Robot path planning optimization method based on heuristic multi-directional rapidly-exploring tree

Abstract

Abstract Aiming at the problems of low exploration efficiency and high optimal solution cost for existing robot path planning methods, a robot path planning optimization method based on heuristic multi-directional rapidly-exploring tree is implemented. In high-dimensional configuration space, drawing on the Rapid-exploration Random Tree (RRT) related algorithm idea, directional sampling control module works under the guidance of the robot goal course as a heuristic exploration. A flexible multi-directional rapidly-exploring tree construction method is used due to a degree of directional instability. In accordance with the principle of the centripetal growth of the tree, new multi-directional trees will be built on demand to arrive at specific coverage of the space. Then based on the previous path exploration vertices, through the merging method of the trees, a closed loop path is formed and optimized to finally generate a relative optimal path. Simulation experiment results show that this method could effectively improve the exploring efficiency with low computational cost.