A real-time dynamic path planning method combining artificial potential field method and biased target RRT algorithm

Abstract In order to improve the efficiency and effectiveness of path planning for mobile robots in dynamic environment, based on the comprehensive analysis of multiple path planning algorithms, an improved algorithm based on fast-spreading random tree algorithm is proposed. The proposed algorithm effectively combines the artificial potential field(APF) method with the biased target RRT algorithm, and judges the local minimum point in the process of solving the APF method to complete the switching with the biased target RRT algorithm. The motion environment simulation model is established in the Python environment, and the path planning simulation case of the mobile robot in the dynamic environment is completed. The simulation results show that the combined algorithm can avoid the phenomenon that the APF method falls into the local minimum point while taking the global and real-time performance into account, reduce the planning time of the biased target RRT algorithm, and improve the quality of path generation.