Vortex Artificial-Potential-Field Guided RRT* for Path Planning of Mobile Robot

The rapidly-exploring random tree (RRT) has the problems of slow convergence, dense sampling nodes, and complicated path twists under the condition of dense obstacles and narrow channels. In this paper, a common variant of the RRT algorithm, RRT*, is designed with atificial potential fields (APF) to guide RRT* for path planning. First, vortex is used to constrain the repulsive field that diverges outward to form a vortex field along the tangential gradient. And vortex-APF (VAPF) is used to guide the sampling node to perform directional sampling in the RRT* deflection area, so as to reduce the execution time and accelerate the convergence speed. Simultaneously, the node rejection is used to remove high cost nodes and invalid nodes and a more centralized tree can be generated to reduce memory requirements. Finally, the extra nodes are pruned and the path is smoothed by vortex potential field to achieve the path optimization. Considering that the RRT algorithm has probabilistic randomness, 32 experi-ments are carried out on the RRT, the improved RRT* and the VAPF-RRT* respectively. The simulation results show that the VAPF-RRT* method significantly reduces the number of iterations, converges to shorter and smoother paths with fewer sampling nodes and execution time, and thus leads to more efficient memory utilization and an accelerated convergence rate