further ablation experiments demonstrate that, compared with the average performance of MOCS and the four single-strategy variants, the complete HI-MOCS improves the total order completion rate by 20.27%, while reducing emergency response time and flight distance per completed order by 20.71% and 36.18%, respectively. The HV, IGD, and Pareto-front results further confirm that the synergistic effects of the four improvement mechanisms effectively enhance the multi-objective optimization performance and the quality of the nondominated solution set. The current study is still validated under simulation conditions assuming reliable GNSS positioning and communication links, without explicitly considering communication delays.

Drones doi: 10.3390/drones10090644

Authors: Changqi Yang Hongjie Hu Yi Ai

Traditional target-oriented task allocation and path planning methods often struggle to balance real-time responsiveness to dynamic task alterations with multi-UAV cooperative operations in complex urban environments under meteorological disturbances. To address these challenges, this paper proposes a dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions. Integrating an improved Discrete Particle Swarm Optimization (DPSO) algorithm with a decoupled Soft Actor–Critic (SAC) and B-spline smoothing framework, the proposed approach optimizes upper-level task allocation and lower-level trajectory planning within a 3D joint meteorological-obstacle feasible region. For task scheduling, an improved DPSO algorithm embedded with a spatial topology guidance mechanism dynamically coordinates task flows governed by Poisson processes. effectively addressing the spatial blindness and fragmented route assignments typical of conventional discrete optimization. Concurrently, local trajectory replanning executes receding-horizon spatial exploration via SAC deep reinforcement learning, followed by B-spline refinement to strictly enforce UAV kinematic limits, systematically bridging continuous-space exploration with low-level flight compliance to overcome the kinematic infeasibility common in pure learning-based models. Validated through extensive Monte Carlo comparative simulations (N=50) and further verified by a high-fidelity AirSim dynamic physics engine, the results demonstrate that: (1) The improved DPSO constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks. (2) The lower-level replanning achieves an average execution time of 3.60±0.18 s and a path success rate of 95.8±1.2%, in numerical tests, while maintaining a 96.2% kinematic feasibility rate under realistic rigid-body inertia and aerodynamic drag. While the current 3.60 s latency presents a potential bottleneck for millisecond-level dynamic emergency reactions, the developed framework offers a highly effective and safe closed-loop dynamic scheduling solution that lays a rigorous computational foundation for low-altitude urban inspections.

Drones doi: 10.3390/drones10090643

Authors: Jaeseok Park Chanoh Park Inkyu Sa Soohwan Kim Hea-Min Lee Donghee Noh Ho Seok Ahn

UAVs increasingly rely on accurate SLAM for aerial mapping and inspection in GPS-denied environments. 3D Gaussian Splatting (3DGS) has opened a new direction for UAV mapping by allowing SLAM systems to build dense, photorealistic, and renderable maps. Yet in 3DGS SLAM the map is optimized from the pose graph, so a false loop closure can deform both the UAV trajectory and the Gaussian map consumed by downstream UAV autonomy. Reliable loop admission is therefore relevant to safe GPS-denied operation because it protects the state and map estimates on which autonomous functions depend. The present work evaluated this upstream estimation-integrity problem; it did not measure closed-loop guidance, control, or navigation-safety outcomes. We address the loop-admission problem that arises after a place-recognition (PR) module proposes a candidate loop and relative-pose seed. GSSeq is a rendered-reference sequential verifier that uses the current Gaussian map as active evidence before inserting a loop factor. It renders RGB-D references with the PR seed, checks LiDAR/rendered-depth consistency and image/rendered-reference consistency over active support, and propagates the seed through a short query trajectory window. A loop is admitted only when this evidence remains geometrically supported and photometrically stable. On fixed LiDAR-PR candidate sets spanning MARS-LVIG, MUN-FRL, and independent NTU-VIRAL aerial sequences together with ground-mobility benchmarks, GSSeq provides a competitive precision-oriented operating point while suppressing false loop admissions. Thresholds calibrated only on NTU-VIRAL spms_01 combine rendered RGB agreement with LiDAR-submap geometry and are then frozen for spms_02. On this held-out sequence, GSSeq rejects all seven false-positive BTC factors while retaining one of three true-positive factors. The trajectory-to-map experiment reduced ATE RMSE from 2.609m to 1.417m and improved selected-view PSNR from 13.80dB to 16.46dB. These results show that rendered verification can preserve an aligned, renderable UAV trajectory-map pair before unsupported loop factors reshape the SLAM map.

Drones doi: 10.3390/drones10090642

Authors: Xingda Li Zhikun Liu Jianqiang Zhang Yiping Liu Pengfei Zhang Ling Tan

Unmanned surface vehicle (USV) clusters operating in contested maritime environments face communication degradation from jamming, satellite denial, and partial node loss. Existing countermeasures either react after link failure or rely on a single signal quality indicator. A framework is presented that integrates two complementary mechanisms for resilient USV cluster communication: (1) a multi-indicator communication quality metric Qcomm that fuses signal-to-noise ratio, packet loss rate, latency, and temporal stability into a single scalar; and (2) a multi-modal backup communication chain spanning acoustic modem, optical link, and multi-hop RF(Radio Frequency) relay that provides physical-layer redundancy when primary radio frequency links are degraded. Across five representative failure scenarios simulated on a seven-vehicle cluster with 50 independent runs per configuration, the multi-indicator Qcomm metric achieves a mean of 0.521, outperforming single-indicator baselines on the composite Qcomm metric (Cohen’s d = 8.020, p < 0.0001, n = 250 per method); Qcomm is the framework’s own optimization target; this comparison is, therefore, presented as an internal consistency demonstration rather than an independent validation. The framework reduces recovery time from 113.8 s (single-indicator) to 15.3 s—an 86.6% improvement. With the backup communication chain enabled, delivery rates exceed 94% across all methods and scenarios. A direct Monte Carlo ablation (50 seeds × 5 scenarios) reveals that multi-indicator fusion is the primary driver of assessment accuracy and recovery speed, while the multi-modal backup chain is the dominant delivery driver: without it, delivery falls from 94.6% to 24.2%. A sigmoid-blended topology utility function is included as an architectural design component; its independent validation requires extended-duration threat experiments identified as future work. The contributions of this work are the validated multi-indicator fusion metric and the multi-modal backup chain architecture, which together provide a practical foundation for resilient USV communication.

Drones doi: 10.3390/drones10090641

Authors: Zhihao Chen Xiaming Yuan Heng Shi Jihong Zhu

Random communication delay, delay jitter, and packet loss cause remote bearing measurements in dual-UAV bearing-only cooperative passive localization to arrive after their generation times. Direct fusion of these historical measurements with current local measurements misaligns the observation rays, platform geometry, and target state. Within the Cubature Kalman filter (CKF) framework, this article constructs a timestamped cache that preserves historical states, measurements, and platform geometry, and investigates two processing strategies. The Predictive Compensation Strategy (PCS) migrates a historical angular innovation to the current geometry and inflates its covariance. Timestamp-Aligned Replay (TSA) activates the historical record at the measurement generation time, restores the corresponding dual-platform observation geometry, and propagates the corrected state to the current time. The evaluation jointly considers the baseline terminal-window error B, average-delay sensitivity S, delay-jitter sensitivity J, and packet-loss response intensity L. Monte Carlo communication scans show that, under the tested conditions, TSA achieves lower terminal-window localization error and a more favorable overall communication-disturbance response than CDS and PCS, while incurring higher average computational cost.

Drones doi: 10.3390/drones10090640

Authors: Xiangrui Tian Kang Miao Song Zeng Xiaohan Xianyu

To address the challenges of limited storage space and mobile launch-and-landing operations for vehicle-mounted multirotor UAV swarms, this paper proposes a stackable Cone-Sleeve UAV swarm system. The UAV airframe incorporates a through-body central sleeve integrated with conical guidance structures, which cooperate with a vertical guide rod mounted at the center of the mobile platform to achieve geometric passive pose correction during landing. In addition, a UWB-based onboard local positioning and navigation system is developed, in which a tightly coupled UWB/IMU estimator is employed to achieve high-precision relative state estimation for the UAV swarm. A five-stage finite state machine (FSM) schedules the landing sequence. During terminal landing, a motion feedforward control strategy is introduced for dynamic motion compensation and to ensure seamless state transitions. Simulation and vehicle-mounted experimental results demonstrate that, strictly under low-speed (≤0.5 m/s), constant-velocity straight-line motion conditions, the proposed system enables autonomous vertical takeoff and landing as well as rapid stacked launch-and-landing operations for multiple UAVs. The cooperative guidance strategy integrating active control and the Geometric Passive Guidance (GPG) mechanism improves the precision and speed of swarm landing operations. The proposed system provides a feasible system-level solution for the storage, transportation, and autonomous rapid launch-and-landing of high-density UAV swarms.

