Path Planning using Multiple Random Trees Algorithm Based on Adaptive Step Adjustment

This paper presents a path planning method using multiple random trees algorithm based on adaptive step adjustment(ASM-RRT). The method introduces a strategy for generating random trees, enabling the placement of root nodes at optimal locations within the environment. An adaptive step adjustment approach is employed to accelerate the exploration process. Additionally, a local search strategy for adjacent points is proposed, defining the search range around the center of each newly generated node to identify more optimal paths. The effectiveness of the method is validated through simulation experiments in two-dimensional and manipulator path planning environments. In comparison to the RRT and RRT-Connect algorithms, the proposed method exhibites superior performance. The manipulator experiment is carried out in physical scenarios to assess the feasibility of the presented method.