面向多目标的无人机集群搜索任务规划方法OA

A phased cooperative mission planning method based on an improved parthenogenetic algorithm and enhanced RRT∗ algorithm is proposed for UAV swarm search task planning.In the task allocation phase,the multi-target assignment is modeled as a Capacitated Multiple Traveling Salesman Problem.The improved parthenogenetic algorithm incorporates Minkowski distance for path cost estimation and a dynamically adjusted mutation probability strategy to enhance global opti-mization capability.During trajectory planning,the RRT∗ algorithm is upgraded through a"semi-circular sampling+artifi-cial potential field"approach,combined with tangent arc transition method to optimize trajectory smoothness and quality.Simulation results demonstrate that compared with benchmark algorithms,the proposed method achieves a 53.2%faster task allocation speed,reduces total trajectory length by 9.2%,and generates trajectories that satisfy UAV kinematic constraints.This provides theoretical support for efficient cooperative mission planning of UAV swarms in complex battlefield scenarios.