Motion Planning of Mobile Robot Based on Goal-orientated Hierarchical Smoothing Optimization JPS Algorithm

Due to lack of direction guidance in the path search process of JPS (jump point search) algorithm, there are many redundant points, turning points and uneven paths on the planned path. Therefore, a GHSO-JPS (goal-oriented hierarchical smoothing optimization JPS) algorithm is proposed. Besides that, the multi-segment polynomial method and the collision penalty algorithm based on repulsive potential field are applied to trajectory optimization. By the attractive potential field, the search is focused on the goal direction, and irrelevant jump points in the search are reduced. Then, a hierarchical smoothing optimization strategy is adopted to eliminate redundant points and turning points in the path. Besides that, both the path point amount and the path length are reduced while improving the path smoothness. Finally, the multi-segment polynomial method is used to optimize the trajectory, and the penalty function against trajectory collision based on repulsive potential field is built to improve the trajectory safety. The real vehicle experiments are executed in the areas of laboratory and university, to analyze the proposed algorithm and compare with JPS, A∗, RRT (rapidly-exploring random tree) algorithms. As the result, the proposed alogrithm shows the best performances among the 4 algorithms. Compared with the traditional JPS algorithm, the search time of the proposed algorithm is reduced by 41.6%, the amount of jump points is reduced by 55.5%, the path length is reduced by 3.22 m, and number of path points and total turning angle are reduced by 89.8% and 81.8% respectively, in the university area. Compared with A∗ and RRT algorithms, the planning time of the proposed algorithm is reduced by 70.4% and 93.7% respectively, and the total turning angle of the planned optimal path is reduced by 90.3% and 97.1% respectively. In conclusion, the proposed algorithm is of a better path planning performance, a higher efficiency and a better ability of trajectory optimization than the traditional motion planning methods. © 2023 Chinese Academy of Sciences. All rights reserved.