Journal of System Simulation
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.
Recommended Citation
Yu, Die; Bao, Baizhong; Si, Yan; Duan, Jian; Zhan, Xiaobin; and Shi, Tielin
(2025)
"Mobile Robot Path Planning Based on Search-step Optimized A* Algorithm,"
Journal of System Simulation: Vol. 37:
Iss.
4, Article 17.
DOI: 10.16182/j.issn1004731x.joss.23-1574
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss4/17
First Page
1041
Last Page
1050
CLC
TP242.6
Recommended Citation
Yu Die, Bao Baizhong, Si Yan, et al. Mobile Robot Path Planning Based on Search-step Optimized A* Algorithm[J]. Journal of System Simulation, 2025, 37(4): 1041-1050.
DOI
10.16182/j.issn1004731x.joss.23-1574
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons