Mobile Robot Path Planning Method Based on Improved A<sup>*</sup> Algorithm and Robot Operating System

As mobile robot technology advances, path planning emerges as a crucial research area. This paper introduces an improved $A^{*}$ algorithm to optimize path planning, addressing the A<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm’s limitations like excessive node expansion, prolonged computation time, and frequent path turning angles, while ensuring safety, efficiency, and stability. The proposed algorithm enhances the $A^{*}$ algorithm’s node expansion by selecting search nodes according to obstacle positions. Then, the heuristic function is dynamically weighted to enhance the search efficiency of the algorithm. Finally, the path is optimized using cubic Bézier curves to ensure smoothness. Experiments in ROS confirm that the proposed algorithm performs well and is suitable as path planning algorithm for mobile robots.

Authors

Institutions

Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1109/aiim64537.2024.10934396
Citations
2
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.50
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mobile Robot Path Planning Method Based on Improved A* Algorithm and Robot Operating System

Feifei Liu, Zhen Zhang, Keke Ming
2 citations
Robotic Path Planning Algorithms
0.50
article

Mobile Robot Path Planning Method Based on Improved A* Algorithm and Robot Operating System

Feifei Liu, Zhen Zhang, Keke Ming
article en
2 citations

Abstract

As mobile robot technology advances, path planning emerges as a crucial research area. This paper introduces an improved $A^{*}$ algorithm to optimize path planning, addressing the A<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm’s limitations like excessive node expansion, prolonged computation time, and frequent path turning angles, while ensuring safety, efficiency, and stability. The proposed algorithm enhances the $A^{*}$ algorithm’s node expansion by selecting search nodes according to obstacle positions. Then, the heuristic function is dynamically weighted to enhance the search efficiency of the algorithm. Finally, the path is optimized using cubic Bézier curves to ensure smoothness. Experiments in ROS confirm that the proposed algorithm performs well and is suitable as path planning algorithm for mobile robots.

Jiangxi University of Science and Technology (CN)
Sustainable cities and communities
Openalex Percentile: Top 32%
Robotic Path Planning Algorithms
0.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.