Search Results (758)

This paper proposes a Multi-Strategy Improved Connected Banking System Optimizer, named MICBSO, for numerical optimization and three-dimensional UAV path planning. MICBSO enhances the original CBSO through three coordinated strategies. First, a chaos–opposition learning initialization strategy is introduced to improve initial population quality and search coverage. Second, a Gaussian perturbation-based multi-elite guidance mechanism is designed to reduce dependence on a single best solution and strengthen the balance between exploration and exploitation. Third, a hybrid boundary control strategy combining reflective correction and random reinitialization is developed to improve solution feasibility and maintain population diversity. The proposed algorithm is evaluated on the CEC2017 benchmark suite and compared with 11 representative algorithms. Experimental results show that MICBSO achieves competitive convergence accuracy, stability, and robustness across different dimensional settings. In addition, MICBSO is applied to three-dimensional UAV path planning in four complex terrain scenarios. The results demonstrate that MICBSO can generate feasible and safe flight paths with lower comprehensive cost. Overall, the proposed method provides an effective optimization framework for both benchmark optimization and constrained UAV path planning tasks. Full article

Reinforcement learning (RL) has emerged as a powerful paradigm for enabling autonomous coordination in multi-UAV systems operating in complex and uncertain environments. However, the effectiveness of learned policies is strongly influenced by how actions are implemented at the control level, an aspect that has received limited attention in the literature. This paper presents a comparative study of three control methods (heading-based, waypoint-based, and deterministic) within a unified hybrid-AI architecture, in which the same RL policy structure is used across two of the three configurations. By isolating the control method as the sole variable, the study evaluates how different action abstractions affect learning efficiency, robustness, and operational performance in cooperative surveillance missions. A statistically rigorous Monte Carlo evaluation, supported by non-parametric hypothesis testing, demonstrates that heading-based control consistently achieves superior performance in terms of revisit period, target acquisition time, and tracking continuity. The analysis further reveals that these gains arise from improved reactivity and constraint handling rather than from differences in policy learning. The results highlight the critical role of control-level design in RL-based multi-agent systems and provide practical guidelines for selecting action abstractions in aerial swarm applications. Full article

Existing target sorting methods suffer severe performance degradation or even failure under inherent severe spectrum overlap, homogeneous protocol parameters, and scarce single-source points in Synchronous Non-Orthogonal Frequency Hopping (SNOFH) scenarios. To address this challenge, this paper proposes a passive sorting framework for SNOFH UAV swarm signals based on multi-station relative hopping time difference. The proposed framework constructs a spatial-location-driven sorting feature system, designs a kernel joint distribution adaptation module to eliminate inter-station measurement discrepancies, and develops a multi-scale wavelet-based method to achieve sub-sampling level hopping time extraction, reducing the dependence on prior FH parameters and hardware radio frequency fingerprints. Experimental comparisons between the proposed and reference sorting methods are conducted on a simulated SNOFH dataset to validate the performance of the proposed sorting framework. The experimental results show that the proposed method achieves the highest sorting accuracy of 98%, outperforming adopted baselines in most SNOFH cases. The proposed method exhibits favorable robustness with noise interference, clock-synchronization error, carrier-frequency offset and multipath influence. It is a suitable choice for UAV swarm sorting under regular and slow-varying UAV formations. Full article

