PRM Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation
Abstract
The problem of motion planning and navigation for mobile robots in complex environments has been a central issue in robotics. Navigating these environments requires sophisticated algorithms that handle obstacles and provide smooth, efficient paths. The Probabilistic Roadmap (PRM) method is a widespread technique in robotics for constructing paths for mobile robot navigation. In this study, we propose a novel path-smoothing method using arc fillets for path planning, building on PRM's foundation in the presence of obstacles. Our method operates in two primary stages to improve path efficiency and quality. The first stage generates the shortest path between the initial and goal states in an obstacle-rich environment using PRM, constructing a straight-line, collision-free route. The second stage smooths corners caused by nodes with arc fillets, ensuring smooth turns and minimizing abrupt changes in direction, resulting in more natural and efficient robot motion. We conducted simulations and tests using various PRM features to evaluate the proposed method. The results indicate that the built route offers a smooth turning motion and quicker, more compact movement while evading obstacles. This study contributes to mobile robot navigation by offering a practical approach to improving pathway quality in challenging environments.
Keywords
Probabilistic Roadmap Method (PRM) planner, Path smoothening, Arc fillet method
References
- Aria, M. (2020). New sampling based planning algorithm for local path planning for autonomous vehicles. Journal of Engineering Science and Technology, 15, 66–76.
- Ayawli, B. B. K., Appiah, A. Y., Nti, I. K., Kyeremeh, F., & Ayawli, E. I. (2021). Path planning for mobile robots using Morphological Dilation Voronoi Diagram Roadmap algorithm. Scientific African, 12, e00745.https://doi.org/10.1016/j.sciaf.2021.e00745
- Bohlin, R., & Kavraki, L. E. (2000). Path planning using Lazy PRM. Proceedings-IEEE International Conference on Robotics and Automation, 1, 521–528. https://doi.org/10.1109/ROBOT.2000.844107
- Canny, J. (1988). The complexity of robot motion planning. MIT Press.
- Cheok, K. C., Iyengar, K., & Oweis, S. (2019). Smooth trajectory planning for autonomous leader-follower robots. Proceedings of 34th International Conference on Computers and Their Applications, CATA 2019, 58, 301–309. https://doi.org/10.29007/n6kt
- Choset, H. M. (2005). Principles of robot motion : theory, algorithms, and implementation. MIT Press.
- Contreras-Cruz, M. A., Ayala-Ramirez, V., & Hernandez-Belmonte, U. H. (2015). Mobile robot path planning using artificial bee colony and evolutionary programming. Applied Soft Computing, 30, 319–328. https://doi.org/https://doi.org/10.1016/j.asoc.2015.01.067
- Davoodi, M., Panahi, F., Mohades, A., & Hashemi, S. N. (2015). Clear and smooth path planning. Applied Soft Computing, 32, 568–579. https://doi.org/https://doi.org/10.1016/j.asoc.2015.04.017
- Elhoseny, M., Tharwat, A., & Hassanien, A. E. (2018). Bezier Curve Based Path Planning in a Dynamic Field using Modified Genetic Algorithm. Journal of Computational Science, 25, 339–350. https://doi.org/https://doi.org/10.1016/j.jocs.2017.08.004
- Gang, L., & Wang, J. (2016). PRM Path planning optimization algorithm research. WSEAS Trans. Syst. Control, 11, 81–85.