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A Global Path Planning Method for Mobile Robots Based on Multiple Improvements of A

Traditional A* algorithm faces challenges in the context of complex environments, optimal solution selection, and non-smooth paths when dealing with the path planning problem for mobile robots. To address these issues, several improvements have been made to the classical A* algorithm, incorporating...

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Main Authors:	Chengyu, Liu, Xun, Li, Yiming, Wei, Jinsuo, Wang, Jinguo, Zhou
Format:	Conference Proceeding
Language:	English
Subjects:	A Algorithm Bayesian Curve Heuristic algorithms Heuristic Function Mobile Robot Mobile robots Path planning Planning Runtime Time factors Turning
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creator	Chengyu, Liu Xun, Li Yiming, Wei Jinsuo, Wang Jinguo, Zhou
description	Traditional A* algorithm faces challenges in the context of complex environments, optimal solution selection, and non-smooth paths when dealing with the path planning problem for mobile robots. To address these issues, several improvements have been made to the classical A* algorithm, incorporating dynamic weighting coefficients, optimizing heuristic functions, and employing Bayesian curve-smoothed paths. These modifications enable the algorithm to achieve favorable search results while considering time constraints. Experimental results demonstrate a significant advantage of the improved algorithm in terms of path length, with a reduction of 1.6 seconds in robot runtime compared to the classical A* algorithm under equivalent conditions, representing a 5% decrease in runtime. Additionally, in local areas with frequent sharp turns and large angles, the robot experiences noticeable speed reduction, effectively avoiding oscillations and potential collisions with walls.
doi_str_mv	10.23919/CCC63176.2024.10662456
format	conference_proceeding
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identifier	EISSN: 1934-1768
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language	eng
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subjects	A Algorithm Bayesian Curve Heuristic algorithms Heuristic Function Mobile Robot Mobile robots Path planning Planning Runtime Time factors Turning
title	A Global Path Planning Method for Mobile Robots Based on Multiple Improvements of A
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