Search Results (510)

In complex three-dimensional airspace, UAV trajectory planning is subject to stringent real-time constraints and must rapidly generate collision-free, near-optimal and curvature-continuous feasible flight paths within a limited computational window. Although mainstream bidirectional RRT-based algorithms improve the basic search speed through parallel dual-tree expansion, they still suffer from inherent limitations, including blind sampling, fixed expansion strategies and poor environmental adaptability. To address these limitations, this study proposes a Dynamic Hybrid Multi-strategy RRT (DHM-RRT*) algorithm. In the sampling stage, a hybrid strategy combining frontier-density adaptive sampling, Halton low-discrepancy sampling and uniform random sampling is adopted. In the expansion stage, a four-level progressive expansion mechanism is designed, comprising goal-directed expansion, dual-distance scoring tangent-cone obstacle avoidance, improved artificial-potential-field guidance and random fallback expansion. The failure rate of each strategy is estimated online using an exponential moving average, and the expansion probabilities are dynamically and adaptively assigned. After path generation, path quality is further improved through greedy direct connection near the stitching seam and B-spline smoothing. The algorithm was independently evaluated in three MATLAB three-dimensional obstacle environments and compared with the best-performing baseline algorithm in each environment. The proposed algorithm reduced the average path length by 1.78%, 0.42% and 5.49%, respectively, and reduced the planning time by 38.89%, 34.78% and 29.03%, respectively. The simulation results demonstrate that the proposed algorithm provides clear advantages in convergence speed, path length, smoothness and environmental robustness under complex obstacle constraints. Full article

This paper investigates the low-altitude penetration path planning problem for a fixed-wing UAVs in complex battlefield environments with mountainous terrain and radar threats. First, a radar detection probability model and a dynamic radar cross section (RCS) model are established to characterize target exposure risk. Then, a deep reinforcement learning-based (DRL) path planning framework is developed, in which the Soft Actor-Critic (SAC) algorithm is employed to solve the penetration task under dynamic RCS constraints. A multi-objective reward design is constructed to jointly account for target-reaching progress, radar-threat avoidance, obstacle avoidance, and terminal task completion. Simulations show dynamically feasible flight paths under the adopted models. HIL tests conducted in the corresponding five-radar and two-radar numerical scenarios support onboard path planning, waypoint transmission, and functional closed-loop execution. Comparisons reveal a trade-off among penetration time, modeled radar exposure, and computation, with SAC achieving the shortest mean penetration time among successful trials. Full article

To address slow convergence, local optimum stagnation, and multi-objective imbalance problems for unmanned aerial vehicle (UAV) three-dimensional (3D) path planning in complex obstacle environments, an improved adaptive two-stage pigeon swarm optimization (IPIO) algorithm is proposed. Firstly, a hybrid initialization strategy integrating Latin hypercube sampling and obstacle avoidance constraints is adopted to improve initial population diversity and the quality of feasible solutions. Secondly, in the map compass stage, a linearly decreasing adaptive map factor and population diversity-based dynamic perturbation strategy are introduced to balance global exploration and local exploitation while preventing premature convergence. In the landmark stage, an inverse fitness weighting elite center updating mechanism and linearly decreasing elite quantity strategy are designed to enhance the guidance of high-quality individuals and accelerate convergence. A multi-objective fitness function integrating path length, obstacle avoidance safety, and flight smoothness is constructed, whose weight coefficients (${\omega }_{\mathrm{L}}=0.3$, ${\omega }_{\mathrm{C}}=0.5$, ${\omega }_{\mathrm{S}}=0.2$) are calibrated through parameter-sensitivity analysis and Pareto frontier comparison across six representative weight combinations. Combining ablation validation for each improved module, single-UAV multi-scenario tests, and preliminary multi-UAV trials, these coordinated improvements realize targeted optimization for UAV 3D flight characteristics. Specifically, the preliminary multi-UAV trials involve three UAVs performing independent trajectory planning in shared obstacle environments without explicit inter-UAV collision avoidance constraints, and the reported improvements are based on single-UAV experiments. Finally, comparative experiments are conducted with a standard 100 × 100 × 50 m space, and varying obstacle densities are demonstrated in six diverse 3D test scenarios, where the proposed IPIO achieves an average path length reduction of 12.8% and 15.3% compared to the standard PIO and PSO, respectively. The average fitness improvement is 14.2% over PIO, 16.8% over PSO, 19.5% over GWO, 24.1% over CO, and 38.7% over CS. Key path-quality metrics include a minimum obstacle clearance of 2.37 m, average smoothness cost of 0.34, average convergence time of 0.60 s, and computational cost of O(N*D*MaxIter). Statistical tests confirm that these improvements are significant (p < 0.05) in all tested scenarios. This study presents an efficient and robust algorithm for autonomous three-dimensional path planning of UAVs in complex obstacle environments. Full article