Drones doi: 10.3390/drones10080639

Authors: Wei Xia Peng Chen Feifei Song Kai Li Tong Zhang Kun Zhu

Unmanned Aerial Vehicles (UAVs) have experienced rapid development due to their advantages of low cost, high efficiency, flexibility, reliability, and strong environmental adaptability. To fully leverage the potential of UAV swarms in multi-task scenarios, optimizing task allocation has become a crucial direction to enhance the efficiency of UAV swarms. While existing task allocation methods have achieved promising results, frequent inter-UAV information exchange can impose substantial communication overhead in distributed UAV swarms, particularly in resource-constrained applications such as emergency rescue and mountainous operations. In this context, to reduce inter-UAV interactions while maintaining the solution quality of task allocation, this paper first analyzes the factors affecting communication interactions between UAVs. Based on this analysis, we utilize historical bidding information to infer other UAVs’ positions and employ estimation strategies to resolve task conflicts, thereby reducing communication iterations. Furthermore, we propose a bidding-based grouping method to eliminate ineffective communication interactions. Finally, we introduce a network simplification algorithm based on reducing the number of triangular network topologies to optimize the communication network structure. Simulation results demonstrate that the proposed algorithm significantly reduces inter-UAV interactions while preserving the number of allocated tasks, with a maximum observed increase of only approximately 8% in task waiting time across the evaluated simulation settings.

Drones doi: 10.3390/drones10080638

Authors: Zishuang Pan Dazhao Yu Wei Han Xichao Su Jie Wang Shansong Song Bing Wan

Automatic carrier landing of fixed-wing UAVs remains challenging under deck motion, carrier airwake, gusts, and actuator faults. This paper proposes a robust flexible predefined-time prescribed performance control (RFPTPPC) framework with beneficial disturbance utilization (BDU). A control-oriented six-degree-of-freedom cascaded model with direct lift control is first established. To address the temporary infeasibility of fixed predefined-time PPC boundaries, direction-selective flexible boundaries restore the admissible attitude error region when the nominal envelope is threatened, while a smoothly coordinated recovery branch drives the error inward. An adaptive super-twisting extended state observer (ASTESO) reconstructs lumped disturbances in the cascaded loops. Using the ASTESO outputs, BDU evaluates each disturbance component, retains those that favor error convergence, and compensates for adverse components, thereby improving attitude tracking accuracy and maintaining PPC feasibility. Lyapunov analysis establishes practical predefined-time stability of the closed-loop system. Comparative simulations demonstrate improved trajectory tracking, attitude regulation, actuator coordination, and robustness under randomized landing conditions.

Drones doi: 10.3390/drones10080637

Authors: Aykut Sirma Angelos Plastropoulos Gilbert Tang Argyrios Zolotas

Search-and-rescue (SAR) missions following earthquakes and other disasters require aerial video perception systems that do more than detect objects in isolated frames. Operators must maintain the identities of access points, vehicles, responders, hazards, and other mission-relevant targets despite UAV ego-motion, dust, debris, occlusion, scale variation, and abrupt scene transitions. This paper presents E’CHIT (Edge-Oriented Colour Histogram Instance-Guided Tracking), a deployment-oriented, operator-centric UAV tracking framework for real-world disaster-response applications. Its primary scientific contribution is an identity-stabilised, detector-assisted tracking methodology. YOLOv8-seg proposals trained on D’RespNeT initialise and refresh tracks; a Custom-RE3 recurrent module propagates target states through short detector dropouts; and a lightweight EOMC verifier, based on edge orientation, mean colour, and shape consistency, determines whether tracks should be accepted, refreshed, or reacquired. A scene-cut watchdog that combines luminance mean absolute difference (MAD) with HSV histogram divergence prevents stale identities from carrying over after hard edits or sudden feed changes. Custom-RE3 is the continuation module implemented and evaluated in this study. The surrounding E’CHIT wrapper follows an initialise–reseed–verify–reset cycle and is tracker-adaptable at the software-interface level: another compatible SOT or MOT continuation module can be integrated through adapter modifications, state and bounding-box conversion, and method-specific retuning, followed by independent validation. All reported quantitative results therefore apply to the Custom-RE3 implementation. D’RespNeT, the optional reinforcement learning (RL) warm start, the HUD, and the deployment stack support this central tracking contribution. D’RespNeT provides 28 polygon-annotated SAR classes. An author-developed PPO/SAC script is used only during offline detector training. In the reported runs, it produces different early optimisation trajectories for selected difficult or under-represented classes, while the default supervised schedule remains the strongest final global mAP reference. No RL policy runs during deployment; the detector architecture, parameter count, and inference graph remain unchanged. Evaluation on D’RespNeT and authentic disaster-response UAV footage shows that E’CHIT increases Success@IoU ≥ 0.5 from 0.62 to 0.79, reduces identity switches by approximately 71%, and maintains real-time 1080p performance, achieving 164–330 FPS for single-target tracking and 24–100+ FPS for end-to-end multi-target operation on an RTX-class GPU using FP16. The VOT2014, NT-VOT211, and VOTS2024 figures reproduce historical result spaces reported in the literature and include a clearly labelled, non-official E’CHIT operating-point marker solely for context. This marker was not produced using the corresponding official datasets, toolkits, reset rules, or submission routes; it is excluded from the primary quantitative claims and must not be interpreted as a leaderboard rank or a protocol-identical comparison. Overall, the system demonstrates how identity-stable UAV tracks can provide actionable operator cues for target monitoring, entry-point assessment, and UAV–UGV/ground-team coordination in cluttered disaster scenes.

Drones doi: 10.3390/drones10080636

Authors: Lei Zuo Ying Wang Jialu Liu Yu Lu Ruiwen Gu

Unauthorized low-altitude UAVs can challenge conventional fixed-parameter interception algorithms through agile maneuvers. This study develops a three-dimensional one-on-one terminal-interception simulation environment that incorporates protected-zone penetration, a within-step geometric interception criterion, and kinematic constraints. The intruder, denoted as the red UAV, combines six physically interpretable maneuver templates to generate structured evasive penetration behavior. The defender, denoted as the blue UAV, augments blended generalized proportional navigation (B-GPN) with a bounded residual corrective acceleration produced by deep reinforcement learning, thereby forming a hybrid architecture that combines a geometry-based nominal guidance command with reward-driven bounded compensation. In standardized tests on 1000 unseen scenarios, the implemented residual PPO pipeline increased the interception rate from 63.8% for nominal guidance to 94.1% (95% Wilson interval: 92.46–95.40%) and maintained at least 88.0% interception under the tested control-delay, kinematic, and noise perturbations. It also achieved the highest interception rate among the evaluated residual-learning implementations under both the stable-configuration comparison and the auxiliary task-side-controlled check; this result is limited to the reported implementations and is not a general ranking of algorithm families. These findings indicate that bounded residual learning can compensate for structural limitations of conventional guidance under the evaluated conditions.

Drones doi: 10.3390/drones10080635

Authors: Watchara Ruangsang Patiyuth Pramkeaw

Wildfires need a fast response, and a small unmanned aerial vehicle (UAV) carrying its own detector is an appealing way to find them early. Almost every published UAV fire detector is validated on a single corpus, which leaves open the question that matters to an operator: how much of the reported accuracy survives a change in scene. We trained unmodified YOLOv8n, YOLOv8s, and YOLOv8m on two public benchmarks that ship official test splits, D-Fire and the UAV subset of the Flame and Smoke Detection Dataset, under one fixed recipe with three seeds on D-Fire, then evaluated every checkpoint on both test splits with frozen weights. In domain, YOLOv8m reached 79.04 ± 0.21% mAP@0.5 on D-Fire, within 0.04 points of the published value for the same architecture on the same split, and 92.71% on FASDD_UAV. Moved across corpora, the same weights fell 38 and 65 points below a locally trained model, with fire degrading about twice as far as smoke. A perceptual-hash audit then found near-duplicates of training images in 46.0% and 90.7% of the two test sets; re-evaluating on the uncontaminated remainder costs 2.8 and 12.4 points, closes two-thirds of the apparent difficulty gap between the corpora, and leaves corrected transfer shortfalls near 26 and 62 points. Onboard, an F450 carrying a Pixhawk 2.4.8 and a Jetson Nano A02 flew and detected a controlled fire, but full-resolution inference ran 4.0 to 41.4 times slower than the video it consumed. Both gaps must close before a platform of this class can support operational wildfire monitoring.

