Path Planning of Mobile Robot Based on the Integration of Multi-scale A* and Optimized DWA Algorithm

Authors

Jianmin Xu, Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China; Fujian Provincial Key Laboratory of Advanced Design and Manufacturing for Bus Coach, Xiamen 361024, China
Lei Song, Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China; Fujian Provincial Key Laboratory of Advanced Design and Manufacturing for Bus Coach, Xiamen 361024, China
Dongdong Deng, Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China; Fujian Provincial Key Laboratory of Advanced Design and Manufacturing for Bus Coach, Xiamen 361024, China
Yaoruo Chen, Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China; Fujian Provincial Key Laboratory of Advanced Design and Manufacturing for Bus Coach, Xiamen 361024, China
Wei Yang, Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China; Fujian Provincial Key Laboratory of Advanced Design and Manufacturing for Bus Coach, Xiamen 361024, China

Abstract

Abstract: In order to solve the problems of sharply increasing computational and time costs, as well as poor flexibility of the traditional A* algorithm and dynamic window approach (DWA) in the face of largescale complex environmental path planning, a fusion algorithm based on the A* algorithm of the multiscale map approach(MMA) and the improved DWA algorithm is proposed. A multi-scale map set is established and an obstacle proportion factor is added to the heuristic function of the A* algorithm. The A* algorithm is used to calculate the optimal path on the coarse-scale map, and the optimal path is mapped onto the fine-scale map for quadratic A* algorithm planning. The Floyd algorithm is used to optimize the nodes, remove redundant nodes, and improve the smoothness of the path. In addition, the heading angle adaptive adjustment strategy and parking wait state are added to optimize the dynamic window method to improve flexibility. The key points of the A* algorithm are used as local target points of the dynamic window method and replanned when there are obstacles on the path to realize the integration of the two algorithms. The results of ROS simulation and actual vehicle experiments show that the computation time of the improved A* algorithm is significantly reduced by 98% in 20×40 maps and the improved fusion algorithm dramatically improves the smoothing and flexibility of the robot in dynamic environments, and can effectively solve the problems existing in the traditional fusion algorithm

Recommended Citation

Xu, Jianmin; Song, Lei; Deng, Dongdong; Chen, Yaoruo; and Yang, Wei (2025) "Path Planning of Mobile Robot Based on the Integration of Multi-scale A* and Optimized DWA Algorithm," Journal of System Simulation: Vol. 37: Iss. 1, Article 21.
DOI: 10.16182/j.issn1004731x.joss.23-1089
Available at: https://dc-china-simulation.researchcommons.org/journal/vol37/iss1/21

First Page

257

Last Page

270

CLC

TP242.6

Recommended Citation

Xu Jianmin, Song Lei, Deng Dongdong, et al. Path Planning of Mobile Robot Based on the Integration of Multi-scale A* and Optimized DWA Algorithm[J]. Journal of System Simulation, 2025, 37(1): 257-270.

DOI

10.16182/j.issn1004731x.joss.23-1089