Two-layer path planning algorithm for multiple UAVs in urban combat

城市环境下多无人机双层路径规划算法

School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

Received: 14 June 2025

Abstract

In response to the challenges of multiple unmanned aerial vehicles'(UAV) rapid strike missions against multiple enemy targets in a complex urban combat environment, where the optimization of multiple objectives such as energy consumption, flight risk and inter-UAV safety distance must be considered, this paper proposes a two-layer optimization algorithm based on the A^* algorithm and the improved particle swarm optimization(PSO) algorithm. The method utilizes the A^* algorithm to generate initial paths, which serve as initial solutions for the PSO algorithm, thereby enhancing the early-stage search efficiency. Meanwhile, a particle mechanism with mutation structures is introduced to improve the global search capability of the PSO algorithm in its solution space and to mitigate premature convergence. The simulation results demonstrate that the proposed two-layer optimization algorithm signi-ficantly improves search efficiency and effectively addresses the problem of local optima. The findings verify the feasibility and effectiveness of the proposed algorithm in multi-UAV path planning tasks in a complex urban combat environment.

摘要

针对复杂城市作战环境下多无人机对敌多目标快速打击任务中需同时考虑能耗、飞行风险及安全距离等多种优化指标, 现有算法在路径规划中存在搜索速度慢、易陷入局部最优等问题, 提出一种基于A^*算法与改进粒子群算法(PSO)的双层优化方法。该方法利用A^*算法生成初始路径, 作为粒子群算法的初始解, 提升搜索初期的效率; 同时引入带变异结构的粒子机制, 增强粒子群在解空间中的全局搜索能力, 有效缓解早熟收敛问题。仿真实验结果表明, 该双层优化算法在提升搜索效率的同时, 显著改善了陷入局部最优的问题。研究结果验证了所提算法在复杂城市环境下多无人机路径规划任务中的可行性和有效性。