Drones doi: 10.3390/drones10080634

Authors: Richard Avella Camila A. González Paula N. López

Atmospheric icing is one of the most critical meteorological hazards for unmanned aerial vehicles (UAV), whose operation under adverse conditions—high latitudes, elevated altitudes, long-endurance missions without pilot intervention—particularly exposes them to ice accumulation on aerodynamic surfaces and propellers. Unlike manned aviation, where this phenomenon has been extensively studied and regulated, a significant knowledge gap exists in the UAV domain that limits the development of effective protection systems adapted to energy constraints. This article provides an integrative review—conducted with a systematic search strategy following PRISMA reporting guidelines—of atmospheric ice formation mechanisms, their specific effects on UAV propellers, and the two most promising mitigation approaches: electrothermal modelling for the optimisation of electric heating systems and the development of functional surface materials including superhydrophobic coatings (SHC); composites with conductive nanofillers (graphene, carbon nanotubes); and piezoelectric actuators. The analysis demonstrates that hybrid systems combining passive and active strategies managed by intelligent control represent the most viable solution for extending UAV operational envelopes under known icing conditions, with a projected reduction in anti-icing system energy consumption of at least 40% relative to conventional continuous heating. This estimate is based on the most conservative published evidence: pulsed electrothermal de-icing achieves 40–60% savings versus continuous anti-icingSHC-assisted hybrid heating reduces IPS power by more than 80% on static aerofoils; and rotary-wing pulsed systems reduce mean consumption by 60–75% relative to continuous operation. Key research gaps are identified, and a prioritised future research agenda is proposed to support the development of certifiable anti-icing systems for rotary-wing UAV platforms.

Drones doi: 10.3390/drones10080633

Authors: Wenhui Ma Han Zhang Shuangxi Liu

In view of the task allocation problem under communication constraints, this paper proposes a distributed performance impact (DPI) algorithm for heterogeneous multi-UAV cooperation. First, the task allocation problem is modelled, incorporating task requirements, heterogeneous flight performance and non-ideal networked communication. Then, a multi-phase DPI algorithm is designed in five stages: task inclusion, cluster generation, conflict resolution, coalition formation and task segmentation. Based on task inclusion, a communication-reachable networked cluster generation method is developed. The conflict resolution mechanism is improved based on the removal-performance-impact and task contribution rate. Further considering independently unachievable tasks, a dual-fallback mechanism is constructed by a pruning-redundancy coalition formation scheme and a task- segmentation scheme, thereby enhancing both system operational efficiency and task-completion certainty. Finally, simulations are conducted to demonstrate the effectiveness, superiority and robustness of the proposed DPI algorithm.

Drones doi: 10.3390/drones10080632

Authors: Xiaojia Yan Zhangsong Shi Shiyan Sun Huihui Xu Huimin Zhu Qingping Hu Weiming Zhu Yinglei Li

Cross-view geo-localization (CVGL) is a critical technology used in unmanned aerial vehicles (UAVs) and widely applied in navigation and target localization tasks. However, owing to the extreme perspective disparity between UAV oblique views and satellite vertical views, CVGL still involves significant challenges, including geometric distortion caused by viewpoint differences, drastic appearance inconsistencies, and the difficulty in bridging semantic gaps between heterogeneous data. To address these issues, we propose a novel CVGL method named dynamic-feature collaborative optimization and semantic-alignment network (DFSA), designed to extract robust feature representations and achieve fine-grained alignment. Specifically, the DFSA employs a residual-based vision transformer as the backbone to capture global context while alleviating the training instability and feature collapse often associated with standard transformers. To bridge the semantic gap between global and local features, we design a feature optimization module comprising a local feature enhancer and a global feature aggregator. This module establishes a closed-loop collaborative system that facilitates top-down semantic guidance and bottom-up detail feedback. Furthermore, we introduce a semantic segmentation and alignment module that adaptively partitions images into semantic regions based on feature response distributions, shifting the matching granularity from the global level to the semantic region level to effectively overcome feature mismatches caused by positional offsets and scale variations. Extensive experiments conducted on the University-1652 and SUES-200 datasets demonstrate the superior image retrieval performance of the proposed DFSA. Specifically, DFSA achieves a Recall@1 of 94.87% and an Average Precision (AP) of 95.32% on the University-1652 dataset and maintains highly competitive Recall@1 performances between 96.83% and 99.25% across various altitudes on the SUES-200 dataset. These results validate the model’s effectiveness in handling extreme viewpoint changes for UAV-based cross-view image retrieval tasks.

Drones doi: 10.3390/drones10080631

Authors: Yichi Zhang Jianxiang Xi Wei Li Le Wang Nanchi Liu

For malicious distributed unmanned aerial vehicle (UAV) swarms operating in civilian airspace, this paper proposes a novel dual-channel induced countermeasure strategy (DCICS) with prescribed countermeasure flight trajectories, in which the designable countermeasure signals are injected into the sensor–controller (SC) channel and the controller–actuator (CA) channel of partial or all UAVs, simultaneously. Firstly, we establish a dual-channel induced countermeasure protocol, in which the sensor-induced countermeasure signal is modeled as a continuous function of swarm dynamics, whereas the actuator-induced countermeasure signal is generated through a countermeasure signal generation exosystem (CSGES). Then, we derive a closed-form expression of the countermeasure flight trajectory, explicitly characterizing the effect of the countermeasure signals on the swarm motion. Furthermore, a necessary and sufficient condition for the feasibility of the DCICS is derived. By introducing adjustable performance factors, we construct a robust H∞ framework and provide the design criteria for the dual-channel induced countermeasure signals accordingly. The proposed strategy can drive the formation of the distributed UAV swarm along a prescribed countermeasure flight trajectory while maintaining the original formation structure. Finally, we present numerical simulation examples to validate the effectiveness of the theoretical results.

Drones doi: 10.3390/drones10080630

Authors: Wenbiao Gan Chenxi Guan Junjie Zhuang Jingwei Ma Xiaozhang Liu Shaojiang Dong Zihan Song Jiangtao Zhang Guoqi Zeng

Owing to its inherent flight safety, low takeoff and landing requirements, and favorable economic efficiency, the autogyro UAV, especially its electric and hybrid-electric variants, has become a core platform for low-altitude aviation missions such as transportation, inspection, and surveillance in plateau and offshore regions. However, the low air density and low Reynolds number conditions encountered in plateau regions can induce aerodynamic issues such as premature laminar flow separation, dynamic stall, and increased induced drag, which directly reduce payload capacity and endurance of small electric autogyro UAVs. In offshore environments, strong winds, turbulence, and gust disturbances intensify rotor–wake interactions, cause abrupt variations in aerodynamic loads, and reduce control margins, which severely restricts the mission reliability and flight safety of low-altitude unmanned platforms. These environmental effects collectively degrade rotor performance, including reduced aerodynamic efficiency and insufficient lift generation, and further amplify the energy constraint of electric/hybrid-electric propulsion systems. In response to bottlenecks that restrict the practical application of autogyro UAVs in extreme environments, this paper systematically reviews research progress on lift vector control for autogyro UAV rotors operating under such conditions. First, the typical aerodynamic problems encountered by autogyro UAVs in plateau and offshore environments are summarized, and their underlying physical mechanisms are analyzed from both system-level and local-flow perspectives, with a focus on how environmental factors affect the autorotation stability of unmanned platforms. Subsequently, the development of passive lift vector control technologies is reviewed, with an emphasis on the aerodynamic benefits of passive pitch mechanisms, vortex generators, and blade-tip winglets, as well as their engineering feasibility for small autogyro UAV blades. Active lift vector control technologies are then examined, including air-jet flow control, synthetic jets, and trailing-edge flaps, with discussions of their potential to delay flow separation and stall, enhance rotor aerodynamic efficiency, and an assessment of their adaptability to the energy and structural constraints of unmanned platforms. Finally, a lift vector control strategy suitable for autorotating rotors of autogyro UAVs is proposed, based on careful consideration of energy consumption, structural constraints, and control effectiveness. It provides a reference for aerodynamic optimization and flight control research on electric and hybrid-electric autogyro UAVs operating in extremely low-altitude environments.

