Research on path planning of mobile robot in complex environment

Abstract To address inefficiencies in search performance, slow convergence, and redundant node generation in mobile robot path planning within complex environments, this paper introduces an enhanced A* pathfinding algorithm. The proposed algorithm improves search efficiency and accuracy by segmentin...

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Main Authors: Haibin Liu, Jingjing Cao, Zhiyuan Wang
Format: Article
Language:English
Published: Springer 2025-04-01
Series:Discover Applied Sciences
Subjects:
Online Access:https://doi.org/10.1007/s42452-025-06713-y
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author Haibin Liu
Jingjing Cao
Zhiyuan Wang
author_facet Haibin Liu
Jingjing Cao
Zhiyuan Wang
author_sort Haibin Liu
collection DOAJ
description Abstract To address inefficiencies in search performance, slow convergence, and redundant node generation in mobile robot path planning within complex environments, this paper introduces an enhanced A* pathfinding algorithm. The proposed algorithm improves search efficiency and accuracy by segmenting the path planning process into distinct stages, applying different heuristic functions at each stage, and integrating an artificial potential field to guide traversal, reducing unnecessary node exploration. Additionally, a random escape strategy prevents the algorithm from getting trapped in local minima. Various optimization methods refine the final path for practical applications. Simulation results demonstrate that, compared to heuristic A*, potential field, Weighted A*, and D* algorithms, the improved approach significantly reduces node traversal, execution time, and enhances planning success rates, making it well-suited for complex environments.
format Article
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institution DOAJ
issn 3004-9261
language English
publishDate 2025-04-01
publisher Springer
record_format Article
series Discover Applied Sciences
spelling doaj-art-7a342277629a4e608ff9c68eb38f21592025-08-20T03:10:13ZengSpringerDiscover Applied Sciences3004-92612025-04-017411810.1007/s42452-025-06713-yResearch on path planning of mobile robot in complex environmentHaibin Liu0Jingjing Cao1Zhiyuan Wang2Hebei University of EngineeringHebei University of EngineeringHebei University of EngineeringAbstract To address inefficiencies in search performance, slow convergence, and redundant node generation in mobile robot path planning within complex environments, this paper introduces an enhanced A* pathfinding algorithm. The proposed algorithm improves search efficiency and accuracy by segmenting the path planning process into distinct stages, applying different heuristic functions at each stage, and integrating an artificial potential field to guide traversal, reducing unnecessary node exploration. Additionally, a random escape strategy prevents the algorithm from getting trapped in local minima. Various optimization methods refine the final path for practical applications. Simulation results demonstrate that, compared to heuristic A*, potential field, Weighted A*, and D* algorithms, the improved approach significantly reduces node traversal, execution time, and enhances planning success rates, making it well-suited for complex environments.https://doi.org/10.1007/s42452-025-06713-yMove robotImproved algorithmPath planningComplex environment
spellingShingle Haibin Liu
Jingjing Cao
Zhiyuan Wang
Research on path planning of mobile robot in complex environment
Discover Applied Sciences
Move robot
Improved algorithm
Path planning
Complex environment
title Research on path planning of mobile robot in complex environment
title_full Research on path planning of mobile robot in complex environment
title_fullStr Research on path planning of mobile robot in complex environment
title_full_unstemmed Research on path planning of mobile robot in complex environment
title_short Research on path planning of mobile robot in complex environment
title_sort research on path planning of mobile robot in complex environment
topic Move robot
Improved algorithm
Path planning
Complex environment
url https://doi.org/10.1007/s42452-025-06713-y
work_keys_str_mv AT haibinliu researchonpathplanningofmobilerobotincomplexenvironment
AT jingjingcao researchonpathplanningofmobilerobotincomplexenvironment
AT zhiyuanwang researchonpathplanningofmobilerobotincomplexenvironment