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Motion planning in semistructured environments with teaching roadmaps

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Abstract

Motion planning is a hot topic in robotics, and the sampling-based algorithms have gained their popularities in research areas. However, these methods are still not suitable for real-world motion planning problems, because it is computationally expensive to completely explore the high-dimensional configuration space (C-space) of robots. Inspired by the related works on learning from demonstration, we propose a novel motion planning method named teaching roadmaps, which can take advantage of the optimal teaching data and quickly find a new path in the similar scenarios. The theoretical analysis and our experiments indicated that our approach is probabilistically complete, and it can find a feasible path faster than other sampling-based methods in similar environments.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under Grant No. 61673261 and YASKAWA Electric Corporation.

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  1. Research Institute of Robotics, Shanghai Jiao Tong University, No. 800 Dong Chuan Road, Shanghai, People’s Republic of China

    Qiang Qiu & Qixin Cao

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  1. Qiang Qiu
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  2. Qixin Cao
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Correspondence to Qiang Qiu.

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Qiu, Q., Cao, Q. Motion planning in semistructured environments with teaching roadmaps. Intel Serv Robotics 13, 331–342 (2020). https://doi.org/10.1007/s11370-020-00316-9

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  • DOI: https://doi.org/10.1007/s11370-020-00316-9

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