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. Full article

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 $3.60\pm 0.18 \mathrm{s}$ and a path success rate of $95.8\pm 1.2\%$, 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. Full article

To address the inherent limitations of the traditional Aquila Optimizer (AO)—specifically slow convergence, susceptibility to local optima, and limited high-dimensional adaptability—this study proposes a multi-strategy Improved Aquila Optimizer algorithm. Key enhancements include the integration of a logarithmically decaying tangent flight factor to optimize high-dimensional solution distributions, and a dual-layer t-distribution adaptive perturbation model to dynamically regulate search density. Additionally, to solve path-planning problems under strict constraints, we incorporate a prior feasible region initialization, a continuous-to-discrete mapping correction, and a local fine-search mechanism for trajectory smoothing. The proposed Improved Aquila Optimizer algorithm is systematically evaluated against the original AO and six popular algorithms (PSO, SSA, GWO, DBO, DE, and GA) across 23 benchmark functions, the CEC2017 suite, and multi-scale grid maps. The results demonstrate that the Improved Aquila Optimizer algorithm achieves an order-of-magnitude improvement in convergence reliability. By prioritizing absolute search stability and robustness in high-dimensional tasks, the proposed algorithm attains an optimal balance between convergence quality and practical engineering efficiency, proving exceptionally effective in complex path-planning scenarios. Full article

Vineyard monitoring requires efficient methods for detecting plant stress and disease while limiting flight time, data volume, and labour effort. This paper presents a satellite-enabled two-tier UAV workflow for semi-automated vineyard inspection. The proposed approach combines high-altitude multispectral scanning, NDVI-based point-of-interest identification, adaptive flight path planning, low-altitude RGB inspection, and downstream vision-based disease analysis. Processing tasks are offloaded to a remote server accessed through an emulated Low Earth Orbit satellite communication environment, allowing the UAV-side system to remain lightweight while receiving multispectral analysis results during the mission. A simulation framework was developed to evaluate mission behaviour under controlled and repeatable conditions, using both pseudo-random point generation and real multispectral vineyard images processed through the satellite emulation testbed. A flight with a real drone was also conducted to validate adaptive flight optimisation. Experimental results focus on the impact of path-adaptation strategies and communication bandwidth on mission efficiency. The results show that route optimisation can reduce mission time by up to $15\%$ when new low-altitude waypoints emerge, while bandwidth bottlenecks affect performance once image transmission can no longer keep pace with acquisition. The findings highlight the need to consider sensing, communication, and mission planning jointly in adaptive UAV-based crop monitoring. Full article

