Robot navigation based on improved A* algorithm in dynamic environment
ISSN: 0144-5154
Article publication date: 8 July 2021
Issue publication date: 9 August 2021
Abstract
Purpose
The purpose of this paper is to put forward a path planning method in complex environments containing dynamic obstacles, which improves the performance of the traditional A* algorithm, this method can plan the optimal path in a short running time.
Design/methodology/approach
To plan an optimal path in a complex environment with dynamic and static obstacles, a novel improved A* algorithm is proposed. First, obstacles are identified by GoogLeNet and classified into static obstacles and dynamic obstacles. Second, the ray tracing algorithm is used for static obstacle avoidance, and a dynamic obstacle avoidance waiting rule based on dilate principle is proposed. Third, the proposed improved A* algorithm includes adaptive step size adjustment, evaluation function improvement and path planning with quadratic B-spline smoothing. Finally, the proposed improved A* algorithm is simulated and validated in real-world environments, and it was compared with traditional A* and improved A* algorithms.
Findings
The experimental results show that the proposed improved A* algorithm is optimal and takes less execution time compared with traditional A* and improved A* algorithms in a complex dynamic environment.
Originality/value
This paper presents a waiting rule for dynamic obstacle avoidance based on dilate principle. In addition, the proposed improved A* algorithm includes adaptive step adjustment, evaluation function improvement and path smoothing operation with quadratic B-spline. The experimental results show that the proposed improved A* algorithm can get a shorter path length and less running time.
Keywords
Acknowledgements
This research is supported by National Natural Science Foundation of China (Grant No. 51875445).
Citation
Zhang, L., Zhang, Y., Zeng, M. and Li, Y. (2021), "Robot navigation based on improved A* algorithm in dynamic environment", Assembly Automation, Vol. 41 No. 4, pp. 419-430. https://doi.org/10.1108/AA-07-2020-0095
Publisher
:Emerald Publishing Limited
Copyright © 2021, Emerald Publishing Limited