Cooperative localization provides a promising solution for multi-UAV navigation in GNSS-denied environments. However, centralized cooperative localization and exact cross-covariance-based distributed methods usually require global state management or explicit propagation of inter-node cross-covariance, resulting in heavy communication and computational burdens. To address this problem, this paper proposes a consistent-innovation-aided distributed cooperative localization method for multi-glider UAV swarms. The method uses a strapdown inertial navigation system as the reference source and introduces inter-node ultra-wideband ranging as cooperative constraints. Each node maintains only its local state and covariance, while a conservative innovation covariance approximation is constructed to perform distributed measurement updates without explicitly propagating global cross-covariance. The cooperative localization sub-filter is further integrated with altitude, geomagnetic, and scene-matching sub-filters through a federated filtering framework with adaptive vector information allocation. A GNSS-denied multi-glider release simulation is established to compare the proposed method with non-cooperative localization, centralized cooperative localization, and an exact-cross-covariance distributed baseline. The results show that the proposed method reduces the position, velocity, and yaw RMSEs by 27.14%, 21.90%, and 9.68%, respectively, compared with the non-cooperative method. Compared with the exact-cross-covariance baseline, the proposed method achieves comparable localization accuracy while reducing communication traffic by approximately 91.44%. Packet-loss experiments further show that the proposed method maintains bounded errors under a 20% packet loss rate, demonstrating improved robustness and engineering feasibility for communication-constrained UAV swarm localization. Full article

Safe-separation-and-collision-avoidance unmanned aerial vehicle (UAV) swarms are increasingly used for inspection, emergency response, environmental monitoring, and search-and-rescue support in cluttered airspace where communication links may be delayed, degraded, or intermittently unavailable. These applications require heterogeneous vehicles to maintain situational awareness, allocate tasks, and avoid hazards under partial observability and changing team topology. To address these challenges, this paper proposes a Hierarchical Graph-Attention Multi-Agent Reinforcement Learning architecture (HG-MARL) for safe-separation-and-collision-avoidance heterogeneous UAV swarm coordination. The proposed framework decomposes the task into high-level resource allocation and low-level local-control execution, uses graph attention for changing swarm topology, and applies Transformer memory, action masking, potential-field reward shaping, and domain-randomized simulation training. In the multi-scenario simulation summaries, HG-MARL achieves 92.9%, 89.8%, and 82.6% task success in Scenarios A–C, respectively, improving upon MAPPO by 15.1, 21.4, and 20.1 percentage points. Summary-statistic Welch tests show that all six HG-MARL comparisons against MAPPO and QMIX yield $p<0.01$ with large effect sizes. Fair-control, reward-sensitivity, communication-degradation, safety-ablation, training-stability, latency, and transfer-oriented stress tests further support the contributions of the integrated architecture. The validation scope is simulator-based, with platform-level flight/HIL evaluation discussed as future work. These results suggest that HG-MARL is a promising simulation-validated framework for civilian UAV swarm coordination in collision-and-separation-critical and communication-degraded environments. Full article

Typhoon-eye sensing places unmanned aerial vehicle (UAV) swarms in a setting where the wind field that carries the target signal also displaces aircraft, drains energy, weakens links, and increases failure risk. A rule that improves only routing or only motion can therefore move the swarm into another failure mode. This paper proposes a risk-driven cross-layer coordination scheme for such missions. A bounded risk index, computed from isolation, connectivity loss, and wind intensity, acts as a supervisory variable for multi-hop reachability maintenance, isolated-node recovery, and layered altitude adaptation. For evaluation, graph reachability is separated from useful data return through a degraded multi-hop aggregation model that includes distance loss, wind-dependent reliability, rain-induced packet loss, relay forwarding loss, and mothership collection capacity. The simulator combines a bounded Holland-type storm field, stochastic turbulence, nonlinear propulsion energy consumption, and wind-dependent structural failure. Against three literature-inspired baselines, two AI-inspired comparators, and six ablation variants, the method keeps a balanced profile across connectivity, isolation, wind exposure, data collection, and survival. In 30-run steady-state robustness tests under heavy-rain attenuation, the full strategy showed clear gains over routing-only and multi-agent reinforcement learning (MARL)-routing comparators in connectivity and isolation, but did not uniformly dominate topology reconstruction or the multi-agent deep deterministic policy gradient–artificial potential field (MADDPG-APF) recovery comparator. The results indicate that, in storm-dominated swarm sensing, resilience comes mainly from coordinating exposure reduction with topology stabilization, rather than from optimizing a single layer. Full article