Drones doi: 10.3390/drones10080629

Authors: Ravil I. Mukhamediev Valentin Smurygin Liudmila Gorodetskaya Yan Kuchin Adilet Dauletuly Nursultan Kuldeyev Adilkhan Symagulov Irina Fedorovich

This study investigates the automatic classification of crop phenological stages from low-altitude UAV RGB imagery. The dataset included 11,489 images of five crops: sunflower, rapeseed, soybean, wheat, and barley. The images were annotated using the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale, with labels corresponding to either single stages or stage ranges to reflect heterogeneous field conditions and transitional crop states. A pretrained ResNet18 model was adapted to the task using transfer learning. Training was conducted in two stages: first, the classification head was optimized while the backbone remained frozen; second, the entire network was fine-tuned. The model achieved strong internal test accuracy across all crops, with 100% test accuracy for rapeseed and barley, more than 99% for the remaining crops, and a mean accuracy of 99.73% under the studied survey conditions. The results also compare favorably with previously reported studies on UAV-based phenological classification. Overall, the findings support the potential of low-altitude UAV imagery and deep learning for localized phenological assessment of selected field zones in precision agriculture, while broader deployment requires validation across independent fields, seasons, regions, and survey conditions.

Drones doi: 10.3390/drones10080628

Authors: Tianyu Yang Mingyang Zhai Qisheng Wen Yifei Ma Shaoshuai Zhi Shuangfeng Wei

Single-UAV dense visual SLAM is often limited by long trajectory accumulation, incomplete local observations, redundant map growth, and onboard computation constraints. Collaborative mapping can distribute a large mission across several local submaps, but dense 3D Gaussian Splatting (3DGS) maps are expensive to exchange and individual Gaussian primitives are not reliable cross-agent matching units. This paper proposes G3M-SLAM, an anchor-guided Gaussian memory framework for UAV-oriented dense SLAM with representation-level submap fusion. Stable geometric anchors organize local Gaussian primitives and form a compact structural interface for submap exchange, overlap recognition, and correction. A hybrid feature-render tracking strategy combines sparse geometric constraints with Gaussian rendering residuals. A generative completion module predicts candidate Gaussians in weakly observed regions, while multi-view geometric verification and an evidence-aware lifecycle mechanism reject unsupported candidates and control redundant map growth. A dual-graph loop bundle adjustment couples the camera pose graph and the anchor memory graph so that corrections can be propagated to anchor-associated Gaussian structures. Experiments on Replica, ScanNet, TUM RGB-D, and EuRoC MAV evaluate local tracking, dense rendering, runtime, and a split-agent fusion protocol. In the latter protocol, fusion reduces ATE from 0.045 m to 0.031 m, translational RPE from 0.030 m to 0.017 m, and rotational RPE from 1.56° to 0.93°. The serialized anchor packet is 0.66 MB, approximately 101.2× smaller than the complete Gaussian submap. On Jetson AGX Orin, the full system processes EuRoC V101 and V103 at 1.49 FPS and 1.42 FPS, respectively, while ATE increases by only 0.001 m relative to the RTX 3090 Ti workstation results. These gains represent recovery from split-agent degradation and compact representation exchange rather than an improvement over full-sequence single-agent processing. The present study therefore evaluates a representation-level fusion interface and does not reproduce the full conditions of a field-deployed decentralized multi-UAV system.

Drones doi: 10.3390/drones10080627

Authors: Chaofeng Wang Shengming Dai Jie Luo Longfei Zhang

UAV fleet configuration for truck–drone collaborative delivery requires deployment-oriented drone specification choice under wind. In this study, a UAV configuration is defined as a specific hardware parameter bundle comprising cruise speed, battery-block count, and associated mass and energy specifications. Nominal cruise speed or battery-block count alone do not predict synchronized system performance. Nine UAV configurations are compared under a common experimental protocol and the same heuristic on 60 mFSTSP instances (10, 25, and 50 customers) and nine wind scenarios, using paired makespan gaps relative to a baseline of six battery blocks and a 20 m/s cruise speed (Configuration 5). Wind affects drone outcomes through flight time and through expansion or contraction of the wind-feasible sortie set, which reshapes customer allocation and truck–drone synchronization. UAV performance is governed by a time–energy balance rather than any single nominal attribute, and no universally best drone configuration emerges. Balanced high-support UAV configurations are most robust at medium and large customer scales, though rankings remain scenario-dependent. Validation using real-world operational data under three wind conditions confirms the simulation findings, with model predictions achieving acceptable accuracy (3–6% deviation for makespan and energy consumption) and demonstrating wind-aware configuration superiority and practical applicability. The evidence supports predeployment drone fleet screening by expected wind exposure and service scale for effective drone deployment planning.

Drones doi: 10.3390/drones10080626

Authors: Jinting Ye Jie Lang Kefei Liao Ningbo Xie Jiansheng Huang Ranjun Yang Xiansui Wei Ming Li

The growing frequency of unauthorized UAV activities has increased the demand for real-time perception and rapid response on resource-constrained edge devices. This study proposes an edge-deployable UAV detection and net-capture system based on Net-Capture Detection YOLO (NCDet-YOLO). Developed from YOLOv8n, NCDet-YOLO incorporates C2f_Faster, SPD_Conv, EMA, and a lightweight three-scale detection head, with CrossKD used to compensate for accuracy loss caused by structural compression. The dataset contains 6615 images and was divided into 5292 training and 1323 validation images. The self-collected data include DJI Phantom 4 and DJI Inspire 2 UAVs observed at approximately 4–30 m under different daytime backgrounds. NCDet-YOLO achieves an mAP50–95 of 0.6504 with 1.55 M parameters and 4.1 GFLOPs. On a Jetson Orin NX Super under the 15 W power mode, it achieves 31.53 FPS, representing a 31.67% increase over YOLOv8n. The detector is further integrated with target alignment, distance determination, trigger control, and net-capture execution. In 10 real-platform trials, 8 captures were successful, corresponding to an 80.0% success rate, with one false-trigger event and an end-to-end latency from target detection to net-capture firing of approximately 400 ms.

Drones doi: 10.3390/drones10080625

Authors: Aliesha Hvala Anindilyakwa Rangers Hamish A. Campbell

Abandoned, lost, or otherwise discarded fishing gear (ALDFG) is a persistent form of marine pollution requiring survey approaches capable of resolving individual items across large spatial extents. While uncrewed aerial vehicles (UAVs) can capture imagery at resolutions sufficient to resolve individual debris items, their use remains largely constrained to visual line-of-sight (VLOS) operations, limiting large-scale coastal monitoring. This case study develops and field-tests an operational framework for low-altitude beyond visual line-of-sight (BVLOS) UAV surveys, in which DEM-based communication viewshed modelling incorporating first Fresnel zone clearance is used to plan BVLOS missions. A lightweight fixed-wing UAV flown at 60 m AGL completed 20 missions across 210 km of remote northern Australian coastline. Communication viewshed modelling reliably guided mission planning with 90.5% of waypoints placed within predicted high-clearance zones maintaining moderate-to-strong command-and-control (C2) link quality in flight. Manual screening confirmed that the resulting imagery was of sufficient quality, with 291 derelict fishing nets detected. In a simulated VLOS operational scenario, 76.3% of these detections fell beyond VLOS range, and equivalent coverage would require an estimated 8.8-fold increase in mission count. These findings demonstrate that fixed-wing UAVs operating under low-altitude BVLOS conditions can support large-scale image acquisition in remote coastal areas, particularly when enabled by communication-aware mission planning.