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. Full article

Three-dimensional (3D) path planning for Unmanned Aerial Vehicles (UAVs) presents a challenging multi-objective optimization problem that necessitates a balanced trade-off among path length, flight safety, and trajectory smoothness, especially in complex environments such as mountainous or hilly terrains. Traditional and even many meta-heuristic planning algorithms often suffer from premature convergence and suboptimal solution quality when navigating such intricate 3D spaces. To address these limitations, this paper proposes an Improved Dhole Optimization Algorithm (IDOA) that exhibits fast convergence and strong global optimization capabilities. The IDOA enhances the original DOA framework by integrating a logistic-map-based chaotic mapping, a dynamic chaotic perturbation mechanism, and an adaptive stage-division strategy. The algorithm is designed to address the 3D path planning problem for quadrotor UAVs, supporting typical flight maneuvers including climb/descent, obstacle avoidance, and smooth turning in simulated complex hilly terrain. A multi-objective fitness function incorporating path length, safety, and smoothness is designed, which constrains the optimization to generate collision-free, smooth paths that satisfy the quadrotor UAV’s dynamic maneuver constraints. Convergence curves confirm that IDOA significantly outperforms the original DOA in terms of convergence speed and final path optimality. Detailed experimental results show that compared to the original DOA, IDOA achieves a 6.31% improvement in minimum fitness values, a 9.7% reduction in average path length, and a 45.28% reduction in average path curvature when compared to the baseline DOA. These consistent performance improvements demonstrate that IDOA provides an effective and robust solution for offline pre-flight 3D path planning in complex terrain, offering valuable technical support for autonomous UAV navigation in practical application scenarios such as terrain surveying and disaster search and rescue. Full article

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. Full article

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. Full article

In the realm of precision agriculture, agricultural UAV path planning is challenging because the UAV must avoid obstacles, follow uneven terrain, and satisfy multiple flight constraints simultaneously. Differential evolution (DE) has been widely adopted for this problem because of its simple structure and effective optimization capability. However, existing DE-based methods often become trapped in local optima and cannot effectively balance exploration and exploitation in complex search environments. To address these issues, this paper proposes a reinforcement learning-based multi-strategy differential evolution algorithm, named PPOMSDE. By introducing Proximal Policy Optimization (PPO) to construct a multi-dimensional state pool and an action pool, PPOMSDE enables adaptive strategies for individuals, improving strategy selection during the search process. An independent multi-buffer is adopted to ensure strict data isolation and efficient learning to avoid strategy confusion. In addition, an adaptive triplet mechanism which partitions the population into fitness-based tiers (best, medium, and worst) assigns different control parameters and mutation strategies to individuals with different fitness levels, improving the balance between global exploration and local exploitation. Extensive experiments on the CEC’2014 and CEC’2017 benchmark suites demonstrate the effectiveness of PPOMSDE. The proposed method achieves the lowest average performance ranks of 1.39 on the combined 10-D and 30-D CEC’2014 benchmarks and 1.03 on the 10-D CEC’2017 benchmarks. In agricultural UAV path planning, PPOMSDE generates safer and smoother flight paths while maintaining accurate terrain-following flight, reducing the overall cost by an average of 22.42% compared with ISDE, L-SHADE, SHADE, and ISHACDE. Full article

High-rise building fires pose substantial challenges to conventional firefighting operations due to restricted rescue space and the difficulty of delivering suppression resources rapidly. To improve response efficiency, this study proposes a two-stage mission planning framework for multi-station UAV-based firefighting. The proposed methodology simultaneously accounts for environmental wind, building obstacles, fire evolution, and UAV payload constraints. In the first stage, an improved particle swarm optimization (PSO) algorithm is employed to generate time-optimal flight paths satisfying both spatial obstacle-avoidance and wind-field constraints. In the second stage, based on the actual flight times derived from the first stage, the multi-UAV resource scheduling problem is formulated as a mixed-integer linear programming (MILP) model to minimize the total fire suppression mission duration. Additionally, an isochrone-based firefighting coverage circle is introduced to optimize the layout of additional fire stations. Simulation results indicate that while optimized paths remain geometrically similar under varying wind conditions, wind-induced flight time variations significantly affect UAV arrival sequences and flight times. In the scheduling stage, differences in station layouts and fire scales alter projectile release timing; under unfavorable conditions, such temporal differences can increase the total mission duration by more than 28%. Notably, the optimized addition of fire stations effectively enhances response redundancy in high-rise clusters, reducing fire suppression time in adjacent scenarios by approximately 50%. The proposed method provides theoretical support and methodological guidance for cooperative UAV firefighting and emergency resource optimization in urban environments. Full article

