Zhang X, Lütteke F, Ziegler C, Franke J (2016)
Publication Type: Conference contribution
Publication year: 2016
Publisher: Springer Verlag
Book Volume: 302
Pages Range: 57-69
Conference Proceedings Title: Advances in Intelligent Systems and Computing
Event location: Padova, ITA
ISBN: 9783319083377
DOI: 10.1007/978-3-319-08338-4_5
RRT* is a practical and efficient incremental sampling-based motion planning algorithm. However, its searching ability is quite inefficient in some cases, due to relying on uniform random sampling like other RRT-based algorithms without taking the environment information and prior knowledge into account, which particularly leads to many sampling failures or generation of useless nodes in complex environments. In this paper, we propose an extension of RRT* based on a self-learning strategy and a hybrid-biased sampling scheme to improve the planning efficiency. By taking advantage of the prior knowledge accumulation and cost estimation, the searching tree has higher probability and success rate to extend in difficult areas. We also demonstrate the performance of our algorithm by building some simulation environments for our mobile robot and conclude with the results compared with RRT*.
APA:
Zhang, X., Lütteke, F., Ziegler, C., & Franke, J. (2016). Self-learning RRT* Algorithm for mobile robot motion planning in complex environments. In Hiroaki Yamaguchi, Nathan Michael, Karsten Berns, Emanuele Menegatti (Eds.), Advances in Intelligent Systems and Computing (pp. 57-69). Padova, ITA: Springer Verlag.
MLA:
Zhang, Xu, et al. "Self-learning RRT* Algorithm for mobile robot motion planning in complex environments." Proceedings of the 13th International Conference on Intelligent Autonomous Systems, IAS 2014, Padova, ITA Ed. Hiroaki Yamaguchi, Nathan Michael, Karsten Berns, Emanuele Menegatti, Springer Verlag, 2016. 57-69.
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