Search Results (114)

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

Large multi-UAV mission systems operate over time-varying communication graphs with heterogeneous platforms, where classical distributed task assignment may incur excessive message passing and suboptimal task–resource matching. To address these challenges, this paper proposes CLAC-CBBA (Centrality-Driven and Load-Aware Adaptive Clustering CBBA), an enhanced variant of the Consensus-Based Bundle Algorithm (CBBA) for large heterogeneous swarms. The proposed method is biomimetic in the sense that it integrates swarm-inspired self-organization and load-aware self-regulation to improve scalability and robustness, resembling decentralized role emergence and negative-feedback workload balancing in natural swarms. Specifically, CLAC-CBBA first identifies key nodes via a centrality-based adaptive cluster-reconfiguration mechanism (CenCluster) and partitions the network into cooperation domains to reduce redundant communication. It then applies a load-aware cluster self-regulation mechanism (LCSR), which combines resource attributes and spatial information, uses K-medoids clustering, and triggers split/merge reconfiguration based on real-time load imbalance. CBBA bidding is executed locally within clusters, while anchors and cluster representatives synchronize winners/bids to ensure globally consistent, conflict-free assignments. Simulations across diverse network densities and swarm sizes show that CLAC-CBBA reduces communication overhead and runtime while improving total task score compared with CBBA and several advanced variants, with statistically significant gains. These results demonstrate that CLAC-CBBA is scalable and robust for large-scale heterogeneous UAV task allocation. Full article

Emerging multi-unmanned aerial vehicle (multi-UAV) applications in smart cities must navigate cluttered airspace while meeting tight mobile edge computing (MEC) deadlines. Classical grid planners, including A-star (A*), D-star Lite (D* Lite), and conflict-based search with D-star Lite (CBS-D*) and metaheuristics such asparticle swarm optimization (PSO), either replan too slowly in dynamic scenes or waste energy on long detours. This paper presents PPO-adjusted incremental refinement (PAIR), a decentralized hybrid planner that couples an A* global backbone with a continuous PPO refinement module for multi-UAV navigation on two-dimensional (2-D) urban grids. A* produces feasible waypoint routes, while a shared risk-aware PPO policy applies local offsets from a compact state encoding. MEC tasks are allocated by a separate heterogeneous scheduler; PPO optimizes geometric objectives (path length, risk, and a normalized propulsion-energy surrogate). Across nine benchmark scenarios with static and Markovian dynamic obstacles, PAIR achieves 100% mission success (matching the strongest baselines) while delivering the best energy surrogate (104.9 normalized units) and shortest mean travel time (207.8 s) on a reproducible $100\times 100$ grid at fixed UAV speed. Relative to the strongest non-learning baseline (PSO), PAIR reduces energy by about 4% and travel time by about 3%, and yields roughly 10–20% gains over the remaining planners. An obstacle-density sweep with 5–30 moving obstacles further shows that PAIR maintains shorter paths and the lowest cumulative replanning time, supporting real-time multi-UAV navigation in dynamic urban MEC environments. Full article

Centralized UAV logistics controllers can achieve strong navigation performance in controlled settings, but they do not capture key deployment factors in crowdsourcing-enabled emergency logistics, where heterogeneous UAV owners participate with unreliability and dropout, and incentive expenditure and fairness must be accounted for. This paper presents a decentralized crowdsourcing multi-UAV emergency logistics framework on an edge-orchestrated architecture that (i) performs urgency-aware dispatch under distance/energy/payload constraints, (ii) tracks reliability and participation dynamics under stress (unreliable agents and dropout), and (iii) quantifies incentive feasibility via total payment and payment inequality (Gini). We adopt a hybrid decision design in which PPO/DQN policies provide real-time navigation/control, while GA/ACO act as planning-level route refinement modules (not reinforcement learning) to improve global candidate quality under safety constraints. We evaluate the framework in a controlled grid-world simulator and explicitly report stress-matched re-evaluation results under matched stress settings, where applicable. In the nominal comparison, centralized DQN attains high navigation-centric success (e.g., 0.970 ± 0.095) with short reach steps, but it omits incentives by construction, whereas the proposed crowdsourcing method reports measurable payment and fairness outcomes (e.g., payment and Gini) and remains evaluable under unreliability and dropout sweeps. We further provide a utility decomposition that attributes negative-utility regimes primarily to collision-related costs and secondarily to incentive expenditure, clarifying the operational trade-off between mission value, safety risk, and incentive cost. Overall, the results indicate that navigation-only baselines can appear strong when participation economics are ignored, while a deployable crowdsourcing system must explicitly expose incentive/fairness and robustness characteristics under stress. Full article