Drones doi: 10.3390/drones10080624

Authors: Mingxi Chen Cheng Guo Shaojie Ma Bingting Zha Zhen Zheng

Detecting small unmanned aerial vehicles (UAVs) in ground-to-air imagery is challenging because their weak visual cues must be preserved without imposing excessive computation on resource-constrained platforms. To address the unresolved trade-off between tiny-target representation and deployment efficiency, we propose a Lightweight Feature-Fusion and Small-Target Enhancement Network (LFE-YOLO), a lightweight detector that coordinates partial-channel feature extraction, efficient cross-scale fusion, high-resolution prediction, background-interference suppression, and stable tiny-box regression within a unified architecture. Specifically, C2fFaster and GSConv reduce redundant computation while maintaining multi-scale feature propagation; a P2 high-resolution detection branch and Efficient Multi-scale Attention preserve fine spatial cues and suppress background interference; and Normalized Wasserstein Distance complements Complete Intersection over Union to improve tiny-box localization stability. We also construct Det-UAV by integrating newly collected multi-platform and multi-scene UAV imagery with existing data using scene- and sequence-independent partitioning and duplicate control. Experiments on Det-UAV show that LFE-YOLO improves detection accuracy while reducing parameters and computation relative to YOLOv8s. Zero-shot evaluation on the public DUT Anti-UAV dataset further indicates favorable transferability to an unseen data distribution. TensorRT 8.2.1 deployment experiments on NVIDIA Jetson TX2 show that, under the same evaluation settings, LFE-YOLO achieves higher detection accuracy and inference throughput, lower latency, and a smaller engine size than the comparable-scale YOLOv8s and YOLO11s models. These results support a practical accuracy–efficiency balance for small-UAV detection under constrained resources.

Drones doi: 10.3390/drones10080623

Authors: Zihao Wang Ying Ma Ziming Liu Hailong Yan Qiaoyu Zhang Meng Zhang

We propose a high-quality and computationally efficient trajectory replanning method for Unmanned Aerial Vehicle (UAV) swarms, termed RHCR-Opt, which is designed to continuously and efficiently generate multiple collision-free trajectories in dense obstacle environments. RHCR-Opt consists of three layers. The first two layers are the rolling-horizon collision resolution (RHCR) algorithm based on the Conflict-Based Search (CBS), while the third layer focuses on trajectory generation and optimization using Minimum Control (MINCO) trajectories. Within the two-layer RHCR framework, the improved Lifelong Planning A* (LPA*) algorithm incorporating spatiotemporal constraints is proposed and employed as the low-level solver of CBS to satisfy the frequent search requirements for feasible paths under varying spatiotemporal constraints, thereby significantly improving computational efficiency. Furthermore, the rolling-horizon collision resolution concept is adopted in the high-level CBS framework, where only the discovery of collision-free paths within a finite time window is considered. This substantially reduces the computational burden associated with trajectory generation and optimization beyond the time window. At the third layer, a MINCO-based trajectory generation scheme is designed, and a swarm trajectory joint optimization framework with a finite time window is proposed to generate dynamically feasible and collision-free trajectories. In addition, for swarm missions requiring simultaneous arrival, a two-stage temporal coordination optimization method is developed. Extensive simulation experiments demonstrate that, compared with state-of-the-art (SOTA) algorithms on the proposed benchmark, RHCR-Opt achieves significant improvements in both trajectory quality and computational efficiency. In particular, when the swarm size becomes large, the computational efficiency is improved by at least 23.7%.

Drones doi: 10.3390/drones10080622

Authors: Yuhao Gong Jinfu Lin Jiaqiang Zhang Han Wang

Fixed-wing UAVs operating in long-range missions often fly through airspace subject to heterogeneous multi-source constraints that vertically compress the flyable space into a constraint corridor of continuously varying thickness. Conventional path planning methods incur high online computational costs in such scenarios. Deep reinforcement learning can generate reactive decisions from local observations, yet existing approaches predominantly target multirotor obstacle avoidance and rely on observations designed for discrete obstacles, lacking a unified representation for corridor constraints. Moreover, constraint conditions vary across mission scenarios, demanding cross-scenario policy generalization. This paper proposes the Egocentric Constraint Corridor (ECC), which fuses multi-source constraints into upper and lower boundary surfaces defining the corridor, then egocentrically encodes the surrounding corridor relative to the vehicle into a margin field serving as structured policy input. A deep reinforcement learning framework built on ECC is trained end-to-end, with its multi-branch network and composite reward function following from the structure of the corridor encoding. Experiments show that ECC-DRL achieves path efficiency approaching that of globally informed A*, and that it is the only one of the compared methods that computes its decisions online within the decision interval. Ablation studies confirm the margin field is necessary for reliable navigation, and the ECC encoding enables zero-shot transfer to scenarios with unseen terrains and radar deployments without retraining. Hardware-in-the-loop experiments on an embedded platform verify real-time closed-loop feasibility.

Drones doi: 10.3390/drones10080621

Authors: Heungseob Kim Seunghyeon Yu Byoungho Choi

Underwater vehicles must operate efficiently within the limits of their onboard resources, and travel time is a primary operational objective for extending submerged endurance and range. Ocean currents significantly affect vehicle motion and travel time, either aiding or impeding propulsion depending on their direction and magnitude. This study proposes a cell-based, ocean-current-aware cost formulation for offline three-dimensional (3D) underwater path planning. A 3D grid map integrating ocean current vectors and underwater terrain is constructed, and rather than modifying a specific search algorithm, the proposed approach defines a travel-time-based cost at the cell-transition level by incorporating current effects into the vehicle’s effective velocity. Because the cost operates at the cell-transition level, it can be adopted by standard grid-search planners such as Dijkstra’s, A*, and D* without modification. Simulation experiments over the Tsushima–Jeju corridor using two measured current fields from the Korea Hydrographic and Oceanographic Agency show that identical fields aided westbound transits (−2.4% and −4.4% travel time versus a current-unaware baseline) while opposing eastbound transits (+1.3% and +1.8%), with the planner exploiting favorable flows and detouring to mitigate adverse flows; these effects amplified roughly threefold as the vehicle speed decreased from 15 to 6 knots. A* and Dijkstra’s algorithms returned identical optimal costs under the same formulation, confirming planner independence. The results demonstrate that embedding measured current information at the map level yields realistic, condition-dependent, minimum-travel-time routes for offline mission planning.

Drones doi: 10.3390/drones10080620

Authors: Seokwon Yeom

Drones, or unmanned aerial vehicles (UAVs), are widely utilized in a variety of fields [...]

Drones doi: 10.3390/drones10080619

Authors: Xin He Yizhan Ju Yaqing Chen Lingxiao Xue Yumei Zhang

With the increasing scale and density of low-altitude unmanned aerial vehicle (UAV) operations, safety separation between multirotor UAVs has become a critical parameter for low-altitude airspace management. Existing studies mainly consider aircraft geometry, navigation errors, trajectory deviations, and conventional collision risk models, while rotor-downwash-induced aerodynamic interference remains insufficiently addressed. This study proposes a safety separation assessment method for quadrotor UAVs by integrating computational fluid dynamics (CFD) with an improved Event collision model. A small-scale quadrotor UAV is analyzed, and its rotor downwash flow fields under vertical- and horizontal-motion conditions are simulated using the multiple reference frame method. Based on a 5 m/s crosswind-resistance capability threshold, aerodynamic-interference characteristic distances are extracted and used to construct a basic collision box. To better represent the actual aerodynamic hazard region, an I-shaped improved collision box is further developed and incorporated into the Event collision model. Under a target level of safety, the longitudinal, lateral, and vertical minimum safety separations are determined as 1.68 m, 1.72 m, and 1.08 m, respectively. The results show that the proposed CFD–Event coupled method can transform rotor downwash characteristics into collision risk parameters and provide a quantitative basis for safety separation assessment in dense low-altitude multirotor UAV operations.

Drones doi: 10.3390/drones10080618

Authors: Mingxuan Li Liang Xu Shuo Wang Yu Han Huayong Xu Juyong Zhang

Urban unmanned aerial vehicle (UAV) navigation may require path planning that accounts for geometric obstacles and spatially varying communication-related risk. This paper presents a transformer-guided interference-aware 3D path-planning method for urban voxel environments. A 3D convolutional neural network (CNN)–transformer network predicts a dense route probability field from occupancy, electromagnetic risk, start–goal, and auxiliary planning channels. The field is restored to the raw-map resolution and used only as a search prior for A* on the original occupancy and risk maps. Obstacle avoidance, endpoint correctness, 6-connected motion (each move reaches one of six face-adjacent voxels, with no diagonal motion), and final path cost evaluation are enforced by graph search rather than by the neural model. On 320 synthetic urban cases covering four map sizes and four building density settings, Guided A* achieves a 27.7× speedup over A* and an 11.9× speedup over Weighted A*, while reducing expanded nodes by 91.2% relative to A*. The mean path cost and electromagnetic cost increase by 2.7% and 5.7%, respectively. Compared with the rapidly exploring random tree (RRT), the method reduces path cost by 10.1% and electromagnetic exposure by 12.0% at similar runtime. A post-training sensitivity study further identifies an empirical balance between route-prior guidance, electromagnetic risk avoidance, route length, and search effort, while the Manhattan weight exhibits the expected heuristic inflation efficiency–quality trade-off. An extended model trained on a larger mixture of procedural and Sionna RT ray-traced data, including real OpenStreetMap building geometry, is further evaluated without retraining on two real-geometry benchmarks, UrbanRadio3D and an OpenStreetMap–Sionna RT suite, where Guided A* retains a 100% success rate and reduces expanded nodes by 97–99% relative to A* while increasing mean path cost by at most 1.7%.

