Literature DB >> 33525078

Application improvement of A* algorithm in intelligent vehicle trajectory planning.

Xiaoyong Xiong1, Haitao Min1, Yuanbin Yu1, Pengyu Wang1.   

Abstract

Trajectory planning is one of the key technologies for autonomous driving. A* algorithm is a classical trajectory planning algorithm that has good results in the field of robot path planning. However, there are still some practical problems to be solved when the algorithm is applied to vehicles, such as the algorithm fails to consider the vehicle contours, the planned path is not smooth, and it lacks speed planning. In order to solve these problems, this paper proposes a path processing method and a path tracking method for the A* algorithm. First, the method of configuring safe redundancy space is given considering the vehicle contour, then, the path is generated based on A* algorithm and smoothed using Bessel curve, and the speed is planned based on the curvature of the path. The trajectory tracking algorithm in this paper is based on an expert system and pure tracking theory. In terms of speed tracking, an expert system for the acceleration characteristics of the vehicle is constructed and used as a priori information for speed control, and good results are obtained. In terms of path tracking, the required steering wheel angle is calculated based on pure tracking theory, and the influence factor of speed on steering is obtained from test data, based on which the steering wheel angle is corrected and the accuracy of path tracking is improved. In addition, this paper proposes a target point selection method for the pure tracking algorithm to improve the stability of vehicle directional control. Finally, a simulation analysis of the proposed method is performed. The results show that the method can improve the applicability of the A* algorithm in automated vehicle planning.

Keywords:  A-star ; intelligent vehicle ; motion control ; pure pursuit ; trajectory planning

Year:  2020        PMID: 33525078     DOI: 10.3934/mbe.2021001

Source DB:  PubMed          Journal:  Math Biosci Eng        ISSN: 1547-1063            Impact factor:   2.080


  3 in total

1.  Path planning of a manipulator based on an improved P_RRT* algorithm.

Authors:  Junhui Yi; Qingni Yuan; Ruitong Sun; Huan Bai
Journal:  Complex Intell Systems       Date:  2022-01-21

2.  Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot.

Authors:  Dan Xiang; Hanxi Lin; Jian Ouyang; Dan Huang
Journal:  Sci Rep       Date:  2022-08-02       Impact factor: 4.996

3.  Research on smooth path planning method based on improved ant colony algorithm optimized by Floyd algorithm.

Authors:  Lina Wang; Hejing Wang; Xin Yang; Yanfeng Gao; Xiaohong Cui; Binrui Wang
Journal:  Front Neurorobot       Date:  2022-08-24       Impact factor: 3.493

  3 in total

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