Research on Mobile Robot Path Planning Based on the Weighted A* Algorithm

The path planning problem is crucial in different areas like autonomous navigation and so on and has a wide range of applications. The A* algorithm is a classic way to solve the path planning problem, able to give the optimal path within a relatively short period. However, in some situations, the A* algorithm requires a long time to find the solution. In the weighted A* algorithm, a weight factor w is introduced to enlarge the role of heuristic estimation in the algorithm, allowing a faster searching process while giving back a path of acceptable length. In the experiment, the weighted A* algorithm was run multiple times under different values of w in two different maps to evaluate the influence of w on the final result and the algorithm’s performance in different conditions. It was found that with a weight factor > 1, generally, the path length became longer, and computational time became quicker. The research illustrates the influence of the value of w in the weighted A* algorithm, allowing people to adjust the value of w that well meets their demands.

Authors

Publication Details

Journal
Highlights in Science Engineering and Technology
Published
2024-12-25
DOI
https://doi.org/10.54097/69bsaj61
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on Mobile Robot Path Planning Based on the Weighted A* Algorithm

Zuhao Teng
Highlights in Science Engineering and Technology
Robotic Path Planning Algorithms
article

Research on Mobile Robot Path Planning Based on the Weighted A* Algorithm

Zuhao Teng
article en

Abstract

The path planning problem is crucial in different areas like autonomous navigation and so on and has a wide range of applications. The A* algorithm is a classic way to solve the path planning problem, able to give the optimal path within a relatively short period. However, in some situations, the A* algorithm requires a long time to find the solution. In the weighted A* algorithm, a weight factor w is introduced to enlarge the role of heuristic estimation in the algorithm, allowing a faster searching process while giving back a path of acceptable length. In the experiment, the weighted A* algorithm was run multiple times under different values of w in two different maps to evaluate the influence of w on the final result and the algorithm’s performance in different conditions. It was found that with a weight factor > 1, generally, the path length became longer, and computational time became quicker. The research illustrates the influence of the value of w in the weighted A* algorithm, allowing people to adjust the value of w that well meets their demands.

Highlights in Science Engineering and TechnologyVol. 120
Openalex Percentile: Top 75%
Robotic Path Planning Algorithms
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.