In large-scale Unmanned Aerial Vehicle (UAV) and task environments—particularly those involving obstacles—dimensional explosion remains a significant challenge in Multi-Robot Task Allocation (MRTA). To this end, a novel heuristic MRTA framework based on Topological Graph Construction (TGC) is proposed. First, the physical map is transformed into a pixel map, from which a Generalized Voronoi Graph (GVG) is generated by extracting clearance points, which is then used to construct the topological graph of the obstacle environment. Next, the affiliations of UAVs and tasks within the topological graph are determined to partition different topological regions, and the task value of each topological node is calculated, followed by the first-phase Task Allocation (TA) on these topological nodes. Finally, UAVs within the same topological region with their allocated tasks perform a local second-phase TA and generate the final TA result. The simulation experiments analyze the influence of different pixel resolutions on the performance of the proposed method. Subsequently, robustness experiments under localization noise, path cost noise, and communication delays demonstrate that the total benefit achieved by the proposed method remains relatively stable, while the computational time is moderately affected. Moreover, comparative experiments and statistical analyses were conducted against k-means clustering-based MRTA methods in different UAV, task, and obstacle scale environments. The results show that the proposed method improves computational speed while maintaining solution quality, with the PI-based method achieving speedups of over 60 times and the CBBA-based method over 10 times compared with the baseline method. Full article

With the advancement of 6G networks, terrestrial centralized network architectures are evolving toward integrated space–air–ground network frameworks, imposing higher requirements on the efficiency of computation offloading and multi-objective collaborative optimization. However, existing single-decision strategies in integrated space–air–ground networks find it difficult to achieve coordinated optimization of delay and load balancing under energy tolerance constraints during task offloading. To address this challenge, this paper integrates communication transmission and computation models to design a two-stage computation offloading model and formulates a multi-objective optimization problem under energy tolerance constraints, with the primary objectives of minimizing overall system delay and improving network load balance. To efficiently solve this constrained optimization problem, a two-stage computation offloading solution based on a Hierarchical Cooperative African Vulture Optimization Algorithm (HC-AVOA) is proposed. In the first stage, the task offloading ratio from ground devices to unmanned aerial vehicles (UAVs) is optimized; in the second stage, the task offloading ratio from UAVs to satellites is optimized. Through a hierarchical cooperative decision-making mechanism, dynamic and efficient task allocation is achieved. Simulation results show that the proposed method consistently maintains energy consumption within tolerance and outperforms PSO, WaOA, ABC, and ESOA, reduces the average delay and improves load imbalance, demonstrating its superiority in multi-objective optimization. Full article

The rapid proliferation of connected and autonomous vehicles in the 6G era demands ultra-reliable and low-latency computation with intelligent resource coordination. Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) provides a flexible and scalable solution to extend coverage and enhance offloading efficiency for dynamic Internet of Vehicles (IoV) environments. However, jointly optimizing task latency, user fairness, and service priority under time-varying channel conditions remains a fundamental challenge.To address this issue, this paper proposes a novel Multi-Agent Priority-based Fairness Adaptive Delayed Deep Deterministic Policy Gradient (MA-PF-AD3PG) algorithm for UAV-assisted MEC systems. An occlusion-aware dynamic deadline model is first established to capture real-time link blockage and channel fading. Based on this model, a priority–fairness coupled optimization framework is formulated to jointly minimize overall latency and balance service fairness across heterogeneous vehicular tasks. To efficiently solve this NP-hard problem, the proposed MA-PF-AD3PG integrates fairness-aware service preprocessing and an adaptive delayed update mechanism within a multi-agent deep reinforcement learning structure, enabling decentralized yet coordinated UAV decision-making. Extensive simulations demonstrate that MA-PF-AD3PG achieves superior convergence stability, 13–57% higher total rewards, up to 46% lower delay, and nearly perfect fairness compared with state-of-the-art Deep Reinforcement Learning (DRL) and heuristic methods. Full article

This paper investigates a load-balanced pickup strategy for heterogeneous multi-UAV systems, where unmanned aerial vehicles (UAVs) with different flight speeds and payload capacities cooperatively collect spatially distributed parcels while avoiding no-fly zones. The goal is to minimize the maximum mission completion time among UAVs while ensuring balanced workload distribution according to their heterogeneous capabilities. The formulated problem is a mixed-integer nonlinear program that jointly optimizes pickup assignment, trajectory planning, and slot duration allocation under mobility, safety, and payload constraints. To address the nonconvexity of the optimization problem, the successive convex approximation and penalty convex–concave procedure are applied, leading to a two-stage iterative algorithm that efficiently derives practical UAV strategies for load-balanced parcel pickup. The first stage minimizes the maximum completion time, and the second stage further refines the trajectories to reduce the total travel distance. Simulation results demonstrate that the proposed scheme effectively adapts to UAV capability asymmetry and achieves superior time efficiency compared to benchmark schemes. The results also point to future research opportunities, such as incorporating energy models, communication constraints, or stochastic task dynamics to extend the applicability of the proposed framework. Full article

