Path Planning Based on Mixed Algorithm of RRT and Artificial Potential Field Method

Aiming at the problems of random sampling and low efficiency of the path planning algorithm of rapidly-exploring random trees (RRT), an improved algorithm combined with the artificial potential field method (APF) is proposed. This method first introduces a probability value in the expansion step of the random tree in the basic RRT algorithm to speed up the convergence of the random tree to the target node and adds a gravitational component to the random tree to guide the tree to grow towards the target point to speed up the search process. Establish a repulsion field around obstacles to limit the search area between obstacles and reduce the randomness of the path. In this research, in addition to the RRT algorithm, the RRT* algorithm will also be used for improvement. The simulation experiment results show that the proposed method is significantly optimized in time, path length and the number of iterations.