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A Comparative Study of AStar, LPA* and DStarLite Path Planning Algorithms Based on Matlab
- 1 Institute of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing, 100000, China
* Author to whom correspondence should be addressed.
Abstract
In the realm of robotics and autonomous systems, path planning is a pivotal component that determines the efficacy and safety of navigational tasks. With the proliferation of autonomous vehicles, drones, and mobile robots, the need for efficient and adaptive path planning algorithms has become increasingly acute. This paper studies AStar, LPA and DStarLite path planning algorithms based on Matlab platform, and compares their performance through simulation experiments. AStar algorithm is simple and widely applicable, but it has some shortcomings in path smoothness and computational efficiency. LPA improves path smoothness by introducing dynamic cost updating, but it may sacrifice some computational efficiency. The DStarLite algorithm performs well in dynamic environments with an efficient incremental update strategy that maintains high path smoothness and low computational costs. The experimental results show that DStarLite is the fastest in most cases, LPA* and DStarLite are superior to AStar in path smoothness. Future research may explore combining the advantages of each algorithm to develop more efficient, flexible and robust path planning algorithms to cope with complex and changeable actual scenarios.
Keywords
AStar, LPA*, DStarLite, Matlab, path planning
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Cite this article
Huang,Z. (2024). A Comparative Study of AStar, LPA* and DStarLite Path Planning Algorithms Based on Matlab. Applied and Computational Engineering,80,35-42.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-MLA Workshop: Mastering the Art of GANs: Unleashing Creativity with Generative Adversarial Networks
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