Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

Jiqing Chen,Ganwei Cai,Hengyu Li,Hongdu Zhang,Chenzhi Tan,Rongxian Mo

doi:10.1177/17298814211026449

Jiqing Chen, Ganwei Cai + Show 4 more

Open Access

https://doi.org/10.1177/17298814211026449

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Abstract

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

Highlights

With the development and progress of science and technology, the development and applications of robots are becoming increasingly mature
This article proposes a three-neighbor search A* algorithm combined with an artificial potential field to solve the problem of repeated search of useless nodes by the A* algorithm and the local minimum of the artificial potential field method
The artificial potential field is applied to the path planning and smooth trajectory of mobile robots, which can reduce the redundancy of the A* algorithm, so this article introduces part of the artificial potential field algorithm to assist in pathfinding

Summary

Introduction

With the development and progress of science and technology, the development and applications of robots are becoming increasingly mature. When multiple obstacles appear in the potential field space at the same time, the zero potential energy point appears, and the path planning task cannot be performed Weerakoon et al used partial information from obstacles to modify the force field function of the artificial potential field, which overcomes the problem of falling into a local minimum in path planning.. The intermediate target information is obtained through the A* algorithm, combined with the improved artificial potential field, to obtain the global optimal path.. This article proposes a three-neighbor search A* algorithm combined with an artificial potential field to solve the problem of repeated search of useless nodes by the A* algorithm and the local minimum of the artificial potential field method. & ði þ 1; jÞ; ði þ 1; j þ 1Þ; ði; j þ 1Þ; ði À 1; j þ 1Þjði; jÞ 2 E Dði; jÞ 1⁄4 ði À 1; jÞ; ði À 1; j À 1Þ; ði; j À 1Þ; ði þ 1; j À 1Þjði; jÞ 2 E (3)

Methodology

Introduction of partial artificial potential field

Findings

Conclusions