Multi-UAV search missions for prioritized targets under constrained communications suffer from weak communication-decision integration, limited global perception synchronization, and delayed mission response. This paper formulates multi-UAV collaboration search as a multi-objective optimization problem to balance communication overhead and search performance. A Cooperative Hierarchical Target Search under Constrained Communications (CHTS-CC) algorithm is proposed to address the problem. The algorithm incorporates a Cluster-Consistent Information Fusion with Event Trigger (CCIF-ET) method, which enables intra-cluster information fusion. When clusters connect, a single merge that applies joint weighting by cluster scale and uncertainty reduces communication overhead. Furthermore, a Dynamic Preemptive Task Allocation (DPTA) mechanism reallocates UAV resources based on target priority and estimated time of arrival (ETA), enhancing responsiveness to high-priority targets. Simulation results show that when all UAVs and communication links operate normally, CCIF-ET reduces total confirmation time by 8.73% compared to the uncoordinated baseline and maintains a 24.43% advantage during single-UAV failures. In scenarios with obstacles, failures, and dynamic targets, CHTS-CC reduced mission completion steps by 34.78%, 32.35%, and 55.45% compared to the non-allocation baseline. The average detection time for high-priority targets decreased by 28.48%, 29.41%, and 58.82%, respectively, demonstrating the effectiveness of the proposed algorithm. Full article

The cooperative operation of multiple Unmanned Aerial Vehicles (multi-UAV) is emerging as a pivotal trend in future complex autonomous systems. To enable accurate mission understanding and efficient collaboration among UAVs in complex, dynamic, and uncertain operational environments, this paper introduces CogMUS, a novel cooperative mission understanding framework based on the Soar cognitive architecture. We first construct a mission understanding framework for UAV operations centered around five typical mission categories. Building on this foundation, we design a distributed cognitive model where each UAV is equipped with a Soar agent. This model leverages the synergy of working memory (WM), long-term memory (LTM), and the decision cycle (DC) to achieve key functionalities, including hierarchical mission decomposition, dynamic task allocation, and proactive airspace conflict detection and resolution. Through comprehensive simulation experiments, we validate the performance of the proposed CogMUS framework across key metrics, including task understanding accuracy, cooperative efficiency, and overall task completion rate. The results demonstrate that CogMUS exhibits superior adaptability to diverse scenarios, as well as remarkable scalability and robustness. Full article

Unmanned aerial vehicles (UAVs) play an increasingly vital role in maritime search and rescue (SAR) because they can be deployed quickly, cover large ocean areas, and operate without exposing human crews to risk. Compared with single platforms, multi-UAV cooperation improves efficiency in locating drifting targets influenced by wind and currents. However, existing allocation methods often focus only on immediate task benefits and neglect search history, leading to redundant revisits and lower overall efficiency. To address this problem, we propose a hybrid auction–pheromone framework for multi-UAV maritime SAR. The method combines an auction-based allocation strategy, which assigns tasks according to target probability, distance, and UAV workload, with a pheromone-guided mechanism that records visitation history through exponential decay to discourage repeated searches. A layered model is constructed, consisting of an airspace/weather constraint layer, a target probability layer, a pheromone layer, and a UAV motion layer. UAVs adopt A* path planning with a nearest-first policy, while a stagnation detector triggers dynamic reallocation when coverage slows. Simulation experiments verify the effectiveness of the proposed approach. Compared with auction-only and pheromone-only baselines, the hybrid method reduces the required steps by up to 27.1%, decreases the overlap ratio to 0.135–0.164, and increases the coverage speed by 64.7%. These results demonstrate that integrating explicit auctions with implicit pheromone memory significantly enhances scalability, robustness, and efficiency in multi-UAV maritime SAR. Future research will focus on dynamic drift modeling, real-world deployment, and heterogeneous UAV collaboration. Full article

