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近年来，多无人机协同执行多目标任务的规划问题受到广泛关注。该问题通常包括三个相互关联的子问题：任务分配、路径规划以及协同优化。在多重约束条件下，这三者存在本质上的耦合关系，彼此影响，从而共同决定协同任务的整体执行效果。针对三者间多重耦合关系的问题，开展了集成式的“任务分配-路径规划-协同优化”方法研究。首先，构建了一种公平优先的任务分配策略，结合分布式拍卖机制与优化建模方法，实现多无人机间任务负载的均衡分配。其次，设计了一种“剪枝-桥接-回溯”（Pruning-Bridging-Backtracking,PBB）的PBB-Dubins路径规划框架，以适应存在复杂障碍的环境，生成平滑可行的轨迹。最后，引入粒子群优化结合局部搜索策略，实现多无人机路径的协同优化。真实数字高程模型地形数据的仿真实验结果表明，多机路径长度偏差能够最终降低至1.37%。该方法能够显著提升在复杂障碍环境中多无人机多目标同步规划的安全性与同步性。
Abstract：In recent years, the cooperative planning of multiple unmanned aerial vehicles(multi-UAVs) for multi-targets missions has received increasing attention. This problem typically involves three interrelated subproblems: task allocation, path planning, and cooperative optimization. Under multiple constraints, these subproblems are inherently coupled, mutually influencing each other and jointly determining the overall performance of the cooperative mission. Focusing on the multiple coupling relationships existing among the three, this research conducts an in-depth study on the integrated task allocation-path planning-collaborative optimization methodology.Firstly, a fair task allocation strategy is developed by combining a distributed auction mechanism with optimization modeling, enabling balanced task distribution among UAVs. Secondly, a pruningbridging-backtracking(PBB) Dubins path planning framework is designed to generate smooth and feasible trajectories in complex obstacle environments. Finally, a hybrid cooperative optimization approach is introduced, integrating particle swarm optimization with local search to refine multi-UAVs trajectories. Simulation experiments based on real digital elevation model terrain data demonstrate that the path length deviation of multiple machines can ultimately be reduced to 1.37%. The proposed method significantly improves both the safety and synchronization of multi-UAVs, multi-targets planning in challenging environments.
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基本信息:
DOI：10.19942/j.issn.2096-5915.2025.06.54
中图分类号:V279;V249;TP18
引用信息:
[1]刘汇钟,王艳坤,孙光辉.复杂障碍场景下多无人机协同任务规划[J].无人系统技术,2025,8(06):38-46.DOI:10.19942/j.issn.2096-5915.2025.06.54.
基金信息:
国家自然科学基金区域创新发展联合基金(U23A20346)
2025-07-16
2025
2025-09-25
2025
1
2025-12-15
2025-12-15