Drones doi: 10.3390/drones10080617

Authors: Yuhua Cong Xian Zhu Zhisheng Wang Yujia Li

Hierarchical multi-UAV planning can produce scheduled paths that become unsafe during execution because tracking dynamics, actuator limits, sampling, and communication are not fully represented upstream. We introduce a certificate-guided safe-tracking framework that treats each planned path as a versioned execution contract with explicit tube, separation, timing, uncertainty, communication, and input bounds. A rigid-body-derived translational interface supports a command-producing control Lyapunov function–control barrier function quadratic program with hard safety constraints. When a candidate becomes infeasible, a separate minimum-slack program localizes the conflict without passing its command to the plant, and an explicit repair map converts the resulting witness into timing or vertical-spacing updates, with escalation when local repair fails. Randomized comparisons show that certificate feedback removes the observed tube and separation failures of a plain CBF-QP while maintaining reliable completion and competitive tracking relative to a tracking-error-bound comparator. Disturbance and sensor-noise sweeps characterize robustness, and separate indoor flights confirm single-reference trackability. The framework therefore turns execution infeasibility into actionable planning feedback rather than a terminal controller failure.

Drones doi: 10.3390/drones10080616

Authors: Qi Li Tad Gonsalves

The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two classical combinatorial optimization problems. In recent years, their drone-assisted variants, the Traveling Salesman Problem with Drones (TSP-D) and the Vehicle Routing Problem with Drones (VRP-D) have attracted growing attention. Generally, these problems are solved using exact algorithms or metaheuristic algorithms. However, as the problem complexity increases and the scale of instances grows, these approaches often become less efficient. In this paper, we propose a reinforcement learning method with a shared attention encoder and a hierarchical dual-decoder architecture, where truck–drone coordination is achieved by first decoding the truck’s next node and then conditionally decoding the drone action. To further explore the solution space of large-scale instances, the proposed method adopts a multi-rollout learning strategy. We conducted experiments on large-scale TSP-D and VRP-D instances, and the results show that this model outperforms traditional metaheuristic algorithms in terms of both solution quality and computational efficiency.

Drones doi: 10.3390/drones10080615

Authors: Huiyu Wu Jie Liu Yazhou Wang Yimin Chen Jian Gao

Thruster health monitoring is essential for unmanned underwater vehicles (UUVs), where propulsion degradation can reduce manoeuvrability, tracking accuracy, and mission safety. Practical diagnosis remains difficult because labelled vibration samples are scarce and deployed vehicles may encounter fault states absent from the support library. Closed-set few-shot classifiers are unreliable in this setting because every query must be assigned to a known state. SAGE-PD (Spectral Attention-Guided Episodic Prototype Displacement) is a few-shot open-set method for UUV thruster vibration monitoring. It encodes each vibration window through raw-waveform and STFT branches, constructs episode-specific prototypes for known thruster states, and models their relational geometry. Unknown-state evidence is obtained by replacing the predicted prototype with the query embedding and measuring the displacement of the transformed prototype structure. Spectral attention weights this displacement toward thruster-related time–frequency components. On the analysed UUV thruster dataset, SAGE-PD achieved 0.9078±0.0573 Open OA and 0.9011±0.0407 AUROC in a 5-shot evaluation, and 0.8745±0.0457 Open OA and 0.8953±0.0409 AUROC in a 1-shot evaluation. The results show that SAGE-PD improves both known-state recognition and unknown-state rejection by combining support-structure compatibility with vibration-aware spectral evidence.

Drones doi: 10.3390/drones10080614

Authors: Weijian Jia Fenghua Wang Haiwen Zheng Penglei Hu Xiaobing Wang Yao Zhao Pengdong Zhang Yufei Gao

To address the susceptibility of single-modality approaches to illumination variations and imaging conditions in low-altitude unmanned aerial vehicle (UAV) detection, this study proposes a structure-aware heterogeneous dual-stream detection network based on infrared and visible-light fusion. First, a multimodal UAV detection dataset oriented toward complex low-altitude scenarios is constructed, providing a data foundation for cross-modal detection research. Then, a structure-aware heterogeneous dual-stream feature extraction framework is designed to enable collaborative modeling of visible-light and infrared features through modality-specific encoding. In the visible-light branch, a Structure-Aware Gated Enhancement Block (SAGE Block) is introduced to enhance the representation of fine-grained structural and edge information. In the cross-modal fusion stage, a Bidirectional Wavelet-Guided Fusion Module (BWFM) is proposed to decouple structural semantics and detailed information in the frequency domain. Adaptive fusion is further achieved through low-frequency cross-modal interaction and high-frequency detail-preservation strategies. Finally, the proposed method is experimentally validated on the proposed Multispectral UAV Detection Dataset (MUDD) and the Multi-scenario Multi-Modality Fusion Dataset (M3FD).. The experimental results show that the proposed method achieves an mAP@0.5 of 0.9680 and an mAP@0.5:0.95 of 0.6794 on the proposed MUDD, as well as an mAP@0.5:0.95 of 0.6072 on the M3FD dataset, demonstrating competitive detection accuracy and generalization capability. Ablation experiments further indicate that the SAGE Block, BWFM, and the low-frequency cross-modal fusion and high-frequency detail-preservation strategies within BWFM all contribute positively to performance improvement.

Drones doi: 10.3390/drones10080613

Authors: Xin Fan Hongxing Zheng Zhaoyang Wang

In maritime safety regulation, conventional ballast water inspection faces critical time-window violations due to manual sampling delays across dispersed inspection points. While drone-based inspection significantly enhances multi-point accessibility and timeliness, its operational viability is severely constrained by limited battery endurance during multi-vessel missions. To address this challenge, we propose a novel partial charging strategy enabling adaptive energy replenishment. This strategy is formalized in a model incorporating dual energy consumption (propulsion and hovering) and spatiotemporal constraints for sampling operations. An adaptive metaheuristic algorithm combining dynamic programming with large neighborhood search is developed for efficient resolution. Computational experiments demonstrate that partial charging strategy reduces drone energy consumption by 25% compared to the 70%-threshold charging strategy. This study provides maritime authorities with a quantifiable configuration decision tool to optimize drone fleets and flight parameters, balancing regulatory compliance with operational costs.

Drones doi: 10.3390/drones10080612

Authors: Chenrui Bai Ya Zhang Zehui Yuan Shuwen Zhao Chenghao Yang

Aiming at the problems of traditional Rapidly exploring Random Tree Star (RRT*), its modified algorithms in the three-dimensional complex underwater path planning of autonomous underwater vehicles (AUVs), including sampling redundancy, simplistic expansion mechanism, mismatch between planned paths and AUV motion constraints, and insufficient real-time performance, this paper proposes an improved bidirectional artificial potential field RRT* (Improved BI-APF-RRT*) algorithm. The algorithm adopts a hybrid sampling strategy to concentrate sampling in high-value regions and optimize node distribution. A three-level progressive expansion strategy is designed to balance fast convergence and excellent obstacle avoidance capability. Meanwhile, a dynamic target switching strategy is introduced to enhance the coordination efficiency of bidirectional search trees. Simulation results show that in three-dimensional underwater obstacle environments with different complexity levels, the proposed algorithm outperforms comparative algorithms such as GB-RRT*, BI-RRT*, and APF-RRT* in terms of path length, planning time, number of generated nodes, and iteration times. The proposed algorithm provides an efficient and feasible technical scheme for the deep-sea autonomous navigation of AUVs, and is of great significance for promoting the engineering application of underwater unmanned systems.