Heterogeneous unmanned aerial vehicle (UAV) swarms are becoming critical components of next-generation non-terrestrial networks, enabling tasks such as communication relay, spectrum monitoring, cooperative sensing, and navigation. Yet, their heterogeneity and multifunctionality bring severe challenges in task allocation and resource scheduling, where traditional multi-agent reinforcement learning methods often suffer from high algorithmic complexity, lengthy training times, and deployment difficulties on resource-constrained nodes. To address these issues, this paper proposes a low-complexity multi-agent soft actor–critic (MASAC) framework that combines parameter sharing (shared actor with device embeddings and shared-backbone twin critics), lightweight network design (fixed-width residual MLP with normalization), and robust training mechanisms (minimum-bias twin-critic updates and entropy scheduling) within the CTDE paradigm. Simulation results show that the proposed framework achieves more than 14-fold parameter compression and over a 93% reduction in training time, while maintaining or improving performance in terms of the delay–energy utility function. These advances substantially reduce computational overhead and accelerate convergence, providing a practical pathway for deploying multi-agent reinforcement learning in large-scale heterogeneous UAV clusters and supporting diverse mission scenarios under stringent resource and latency constraints. Full article

To address the uneven distribution of the Pareto front and insufficient convergence in multi-UAV task allocation, this paper proposes GrEAPSO, an improved algorithm that hybridizes Particle Swarm Optimization (PSO) with Genetic Algorithm (GA). GrEAPSO balances exploitation and exploration through grid partitioning, adopts a dual-encoding scheme coupled with crossover and mutation to enhance population diversity, and employs a grid-based environmental selection mechanism to improve the uniformity of the Pareto set. After initialization, the algorithm iteratively performs a PSO-based local search, genetic crossover and mutation, and grid-based environmental selection. The offspring and parent populations are then merged, and the archive set is updated accordingly. Across three military UAV task-allocation scenarios (small, medium, and large), GrEAPSO is benchmarked against MOPSO, NSGA-II/III, MOEA/D-DE, RVEA, IBEA, MOMVO, and MaOGOA. All experiments use a population size of 100. Its reference point is undominated and dominates some competitors, with median gains of 55.78% in hypervolume and 8.11% in spacing. Finally, the sensitive analysis further indicates that dividing the objective space into 15–20 grids offers the best trade-off between search breadth and solution distribution. Full article

Cooperative pursuit using multi-UAV systems presents significant challenges in dynamic task allocation, real-time coordination, and trajectory optimization within complex environments. To address these issues, this paper proposes a reinforcement learning-based task planning framework that employs a distributed Actor–Critic architecture enhanced with bidirectional recurrent neural networks (BRNN). The pursuit–evasion scenario is modeled as a multi-agent Markov decision process, enabling each UAV to make informed decisions based on shared observations and coordinated strategies. A multi-stage reward function and a BRNN-driven communication mechanism are introduced to improve inter-agent collaboration and learning stability. Extensive simulations across various deployment scenarios, including 3-vs-1 and 5-vs-2 configurations, demonstrate that the proposed method achieves a success rate of at least 90% and reduces the average capture time by at least 19% compared to rule-based baselines, confirming its superior effectiveness, robustness, and scalability in cooperative pursuit missions. Full article

In multi-UAV cooperative tasks, dynamic communication topologies and resource heterogeneity present significant challenges for distributed task allocation, leading to high communication overhead and poor task-resource matching, which in turn increases computational costs. While the Consensus-Based Bundle Algorithm (CBBA) offers a robust decentralized framework, its scalability and adaptability in heterogeneous, large-scale scenarios are limited. To overcome these issues, this paper introduces a novel Two-Level Clustered CBBA (TLC-CBBA). In the first-layer clustering, UAVs are grouped based on communication topology using graph-theoretic centrality measures to rank node importance, followed by clustering based on shortest-path distances to minimize communication costs. In the second-layer clustering, a resource-balanced and distance-aware K-medoids algorithm is applied within each subgroup obtained from the first-layer clustering, taking into account UAV resource heterogeneity and spatial proximity. This method ensures spatial compactness among UAVs within each subgroup while achieving a more balanced distribution of total resources across clusters. Finally, after completing the two-level clustering, each subgroup executes CBBA for local task bundling and consensus, while the cluster centers coordinate inter-cluster communication to guarantee globally consistent and conflict-free task allocation. Simulations across diverse mission scenarios and UAV team sizes demonstrate that TLC-CBBA substantially outperforms CBBA and its variants (DMCHBA, G-CBBA, and Clustering-CBBA) in terms of communication efficiency, total task score, runtime, and significance analysis. The proposed TLC-CBBA demonstrates strong robustness and scalability for heterogeneous multi-UAV task allocation in dynamic environments. Full article