PSO-RRT is proposed to solve some problems of low sampling rate and long search time in the traditional RRT algorithm for feasible path search in static obstacle environments.PSO-RRT,a robot feasible path search algorithm,is proposed by integrating PSO(Particle Swarm Optimization)algorithm and RRT(Rapidly Exploring Random Tree)algorithm.In order to reduce the number of iterations and search time of the RRT algorithm and improve search efficiency,the PSO-RRT algorithm introduces a sampling rejection rate parameter to change the random sampling method,and uses PSO algorithm to optimize parameters such as the random sampling rejection rate and extension step size.The simulation experiments have verified the effectiveness of the PSO-RRT fusion algorithm proposed in this paper in three different obstacle environments,and the experimental results illustrate that the PSO-RRT algorithm outperforms the comparison algorithm in terms of iteration times,search time,and path length.
万方数据
维普
