Research on Mobile Robot Path Planning Based on the Weighted A* Algorithm
DOI:
https://doi.org/10.54097/69bsaj61Keywords:
Path planning; A* algorithm; Heuristic search.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.
Downloads
References
[1] Sanchez-Ibanez J R, Pérez-del-Pulgar C J, García-Cerezo A. Path planning for autonomous mobile robots: A review. Sensors, 2021, 21(23): 7898.
[2] Aggarwal S, Kumar N. Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer communications, 2020, 149: 270-299.
[3] Liu L, Wang X, Yang X, et al. Path planning techniques for mobile robots: Review and prospect. Expert Systems with Applications, 2023, 227: 120254.
[4] Zhang H, Lin W, Chen A. Path planning for the mobile robot: A review. Symmetry, 2018, 10(10): 450.
[5] Karur K, Sharma N, Dharmatti C, et al. A survey of path planning algorithms for mobile robots. Vehicles, 2021, 3(3): 448-468.
[6] Guruji A K, Agarwal H, Parsediya D K. Time-efficient A* algorithm for robot path planning. Procedia Technology, 2016, 23: 144-149.
[7] Fu B, Chen L, Zhou Y, et al. An improved A* algorithm for industrial robot path planning with a high success rate and short length. Robotics and Autonomous Systems, 2018, 106: 26-37.
[8] Zidane I M, Ibrahim K. Wavefront and a-star algorithms for mobile robot path planning. In Proceedings Of The International Conference On Advanced Intelligent Systems And Informatics 2017. Springer International Publishing, 2018, pp. 69-80.
[9] Korkmaz M, Durdu A. Comparison of optimal path planning algorithms. In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). IEEE, 2018, pp. 255-258.
[10] Ebendt R, Drechsler R. Weighted * A search–unifying view and application. Artificial Intelligence, 2009, 173(14): 1310-1342.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Highlights in Science, Engineering and Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