This paper presents an integrated framework for automated detection of surface defects in infrastructures using unmanned aerial vehicles (UAVs), comprising 3D model-based adaptive path planning, high-resolution image acquisition, and an attention-enhanced instance segmentation model. However, existing approaches face two key limitations: (i) conventional UAV path planning lacks adaptive trajectory correction for non-horizontal bridge geometries, and (ii) instance segmentation models for infrastructure defects have not been systematically optimized for both accuracy and edge-device deployability. To address these gaps, the proposed framework was trained on 3625 annotated images covering two defect categories (spalling and cracking) and preliminarily validated through a proof-of-concept field study on a concrete viaduct section where seven spalling and one reinforcement exposure were detected. The methodology consists of three core components: (i) 3D model-based adaptive path planning, (ii) high-resolution image acquisition under variable infrastructure geometries, and (iii) an improved instance segmentation model based on YOLOv8-seg. To ensure consistent imaging geometry, a segmented linear interpolation method is introduced to adaptively correct flight trajectories for non-horizontal infrastructure sections. For damage detection, we propose a structurally enhanced YOLOv8-seg model, denoted as YOLOv8-seg-ECAC2f-all, which integrates Efficient Channel Attention (ECA) modules into a fully modified backbone architecture. Compared to the baseline YOLOv8-seg, the proposed model achieves a mean average precision (mAP_50–95) of 91.8% for bounding box detection and 61.7% for instance segmentation, corresponding to improvements of 7.7% and 2.8%, respectively. The framework was preliminarily validated on a concrete viaduct of the Guangfojiangzhu Expressway, achieving 100% inspection coverage and detection of all eight ground-truth surface defects (seven spalling and one reinforcement exposure) in this pilot study, including two minor spalling cases (≤0.5 m²) missed by manual inspection. These results demonstrate the technical feasibility of the proposed framework for real-world concrete bridge inspection and its potential for reducing manual inspection effort while improving detection sensitivity for minor defects (${\mathrm{m}\mathrm{A}\mathrm{P}}_{50–95}$). Full article

Multi-UAV cooperative path planning in time-varying environments requires balancing flight efficiency, threat avoidance, and coordination consistency while responding rapidly to environmental changes. To address slow population recovery, insufficient inter-task cooperation, and unreliable reuse of historical search information, this paper proposes a Gaussian mixture model (GMM)-based spatio-temporal knowledge transfer multi-objective evolutionary algorithm within the MOEA/D framework, termed STKTM-MOEA/D. The proposed method represents mission requirements and flight constraints using objective functions and feasibility constraints. Elite decision vectors selected through non-dominated sorting and crowding-distance ranking are used to construct GMMs that probabilistically describe promising search regions. Each GMM captures the locations, dispersions, and relative importance of multiple high-quality regions, providing a unified distribution-level knowledge representation for spatial and temporal transfer. Spatial knowledge transfer jointly considers task similarity and distribution complementarity between the main and auxiliary tasks to improve collaborative search. Temporal knowledge transfer retrieves reliable historical GMMs from a knowledge pool according to environmental similarity. After an environmental change, an adaptive reconstruction strategy combines current elites, temporally transferred individuals, spatially transferred individuals, and randomly generated exploratory individuals. The reconstructed population then continues MOEA/D evolution, environmental selection, GMM updating, and knowledge-pool updating under the new environment, improving convergence, diversity, and adaptability. Experiments on 14 DF and 5 FDA problems show that STKTM-MOEA/D achieves the best mean MIGD on 10 problems. In multi-UAV path planning, it attains a 100% success rate and outperforms KTM-DMOEA in HV, PD, and runtime, demonstrating strong effectiveness and efficiency in dynamic environments. Full article