Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (10): 2912-2918.

Special Issue: 先进计算

### Optimal path convergence method based on artificial potential field method and informed sampling

1. School of Instrument Science and Engineering, Southeast University, Nanjing Jiangsu 210096, China
• Received:2020-12-23 Revised:2021-03-06 Online:2021-10-10 Published:2021-07-14

### 基于人工势场法和启发式采样的最优路径收敛方法

1. 东南大学 仪器科学与工程学院, 南京 210096
• 通讯作者: 金世俊
• 作者简介:李伟(1993-),男,安徽马鞍山人,硕士研究生,主要研究方向:移动机器人导航、路径规划;金世俊(1969-),男,江苏南京人,副教授,博士,主要研究方向:汽车离合部件研究和检测、移动机器人导航、水声测试系统。

Abstract: The Rapidly exploring Random Tree star (RRT*) algorithm ensures its probabilistic completeness and asymptotic optimality in the path planning process, but still has problems such as slow convergence speed and large and dense sampling space. In order to speed up the convergence of the algorithm, a fast obtaining method of optimal path based on artificial potential field method and informed set sampling was proposed. First, the artificial potential field method was used to construct an initial path from the starting point to the target point. Then, the positions of and the distance between the starting point and the target point as well as the path cost of the initial path were used as parameters to construct the initial informed sampling set. At last, the sampling was limited in the informed set, and the range of the informed sampling set was adjusted during the running process of the algorithm to accelerate the path convergence speed. Simulation experiments show that, Potential Informed-RRT* (PI-RRT*) algorithm based on the artificial potential field combined with informed sampling method reduces the number of sampling points by about 67%, and shortens the algorithm running time by about 74.5% on average compared with RRT* algorithm; and has the number of sampling points reduced by about 40%-50%, the algorithm running time shortened by about 62.5% on average compared with Informed RRT* (Informed-RRT*) algorithm. The proposed optimal path convergence method greatly reduces the number of redundant sampling and the algorithm running time, has higher algorithm efficiency, and converges to the optimal path with faster speed.

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