Improved RRT Path Planning Algorithm Based on Multiple Sampling Heuristic Strategy
In the field of path planning,rapid exploration random tree(RRT)algorithms are important tools for robotic arms to solve path planning in complex environments.However,their purely random sampling process results in a large number of invalid or inefficient attempts,wasting computational resources.To solve this problem,an improved RRT algorithm based on multiple sampling heuristic strategy(MH-RRT)is proposed.Firstly,the heuristic function strategy is used to evaluate the proxy value of multiple sam-pling points,select the sampling point with the lowest proxy value,and intruduce the path tree to grow faster towards the target point;Then,the heuristic function strategy is effectively improved similarly to the RRT*algorithm and the bidirectional RRT*al-gorithm;Finally,the impact of different parameters on the performance of the improved algorithm is thoroughly explored,and the optimal parameter combination is determined.Experimental results show that the improved algorithm can significantly improve the time efficiency,path length,and number of sampling points in path search,thereby enhancing the effectiveness of path planning.
NETL
NSTL
万方数据