This paper investigates the coordinated deployment and routing of communication equipment by a Sub-mother UAV swarm in power-grid emergency communication scenarios. Considering mission timeliness and payload constraints, a heterogeneous MUAV–SUAV coordinated deployment-and-routing model is established to minimize the total system cost, including platform flight cost, SUAV activation cost, and penalty cost caused by delayed deployment. To solve this problem, a two-stage optimization framework is proposed. In the first stage, an improved K-means clustering algorithm with neighborhood search (K-means-NS) is developed to divide deployment points into feasible sub-regions while satisfying SUAV endurance constraints and maintaining the deployment–retrieval payload balance required by the MUAV. In the second stage, the MUAV inter-region visiting sequence is treated as a routing subproblem, and an improved adaptive genetic algorithm (IAGA) is designed to optimize the coordinated routes of the MUAV and SUAVs within each sub-region. The IAGA adopts hybrid encoding, feasible-solution adjustment, elitist selection, and adaptive crossover–mutation operations to improve search efficiency under complex constraints. Numerical experiments on small-, medium-, and large-scale scenarios show that the proposed method can generate feasible sub-region divisions and coordinated routing schemes. Compared with GA and G-PSHA, IAGA reduces the total flight cost by approximately 21.2%, 10.5%, and 23.2% relative to GA and by approximately 0.2%, 2.5%, and 8.1% relative to G-PSHA in the three scenarios, respectively. Sensitivity analysis further indicates that stricter mission-timeliness requirements increase penalty costs, highlighting the importance of timely communication-device deployment in emergency restoration. Full article

UAV path planning in complex static 3D environments involves multiple conflicting objectives and intricate constraints. However, when applied to highly constrained path planning tasks, MOWOA often suffers from a low proportion of feasible solutions, convergence instability, single-leader search bias, and an uneven distribution of Pareto solutions. To address these issues, this study formulates the UAV path planning problem as a multi-objective optimization problem that simultaneously considers path length, threat cost, smoothness cost, and altitude cost, and proposes an improved multi-leader multi-objective whale optimization algorithm (IML-MOWOA). The proposed IML-MOWOA progressively improves three key stages of the optimization process: initial population construction, search guidance, and external archive maintenance. Specifically, an adaptive opposition-based learning initialization strategy is first introduced to improve the feasibility and spatial coverage of initial paths. Based on the resulting non-dominated solution set, a grid-based external archive update strategy is then used to regulate solution density and provide representative candidate leaders from sparse Pareto regions. Subsequently, a multi-leader dynamic weighted search mechanism with Softmax-based cosine annealing integrates these leaders into the WOA update process, thereby enhancing multi-directional path exploration and alleviating premature convergence. Comparative experiments conducted in three static 3D environments of varying complexity demonstrate that the proposed method achieves more robust convergence, better Pareto-front distribution, and more balanced task-level path quality than the benchmark algorithms. In the most challenging scenario, IML-MOWOA achieves the highest number of feasible paths, reduces the mean IGD by 25.04%, and decreases the mean path length, threat cost, smoothness cost, and altitude cost by 1.65%, 28.45%, 53.23%, and 29.88%, respectively, compared with the best-performing competing algorithm for each metric. These results indicate that the proposed algorithm is effective and robust for constrained multi-objective UAV path planning in complex static 3D environments. Full article

Coordinating multiple unmanned aerial vehicle (UAV) systems under strict energy and temporal constraints remains a complex scheduling problem. Existing reinforcement learning methods typically rely on fixed-time-step modeling, which struggles to accommodate flight actions of varying durations and often leads to temporal mismatches between task planning and physical execution. To address this limitation, we propose an Adaptive Hierarchical Semi-Markov Decision Process (AH-SMDP) framework. This architecture decouples task allocation from execution by modeling variable-length actions via an SMDP. An event-driven synchronization mechanism is introduced to align the swarm’s decision-making rhythm with actual task completion times. Additionally, a state-aware reward formulation and a dynamic action space pruning strategy are designed to help UAVs balance energy efficiency with deadline compliance. Simulation results in multi-constraint environments demonstrate that the AH-SMDP framework effectively improves scheduling performance compared to standard MAPPO and PPO algorithms. Under the evaluated experimental settings, the proposed method yields improvements of approximately 30% in average task completion rate, 40% in energy reduction, and 60% in convergence stability. Ablation studies further suggest that this integrated framework offers a viable and effective approach for multi-UAV scheduling. Full article

