Research on an Adaptive RRT Path Planning Algorithm Based on Regional Sampling
Yu Su, Hongbing Li, Wenbin Gong, Honghao Chen, Yutao Jiang, Boxuan Long · 2024
An enhanced RRT path planning algorithm based on regional sampling is proposed with the goal of addressing the issues of inadequate guidance, discontinuous sampling, and unsatisfactory path in RRT algorithm path search in complicated situations. The improved algorithm first enhances the algorithm’s guidance and avoids unnecessary sampling by using a target bias strategy and a regional sampling strategy. Secondly, an adaptive step size strategy based on obstacle factors is adopted to dynamically adjust the step size by detecting the complexity of the environment around the expansion direction. Finally, the produced path is trimmed and optimized. According to simulation results, the revised algorithm reduces running time, path length, and number of path nodes by 68.65%, 18.56%, and 85.45%, respectively, in comparison to the RRT algorithm. The enhanced approach reduces the running time, path length, and number of path nodes by 14.06%, 12.91%, and 82.97%, respectively, in comparison to the Bias RRT algorithm.