Drones doi: 10.3390/drones10080611

Authors: Ziyi Qiu Defu Lin Bo Liu Hui Han Wen Guo Jianjian Liang Zhaojiang Chen Ziheng Yan Haolong Wang Xinghao Yang Zelin Liu Liuhang Zhao

To address the problem of lacking effective evaluation benchmarks for UAV navigation algorithms in complex-structured, GNSS-denied industrial environments (e.g., fully enclosed stockyards), this paper proposes and open-sources a multi-modal UAV navigation dataset. The dataset is collected in a real steel plant enclosed stockyard, integrating LiDAR point clouds, IMU, RGB images, and high-precision total station ground truth trajectories, and specially designs ArUco markers to aid visual localization. Different from existing datasets targeting urban or campus scenes, this dataset realistically reflects the challenges of GNSS-denied signal, weak texture, high dust, and complex spatial grid structures in industrial environments. Through the evaluation of various mainstream LiDAR odometry and fusion navigation algorithms, the difficulties encountered by existing methods in this scenario are highlighted, and the potential of the proposed dataset as a valuable benchmark for developing and quantitatively testing highly robust navigation algorithms is suggested.

Drones doi: 10.3390/drones10080610

Authors: Jingjing Qian Yang Cheng Junhong Wu Bing Liu Wei Dai

Autonomous UAV landing in urban scenes requires a perception module that can identify candidate landing surfaces, reject structural and dynamic hazards, and express uncertainty before a downstream controller commits to a landing maneuver. This study reformulates UAV landing perception as a unified three-class landing-safety segmentation problem by relabeling UAVid, UDD6, and VDD into candidate landing area, structural obstacle, and critical hazard classes. The Landing3 dataset is constructed with an implementation-consistent relabeling protocol in which critical hazards override other labels and only sufficiently large connected components of source-specific candidate classes are retained as landing candidates. Two real-time segmentation models are evaluated under this unified task: PIDNet, a CNN-based multi-branch model with explicit boundary modeling, and SCTNet, a Transformer-guided model with semantic alignment for long-range context modeling. A post hoc conformal prediction (CP) module then converts softmax outputs into pixel-wise prediction sets, and the final candidate landing region is extracted only from pixels whose prediction set is the singleton candidate-landing class. Experiments compare the two models in terms of best validation mIoU, class-wise IoU, resolution-dependent accuracy–speed trade-offs, power-constrained FPS and complexity, qualitative candidate-area visualization, and CP-derived safe-area quality. A fixed-checkpoint sensitivity analysis further shows that the comparative model ranking remains stable across the tested connected-component threshold settings. SCTNet provides a lighter model and higher throughput under most tested resolutions and power limits, whereas PIDNet preserves higher safe-area recall, safe IoU, and spatial coherence after CP filtering. These results show that reliable UAV landing perception requires joint consideration of cross-dataset task definition, real-time model efficiency, and uncertainty-aware candidate-area extraction, rather than semantic segmentation accuracy alone.

Drones doi: 10.3390/drones10080609

Authors: Cuauhtemoc Acosta Lúa Bernardino Castillo-Toledo Stefano Di Gennaro Ulises Larios

This paper addresses the problem of autonomous landing of a quadrotor unmanned aerial vehicle (UAV) on a moving ground vehicle subjected to unknown vertical oscillations generated by road irregularities. The proposed approach considers simultaneous longitudinal, lateral, heading, and altitude regulation in the presence of nonlinear coupled dynamics, aerodynamic effects, and platform motion disturbances. A nonlinear control architecture is developed by combining backstepping techniques, super-twisting sliding-mode control, and an adaptive internal model regulator. The longitudinal, lateral, and heading subsystems are stabilized through a block backstepping–sliding-mode framework, whereas the altitude subsystem is regulated using an adaptive internal model controller capable of compensating unknown multi-frequency oscillatory disturbances without prior knowledge of their amplitudes or frequencies. The complete UAV dynamics are derived from the Newton–Euler formulation, including aerodynamic forces, gyroscopic effects, and coupled translational–rotational dynamics. To improve robustness and avoid algebraic differentiation, exact first-order differentiators based on the super-twisting algorithm are incorporated into the control implementation. The proposed adaptive regulator is compared against a robust super-twisting sliding-mode altitude controller under low- and high-frequency oscillatory platform motions. Simulation results demonstrate that the adaptive internal model regulator achieves accurate trajectory tracking and consistently lower accumulated tracking errors than the robust super-twisting sliding-mode controller under both low- and high-frequency platform oscillations. These results highlight the suitability of adaptive output regulation techniques for autonomous UAV landing operations under oscillatory platform conditions with measurement noise.

Drones doi: 10.3390/drones10080608

Authors: Zishuang Pan Dazhao Yu Wei Han Xichao Su Jie Wang Shansong Song Bing Wan

Carrier-based fixed-wing UAV landing is challenged by deck motion, carrier airwake, gust disturbances, strong trajectory–attitude coupling, and actuator constraints. To address these issues, this paper proposes a Predefined-Time Direct Lift/Side-Force-Integrated Approach Landing (PTDIAL) method. An integrated direct-force architecture is constructed using the trailing-edge flap for direct lift and the spoiler for direct side force, thereby reducing the dependence of trajectory correction on angle-of-attack- and bank-angle/sideslip-mediated regulation. A preview-based reference glide slope is generated from the predicted Ideal Touch Point (ITP) sequence to improve the response to deck motion. Predefined-time control laws are developed for the cascaded position, trajectory, attitude, angular rate, and velocity loops, with prescribed-performance constraints imposed on the attitude response. A predefined-time disturbance observer is introduced to estimate the lumped aerodynamic disturbances, while an auxiliary anti-saturation mechanism compensates for the effect of trailing-edge flap saturation. Lyapunov analysis establishes the practical predefined-time stability of the closed-loop system under bounded disturbances and actuator constraints. Various simulations demonstrate that the proposed architecture improves lateral and vertical tracking while preserving the UAV attitude, and Monte Carlo simulations further confirm the robustness of PTDIAL.

Drones doi: 10.3390/drones10080607

Authors: Ahmed Alamouri Cosima Berger Mohammad Shafi Bajauri Konstantin Wenzlaff

Risk assessment of Unmanned Aerial Vehicle (UAV) path planning is a crucial step towards ensuring a safe UAV operation. However, achieving a reasonable risk assessment of UAV flight paths remains challenging because it involves multiple responsibilities and processes that extend beyond a single agency or organization. Additionally, it must balance various complex factors and data from social, technical, political, and economic sources. Most existing works on flight path planning evaluate flight risks at a global level and generalized geometric representations of the UAV operating environment with respect to the current applicable UAV regulations. However, geometric information alone does not provide sufficient insight for comprehensive and safe path planning. Therefore, there are other ideas and concepts for using semantic data to characterize objects, obstacles and actions in the UAV environment. Incorporating semantic information into path planning enables more meaningful scene descriptions and a better representation of relevant constraints, obstacles within UAV environment that may influence the safety level of UAV operation, and the relevant risk assessment process. In this paper, we propose the development of methods and frameworks integrated into a flight planning prototype designed to generate safe two-dimensional UAV routes within a local, fine-grained planning context. The prototype incorporates safety considerations to enable a comprehensive assessment of UAV operational risks. It leverages both geometric and semantic datasets to characterize objects and obstacles within the UAV environment. These datasets are processed and stored in a relational database to support structured access and long-term usability. All concepts and experiments were implemented using datasets from a study area in the city of Brunswick, Germany.

Drones doi: 10.3390/drones10080606

Authors: Xujun Guan Junwu Luo Hai Zhang

In multi-UAV airborne radar sensing, target-level fusion cannot start until detections reported in different UAV coordinate frames have been associated. This paper focuses on a single-epoch case: two UAV-mounted radars observe a common target group, but the reported point sets are sparse, unordered, only partially overlapping, and corrupted by angular noise, missed detections, and additional observations. In this setting, appearance cues, long tracks, and dense point-cloud neighborhoods are absent, so the association problem is mainly a sparse geometric registration problem. We propose a Triangle Attribute-Guided Robust Association (TARA) framework. TARA describes each target triple by its absolute scale and two normalized side-length ratios. These attributes are not used as final identity labels. Instead, they retrieve, verify, and rank plausible cross-view triangle pairs, from which a fixed number of rigid-transformation hypotheses are estimated. The best coarse transformation is then refined by the trimmed ICP, and the final correspondences are obtained by gated one-to-one matching in the aligned frame. The ablation results show why the attribute-guided step matters. With only 100 evaluated hypotheses, TARA reaches 93.7% F1 in the medium setting, while blind RANSAC needs about 10,000 random hypotheses to reach comparable accuracy. In composite degradation tests, TARA obtains mean F1-scores of 98.7%, 94.2%, 69.3%, and 69.1% from low to extreme conditions. The advantage is clearest when clutter, missing detections, and angular uncertainty appear together because the triangle attributes make the limited hypothesis budget much less likely to be spent on geometrically implausible triples.

