Abstract
To solve the problems of low efficiency and slow convergence of traditional RRT algorithm and RRT* algorithms, an improved informed-RRT* algorithm is proposed in this paper. The algorithm keeps the probability completeness and path optimality of RRT algorithm, improves the speed of iterative convergence and the quality of the generated path. After the final path is obtained, the problem of sharp and burr in the generated trajectory is solved by trajectory smoothing strategy. Finally, the comparison experiment shows that the performance of the proposed algorithm in three-dimensional space is better than RRT* algorithm, and the algorithm is applied to real manipulator, which verifies the feasibility of the algorithm.
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References
LaValle, S.M.: Rapidly-exploring random trees: a new tool for path planning. 98--11 (1988)
Kavraki, L.E., Kolountzakis, M.N., Latombe, J.C.: Analysis of probabilistic roadmaps for path planning. IEEE Trans. Robot. Autom. 14(1), 166–171 (1998)
Kuffner, J., Lavalle, S.: RRT-connect: an efficient approach to single-query path planning. In: Proceedings Millennium Conference IEEE Interantional Conference Robotics Automation Symposia, vol. 2, pp. 995--1001, San Francisco (2000)
Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)
Gammell, J.D., Srinivasa, S.S., Barfoot, T.D.: Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In Proceedings of IEEE/RSJ International Conference Intelligent Robots System, pp. 2997--3004 (2014)
Shan, E., Dai, B., Song, J., Sun, Z.: A dynamic RRT path planning algorithm based on B-Spline. In: 2009 Second International Symposium on Computational Intelligence and Design, pp. 25--29, Changsha, China (2009)
Koyuncu, E., Inalhan, G.: A probabilistic B-Spline motion planning algorithm for unmanned helicopters flying in dense 3d environment. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 815--821 (2008)
Hutton, D.M.: Numerical analysis and optimization. Oxford University Press (2007)
Kim, M., Song, J.: Informed RRT* towards optimality by reducing size of hyperellipsoid. In: 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). pp. 244–248, Busan, Korea (2015)
Chen, L., Shan, Y., Tian, W., Li, B., Cao, D.: A fast and efficient double-tree RRT-like sampling-based planner applying on mobile robotic systems. IEEE/ASME Trans. Mech. 23(6), 2568–2578 (2018)
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Li, Q., Li, N., Miao, Z., Sun, T., He, C. (2021). Path Planning of Manipulator Based on Improved Informed-RRT* Algorithm. In: Han, Q., McLoone, S., Peng, C., Zhang, B. (eds) Intelligent Equipment, Robots, and Vehicles. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1469. Springer, Singapore. https://doi.org/10.1007/978-981-16-7213-2_48
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DOI: https://doi.org/10.1007/978-981-16-7213-2_48
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