Abstract
To address the problems of slow planning speed and too many sharp turns in the planned route, this paper focuses on the optimization of the probabilistic roadmap by searching the neighboring nodes in the composition stage, improving its search efficiency using K-dimensional Tree (KD-TREE), smoothing the planned paths, and ensuring the safety of the planned route by expanding the map obstacles. To test the performance of the improved probabilistic roadmap algorithm, it is compared with the traditional PRM algorithm and the PRM based on the common K-Nearest Neighbor (KNN) algorithm. The simulation results show that the optimized algorithm has a significant improvement in the planning time and the final planned path is a smooth path without inflection points, which is more conducive to the actual walking of the mobile robot. The study has a wide range of applications.
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References
Nannicini, G., Delling, D., Liberti, L., Schultes, D.: Bidirectional A* search for time-dependent fast paths. In: McGeoch, C.C. (ed.) WEA 2008. LNCS, vol. 5038, pp. 334–346. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68552-4_25
Ma’Arif, A., Rahmaniar, W., Vera, M.A.M., et al.: Artificial potential field algorithm for obstacle avoidance in UAV quadrotor for dynamic environment. In: 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), pp. 184–189. IEEE (2021)
Zeng, N., Zhang, H., Chen, Y., et al.: Path planning for intelligent robot based on switching local evolutionary PSO algorithm. Assem. Autom. (2016)
Brand, M., Masuda, M., Wehner, N., et al.: Ant colony optimization algorithm for robot path planning. In: 2010 International Conference on Computer Design and Applications, vol. 3, pp. V3-436–V3-440. IEEE (2010)
Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Queen’s University Belfast (2010)
Wei, K., Ren, B.: A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm. Sensors 18(2), 571 (2018)
Kavraki, L.E., Svestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996)
Mohanta, J.C., Keshari, A.: A knowledge based fuzzy-probabilistic roadmap method for mobile robot navigation. Appl. Soft Comput. 79, 391–409 (2019)
Hsu, D., Latombe: On the probabilistic foundations of probabilistic roadmap planning. Int. J. Robot. Res. (2006)
Ram, P., Sinha, K.: Revisiting kd-tree for nearest neighbor search. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1378–1388 (2019)
Farin, G.: Algorithms for rational Bézier curves. Comput. Aided Des. 15(2), 73–77 (1983)
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Wang, B., Huang, H., Sun, L., Feng, C. (2024). Optimization of Probabilistic Roadmap Based on Two-Dimensional Static Environment. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_35
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DOI: https://doi.org/10.1007/978-3-031-50580-5_35
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