Drones doi: 10.3390/drones10080605

Authors: Xi You Wenjun Yi

Urban low-altitude unmanned aerial vehicle (UAV) navigation combines partial observability, building occlusion, wind disturbance, and continuous control. This study develops and evaluates HW-GA-SAC, a multimodal history-window Soft Actor-Critic (SAC) policy for procedurally generated three-dimensional MuJoCo cities. A Gated Transformer-XL (GTrXL)-inspired gated-attention encoder processes a fixed eight-step navigation history, while a current-frame safety branch supplies vertical clearance, sparse Light Detection and Ranging (LiDAR)-like range sectors, and handcrafted safety cues directly to the actor and critic. The policy uses obstacle-related observations and reward shaping to support collision avoidance; it does not include constrained policy optimization or a separate runtime safety filter. In a seven-method comparison using five training seeds and five evaluation layouts, HW-GA-SAC achieved a 96% ± 3% success rate, 207 ± 16 average return, and 3% ± 4% timeout rate. Feedforward SAC achieved 92% ± 11% success and a 7% ± 10% timeout rate, but its successful paths were more direct. Five-seed learning curves, city-split evaluation, wind sensitivity, sensing perturbations, inference profiling, and ablation studies further characterize the method. Within this simulation protocol, HW-GA-SAC provides the strongest completion-oriented performance, with a measurable trade-off between task completion and path directness.

Drones doi: 10.3390/drones10080604

Authors: Shang-En Tsai Chia-Han Hsieh Wei-Cheng Sun Sin-Dao Shen

This paper presents a distributed counter-UAV early-warning and response-decision support framework for low-altitude UAV defense. Acoustic–visual edge nodes generate local state packets and exchange compact information-filter parameters through Multi-Target Information Consensus (MTIC), avoiding centralized fusion and reducing payload bandwidth. The MTIC naïvety-handling mechanism is extended to heterogeneous acoustic–visual sensing, improving robustness to partial observations, packet loss, and node disconnection. A Fuzzy–Bayesian threat-assessment layer converts fused distance, velocity, and heading cues into interpretable response recommendations with calibrated confidence. Implemented on ROS 2/Fast DDS with tiered QoS, software-assisted IEEE 1588 synchronization, and Preempt-RT scheduling, the framework achieves within about 5% of centralized accuracy while reducing payload bandwidth by up to about 97% relative to the main centralized baseline. Simulation and hardware-in-the-loop tests on three- and five-node mesh topologies show software-assisted sub-millisecond synchronization (200–500 μs offset), bounded latency, gradual AUC degradation under association mismatch, and end-to-end feasibility under controlled packet loss. Overall, the system provides a resilient, deployment-oriented architecture for distributed C-UAV early warning.

Drones doi: 10.3390/drones10080603

Authors: Hongbin Liu Liang Zeng Mengyuan Lu Ke Tang Bo Li Xinping Zhu

Urban low-altitude UAV missions require efficient, risk-aware path planning and rapid response to dynamic airspace changes. This study proposes a hierarchical planning and dynamic replanning framework based on the Geographic coordinate Subdivision grid with One-dimensional integer coding on a 2n-Tree (GeoSOT) and height-layer encoding (GeoSOT-H). The framework constructs a multi-granularity 3D semantic-risk voxel model and uses semantic-triggered refinement to limit fine-resolution modeling to flight-relevant high-risk regions. The Hierarchical Semantic-risk-aware Path Planning with Corridor-constrained A* (HSPC-A*) algorithm generates a macro-corridor and conducts fine-level search to balance path length, semantic-risk exposure, and vertical maneuvering cost while satisfying no-fly constraints. Its output is a connected L24-H voxel-center path for subsequent navigation or post-processing. Experiments in a 2.89 km2 urban area show that explicit storage is reduced to 16.4% of full-domain L24-H voxels. Compared with conventional 3D A*, HSPC-A* slightly increases path length from 2183.06 m to 2202.38 m, while reducing average semantic risk from 8.5927 to 5.2595, eliminating high-risk samples, and reducing search time from 164.09 s to 3.74 s. Code-based updating achieved a 104.6-fold speedup, and two-branch replanning handled both corridor-retained and corridor-disconnecting no-fly events, jointly demonstrating the trade-offs among path length, semantic-risk exposure, computational efficiency, and compliance with modeled flight-safety constraints.

Drones doi: 10.3390/drones10080602

Authors: Emmanouel T. Michailidis Irene S. Karanasiou

Unmanned aerial vehicles have become indispensable components of modern defense systems by conducting Intelligence, Surveillance, and Reconnaissance (ISR) missions to collect critical operational information through onboard sensing technologies, supporting secure communication and information sharing among distributed military assets, and enhancing battlefield situational awareness through real-time sensing and data fusion. In addition, UAVs are increasingly capable of executing a wide range of defense missions, including target search and tracking, electronic warfare, search-and-rescue, and combat support. The integration of AI into UAV-based systems has the potential to enhance these operational capabilities by enabling intelligent perception, autonomous decision-making, adaptive mission planning, autonomous navigation, resilient communications, and cooperative multi-UAV coordination, thereby enabling the autonomous and collaborative execution of complex defense missions. This paper presents an up-to-date review of AI-enabled UAV-based defense systems, focusing on major operational domains including autonomous air combat and cooperative UAV operations, path planning and autonomous navigation, target tracking/detection/classification, cybersecurity, electronic warfare protection, and resilient UAV operation. In addition to surveying the recent literature, this paper provides an integrated system architecture, a functional classification framework, and an analysis of the AI paradigms enabling next-generation UAV-based defense systems. Furthermore, this review synthesizes the key technological trends, lessons learned, and cross-domain research challenges identified across the reviewed studies, providing a unified perspective on the current state of the field. Finally, this paper highlights promising future research directions for resilient, scalable, secure, and intelligent next-generation AI-enabled UAV-based defense systems.

Drones doi: 10.3390/drones10080601

Authors: Jianfeng Wang Jun Li Yuze Liu Cheng Wang Chen Bu Shuai Feng Mingying Huo

Low-aspect-ratio flying-wing unmanned aerial vehicles (UAVs) are attractive drone platforms for civilian remote sensing, environmental monitoring, infrastructure inspection, disaster assessment, and persistent public-service monitoring because their integrated tailless layout offers high aerodynamic efficiency and payload volume. A trajectory-command-based three-loop nonlinear dynamic inversion (NDI) control architecture enhanced by a nonlinear disturbance observer (NDO) is designed to address the critical challenges of rapid time variation, strong nonlinearity, strong coupling, and restricted yaw authority in low-aspect-ratio flying-wing UAVs. The core innovation lies in the development of a trajectory-command-to-attitude kinematic mapping mechanism, integrated with the NDO for active torque compensation of lumped uncertainties and time-varying external disturbances. Leveraging a mathematical model of a low-aspect-ratio flying-wing UAV standard model, a three-loop NDI controller comprising angular rate, attitude, and trajectory command loops was designed based on the time-scale separation principle. The NDO was further designed to estimate lumped disturbances and provide feedforward compensation, thereby establishing an NDI-DO system that mitigates the high sensitivity of conventional NDI to modeling inaccuracies. Simulation and robustness tests involving typical high-angle-of-attack maneuvers (e.g., Cobra and Split-S maneuvers) demonstrated that the NDI-DO system achieved a reduction in angular-rate tracking error by over 77.2% compared to the baseline NDI. Furthermore, the permissible range of aerodynamic parameter perturbations was improved by 23%, significantly enhancing tracking fidelity and disturbance rejection. In a 3-DOF wind tunnel free-flight test, the NDI-DO system achieved a substantial expansion of the controllable angle-of-attack (attitude-stability) envelope from 72.9° to 99.19°, substantiating the high reliability and engineering utility of the control framework in post-stall nonlinear regimes. These results indicate that the proposed NDI-DO framework can support safer envelope expansion, autonomous upset recovery, and robust flight control for civilian flying-wing drones operating under uncertain aerodynamic and environmental conditions.