Mobile Robot Path Planning Based on Search-step Optimized A* Algorithm

doi:10.16182/j.issn1004731x.joss.23-1574

Abstract

Abstract:

A search-step optimized A* algorithm is proposed to address the issues with the traditional A* algorithm in robot path planning tasks, such as the high time consumption in large-scale high-resolution maps and the poor paths qualitys. Based on the cubic Hermite curve, a set of search steps (the path edges connecting the current node to its successors) is constructed, which can match the size of the robot and satisfy the dynamic constraints of the robot. More accurate cost functions are established based on the length and maximum absolute curvature value of the curve. Experimental results show that compared with the A* algorithm, the planning time is reduced by an average of 51.83% , and the movement time of the robot execution path is reduced by an average of 14.07% . Compared with the Hybrid A* algorithm, the average planning time is reduced by an average of 67.65%, while the movement time is similar. The results prove that the search-step optimized A* algorithm not only improves search efficiency, but also enhances the robot's performance by improving path quality.

Key words: mobile robot, path planning, A* algorithm, parametric curve, search-step set

CLC Number:

TP242.6

Yu Die, Bao Baizhong, Si Yan, Duan Jian, Zhan Xiaobin, Shi Tielin. Mobile Robot Path Planning Based on Search-step Optimized A* Algorithm[J]. Journal of System Simulation, 2025, 37(4): 1041-1050.

Figures/Tables 13

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