While MADRL has demonstrated significant potential in Unmanned Aerial Vehicle (UAV) swarm control, traditional architectures often rely on fixed-dimensional observation spaces. This rigid structural constraint severely limits the swarm’s adaptability in dynamic environments, particularly when facing sudden topological changes such as node failures or dynamic reinforcements. To overcome these limitations, this paper proposes an end-to-end UAV swarm motion control framework incorporating a state-modulated Graph Attention Network (GAT). By modeling the swarm as a dynamic interaction graph, the proposed method dynamically aggregates neighbor features using attention weights modulated by the agents’ real-time kinematic states. Furthermore, a virtual structure combined with an auction mechanism is introduced to achieve precise formation planning and target allocation. Evaluated in the Genesis 3D physics engine, the proposed Prioritized Experience Replay (PER)-MADDPG-Graph Attention Network (GAT) algorithm exhibits superior robustness and spatial adaptability. Extensive experiments, including dynamic node reduction and addition scenarios, confirm that the proposed framework seamlessly maintains swarm configurations without catastrophic policy degradation, outperforming baseline MADRL methods in both convergence speed and control precision. Full article

Owing to the stringent spatio-temporal coupling and kinematic constraints, the task allocation problem for heterogeneous unmanned aerial vehicle (UAV) swarms is generally regarded as an NP-hard problem. To address this, this paper proposes the Sequentially Extended Consensus-Based Bundle Algorithm (SECBBA), a deadlock-free distributed scheduling framework. First, a multi-task allocation model is established by incorporating constraints associated with payload resources, task scheduling, and threat zone. Subsequently, the conventional Consensus-Based Bundle Algorithm (CBBA) is extended through the integration of a deadlock detection and resolution mechanism based on directed graph Depth-First Search (DFS), thereby guaranteeing conflict-free task allocation. Furthermore, a sequential hierarchical strategy is introduced to transform global temporal dependencies into tractable soft time-window constraints. Finally, to ensure physical feasibility, Dubins curves are tightly coupled with the allocation process, enabling nonholonomic path planning for fixed-wing UAVs. Simulation results demonstrate that SECBBA reduces global task costs by 13.3%, 22.7%, and 39.4% compared to the Consensus-Based Bundle Algorithm with Temporal Consistency Constraints (CBBA-TCC), Improved Genetic Algorithm (IGA) and Q-Learning baselines, respectively. It consistently maintains performance advantage of 9.8%, 23.2% and 19.0% under variable weights with high computational efficiency, significantly enhancing swarm timeliness in complex, coupled multi-task scenarios. Full article

To tackle the dynamic assignment problem of unmanned aerial vehicle (UAV) swarms, a merged coordination-optimized pigeon-inspired optimization (MCOPIO) algorithm based on the pigeon-inspired optimization (PIO) algorithm is proposed in this paper. The algorithm disrupts the original pigeon distribution via random grouping and performs mutual learning and optimization within the new groups. After dynamic optimization, the underperforming pigeons are discarded, and the flock is reorganized. Subsequently, the two stages of the basic PIO are integrated through a dynamic factor. These improvements overcome the limitations of the basic PIO algorithm, such as insufficient global search capability, poor stability, and disconnection between the two algorithm stages. Comparative experiments are conducted with the state-of-the-art intelligent computing algorithms, such as the basic PIO, particle swarm optimization (PSO), genetic algorithm (GA), and improved consensus-based bundle algorithm (ICBBA), the comparative results verify the feasibility and effectiveness of our improved PIO for UAV swarm dynamic task allocation. Full article

