基于RRT的无人机航迹规划算法  

UAV Path Planning Algorithm based on Improved Rapidly-exploring Random Tree

作　　者：王仁杰 杨苹[1] 刘泽健 周德棕 李文胜 WANG Ren-jie;YANG Ping;LIU Ze-jian;ZHOU De-zong;LI Wen-sheng(South China University of Technology,Guangzhou 510000,China;Shenzhen Huagong Energy Technology Co.,Ltd.,Shenzhen 518000,China;Southern Offshore Wind Power United Development Co.,Ltd.,Zhuhai 519000,China;China Southern Power Grid Technology Co.,Ltd.,Guangzhou 510000,China)

机构地区：[1]华南理工大学,广东广州510000 [2]深圳华工能源技术有限公司,广东深圳518000 [3]南方海上风电联合开发有限公司,广东珠海519000 [4]南方电网电力科技股份有限公司,广东广州510000

出　　处：《航空计算技术》2024年第2期66-70,共5页Aeronautical Computing Technique

基　　金：广东省自然资源厅2022年海洋六大产业专项资助(GDNRC[2022]26)。

摘　　要：针对传统快速搜索随机树无人机航迹规划算法中采样点生成随机性过高,规划路径平滑度差的问题,提出一种基于改进快速搜索随机树的无人机航迹规划算法。在搜索树拓展过程中,引入限定采样点生成范围的探测场,改变采样点的生成策略,使算法具备迅速向目标收敛的能力和绕开障碍物威胁的能力。此外,改进算法还对初始航迹的冗余点进行裁剪,并采用B样条曲线法改善航迹平滑度。仿真结果表明,基于改进快速搜索随机树的无人机航迹规划算法比传统快速搜索随机树无人机航迹规划算法平均航迹长度减少21.75%,平均搜索树节点数减少75.78%,平均计算时间减少48.04%。Aiming at the problems of high randomness of sampling point generation and poor smoothness of planning path in rapidly-exploring random tree UAV path planning algorithms,an UAV path planning algorithm based on improved rapidly-exploring random tree is proposed.During the tree expansion process,a detection field that limits the generation range of sampling points is introduced,and the generation strategy of sampling points is changed,enabling the algorithm to quickly converge to the target and bypass the threat of obstacles.In addition,the improved algorithm also cuts the redundant points of the initial track,and uses B-spline curve method to improve the smoothness of the track.The simulation results demonstrate that the proposed path planning algorithm can reduce the average path length by 21.75%,the average number of search tree nodes by 75.78%,and the average calculation time by 48.04% compared to the rapidly-exploring random tree based UAV path planning algorithm.

关 键 词：无人机 航迹规划算法 快速搜索随机树 

分 类 号：V279[航空宇航科学与技术—飞行器设计]