摘要
Abstract
Unmanned aerial vehicle path planning,derived from robot motion planning,is currently a central focus in unmanned aerial vehicle application research and plays a crucial role in enhancing the operational capabilities of unmanned aerial vehicle systems in complex environments.In this paper,aiming at the problems of Rapidly-exploring Random Tree(RRT)algorithm in unmanned aerial vehicle path planning,such as high search randomness in search,redundant paths and poor path smoothness,an improved RRT algorithm for Unmanned Aerial Vehicle path planning is proposed.The improved RRT algorithm combines the gravitational function from the artificial potential field method with the RRT algorithm to guide the generation of random nodes in a directed manner,thereby con-straining the expansion direction of the random tree and reducing search randomness.Secondly,the greedy algorithm is combined to prune and optimize the planned path,shortening path length.Finally,the B-spline curve is combined to smooth the path,removing the turning points of curvature mutations,and forming a smooth path suitable for the actual flight of unmanned aerial vehicles.A comparison and analysis of the A* algorithm,the traditional RRT algorithm and the improved RRT algorithm are conducted using simulation soft-ware.Simulation results show that the improved RRT algorithm proposed in this paper has higher performance,with average planning time improvements of 49.44%and 17.97%compared to the traditional RRT algorithm in narrow-channel scenarios and complex obsta-cle scenarios,and 80.23%and 52.93%compared to the A*algorithm.The generated paths are shorter and smoother,while significant-ly reducing the possibility of path planning failures compared to the RRT algorithm.This verifies the feasibility and effectiveness of the improved RRT algorithm,addressing the issues of high search randomness,redundant paths and poor path smoothness in the original al-gorithm.关键词
无人机/路径规划/改进快速扩展随机树算法/快速扩展随机树算法/A*算法/引力函数Key words
unmanned aerial vehicle/path planning/improved RRT algorithm/RRT algorithm/A* algorithm/gravitational function分类
信息技术与安全科学