Multi-UAV cooperative path planning is an important technical basis for improving offshore emergency response efficiency in complex maritime environments. However, in complex offshore environments, cooperative trajectory planning is affected not only by geometric obstacles but also by wind disturbances, island terrain, restricted flight zones, and inter-UAV safety and communication constraints. These coupled factors make it difficult for conventional swarm intelligence optimizers to maintain risk awareness, local correction capability, and stable late-stage refinement. To address this problem, this paper proposes a risk-aware Modified Traffic Jam Optimizer for cooperative multi-UAV path planning in complex offshore missions. Unlike the original Traffic Jam Optimizer, the proposed method explicitly incorporates risk information into the population update process. A risk-opposition collaborative guidance strategy is designed to adjust the global search direction away from high-risk regions; a risk-based geometric multiscale adaptive mutation strategy is developed to identify and correct high-risk local control blocks; and a generalized quadratic interpolation decision-vector reconfiguration mechanism is introduced to refine the current best solution during stagnation or late-stage search. Two-UAV and three-UAV simulations are conducted using the constructed offshore environment and cooperative constraint models. The results show that the proposed method can generate feasible cooperative trajectories and achieve better performance than the comparison algorithms in path cost, path length, synchronized flight time, and convergence behavior. These results verify the feasibility and effectiveness of the proposed method for risk-aware multi-UAV cooperative path planning in complex offshore environments. Full article

UAV swarms provide a flexible sensing platform for smart-city crowd surveillance, but cooperative aerial monitoring remains challenging due to dynamic pedestrian distributions, partial observability, and the trade-off between visual coverage and recognition accuracy. In particular, flying at higher altitudes increases the field of view but reduces recognition accuracy, while low-altitude flight improves visual quality at the cost of limited coverage. To address these challenges, this paper proposes an environment-aware cooperative navigation framework that integrates spatiotemporal density prediction with multi-agent reinforcement learning. The surveillance area is modeled as a spatiotemporal graph, where sparse and partial UAV observations are used to predict future pedestrian-density maps and confidence intervals. The predicted density and uncertainty, together with empirical recognition error, UAV position, flight height, battery state, and historical observations, are incorporated into MARL-based policy learning. The learned policy enables UAVs to cooperatively adjust movement and altitude decisions under the centralized training and decentralized execution paradigm. Extensive simulations in UAV-based crowd surveillance environments demonstrate that the proposed framework achieves a more favorable coverage–error trade-off than representative heuristic, prediction-based, single-agent reinforcement learning, and multi-agent reinforcement learning baselines. The results show that prediction-aware and accuracy-aware cooperation improves pedestrian-level surveillance performance under dynamic and partially observable crowd distributions. Full article

In resource-constrained UAV swarm systems, multimodal sensory data are often affected by complex environmental factors, resulting in modality missing, signal degradation, and asynchrony, which significantly reduce the reliability of multimodal learning and incremental model updates. To address this issue, we propose a Compensatory Collaboration Modality-Balanced Sample Selection framework (CC-MBS), which improves robustness through modality quality modeling and cross-UAV collaborative compensation. Specifically, a modality confidence vector is introduced to quantify modality reliability from missing rate, degradation, and asynchrony. A lightweight collaboration mechanism is designed to exchange low-dimensional confidence information instead of high-dimensional features or model parameters. Based on the compensated confidence, a modality-aware sample selection strategy is further developed to prioritize high-value samples under limited memory. Experimental results in simulated UAV-swarm-inspired benchmark settings show that CC-MBS outperforms representation-based methods such as ShaSpec and its parameter aggregation variants (AVG, PFM, POW) in both modality compensation accuracy and communication–computation efficiency under missing conditions. In addition, it achieves stronger robustness than MBS and training-dynamics-based methods such as EL2N and GraNd in sample selection. These results demonstrate that CC-MBS effectively improves robustness and data efficiency for multimodal incremental learning under incomplete modalities. Full article