北京林业大学学报2025，Vol.47Issue(4)：129-141,13.DOI:10.12171/j.1000-1522.20240356
基于改进RRT与GA的多目标路径规划
Multi-objective path planning based on improved RRT and GA:taking UAV forest inspection as an example
摘要
Abstract
[Objective]To address the path planning problem of UAVs in plantation areas for inspection tasks(such as pest and disease monitoring and fire prevention),which involves solving the optimal traversal sequence of inspection points and generating collision-free flight trajectories,this paper proposes a multi-objective path planning algorithm by integrating and improving the rapidly-exploring random tree(RRT)algorithm and genetic algorithm(GA).[Method]First,the traditional GA was improved to enable traversal of all inspection points in 3D space and solve the optimal sequence.Second,based on this sequence,the path search was conducted by improving the random sampling principle of RRT algorithm.Obstacle avoidance was achieved through target and tree-avoidance strategies,and redundant turning points generated by obstacle avoidance were eliminated by continuously selecting parent nodes.Finally,the final path was generated through three iterations of B-spline curve optimization.[Result]Simulation results showed that the proposed algorithm can traverse all inspection points and plan high-quality,collision-free paths in complex forest environments within a short time.Compared with particle swarm optimization(PSO),ant colony optimization(ACO),and RRT algorithms,when the number of inspection points increased from 3 to 9,the search times of PSO,ACO,and RRT algorithms increased by 221.77％,332.42％,and 184.78％,respectively,while the proposed algorithm only increased by 102.35％.In a complex environment with 9 inspection points,the path cost of proposed algorithm was reduced by 14.46％,30.28％,and 24.76％compared with PSO,ACO,and RRT algorithms,respectively.The path quality was significantly improved,eliminating path crossing and overlap.Additionally,the algorithm was successfully validated through simulation flights on forest point clouds using a UAV on the ROS platform,demonstrating its applicability for multi-objective path planning in forest inspections.[Conclusion]For the path planning problem of UAVs in artificial forest inspections,the proposed algorithm successfully planned a collision-free path that traversed all inspection points while avoiding obstacles in the forest.Compared with PSO,ACO,and RRT algorithms,the proposed algorithm shows significant advantages in path quality,path cost,and search time.关键词
多目标优化/路径规划/快速随机扩展树(RRT)/遗传算法(GA)/无人机/粒子群算法(PSO)/蚁群算法(ACO)Key words
multi-objective optimization/path planning/rapidly-exploring random trees(RRT)/genetic algorithms(GA)/unmanned aerial vehicles(UAV)/particle swarm algorithm(PSO)/ant colony algorithm(ACO)分类
信息技术与安全科学引用本文复制引用
张彪,康峰,许舒婷..基于改进RRT与GA的多目标路径规划[J].北京林业大学学报,2025,47(4):129-141,13.基金项目
国家自然科学基金项目(12402413),中央高校基本科研业务启动基金项目(BLX202224). （12402413）
