Download pdf
Exploring the Comparison between Dijkstra's Algorithm and Rapidly-exploring Random Trees Algorithm for Robot Path Planning
- 1 Faculties of Engineering, University of Bristol, Bristol, BS8 1QU, UK
* Author to whom correspondence should be addressed.
Abstract
Since the inception and evolution of artificial intelligence, control systems, particularly automated pathfinding, have constituted a pivotal area of significance. It is reflected in today's world of life in all aspects of autonomous driving, rescue robots and so on. Algorithms as the basis for the realization of this field exist in thousands of situations, which are reflected in different logic, different computational efficiency, different time and space complexity, etc. We will introduce the development history and basic principles of Dijkstra’s algorithm and Rapidly-exploring Random Trees algorithm, and analyze the advantages and disadvantages of each of these two algorithms to determine the domains in which they are applicable. In this paper, we will use MATLAB to set up a test environment and will investigate the effect of different environments comparing Dijkstra’s algorithm and RRT algorithm on the automatic control system in artificial intelligence. In addition, some experimental data and icons will be cited to support the experimental results by comparing the algorithms in terms of distance and efficiency. It is concluded that Dijkstra’s algorithm will be more suitable for static and low dimensional environments, while Rapidly-exploring Random Trees algorithm will be suitable for more complex and high dimensional environments.
Keywords
Pathfinding Algorithms, Dijkstra’s Algorithm, Rapidly-exploring Random Trees, MATLAB Simulation
[1]. Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. (2024). Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions. IEEE Access.
[2]. Lin, Y. (2024). Dijkstra and A* algorithms in automated vehicle driving. Proceedings of SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130712F. https://doi.org/10.1117/12.3025670.
[3]. Fan, D., & Shi, P. (2010). Improvement of Dijkstra's algorithm and its application in route planning. Seventh International Conference on Fuzzy Systems and Knowledge Discovery, Yantai, China, 1901-1904. https://doi.org/10.1109/FSKD.2010.5569452.
[4]. Behún, M., Knežo, D., Cehlár, M., Knapčíková, L., & Behúnová, A. (2022). Recent application of Dijkstra’s algorithm in the process of production planning. Applied Sciences, 12(14), 7088. https://doi.org/10.3390/app12147088.
[5]. Tian, Y., et al. (2007). Application of RRT-based local path planning algorithm in unknown environment. 2007 International Symposium on Computational Intelligence in Robotics and Automation, Jacksonville, FL, USA, 456-460. https://doi.org/10.1109/CIRA.2007.382896.
[6]. Zhang, H., Wang, Y., Zheng, J., & Yu, J. (2018). Path planning of industrial robot based on improved RRT algorithm in complex environments. IEEE Access, 6, 53296-53306. https://doi.org/10.1109/ACCESS.2018.2871222.
Cite this article
Liu,Y. (2025). Exploring the Comparison between Dijkstra's Algorithm and Rapidly-exploring Random Trees Algorithm for Robot Path Planning. Applied and Computational Engineering,150,143-148.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).