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Intelligent Service Robotics
Published in: Intelligent Service Robotics 3/2020

26-02-2020 | Original Research Paper

Motion planning in semistructured environments with teaching roadmaps

Authors: Qiang Qiu, Qixin Cao

Published in: Intelligent Service Robotics | Issue 3/2020

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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.
next article Discrete vector fields for 2-D navigation under minimum turning radius constraints

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Metadata
Title
Motion planning in semistructured environments with teaching roadmaps
Authors
Qiang Qiu
Qixin Cao
Publication date
26-02-2020
Publisher
Springer Berlin Heidelberg
Published in
Intelligent Service Robotics / Issue 3/2020
Print ISSN: 1861-2776
Electronic ISSN: 1861-2784
DOI
https://doi.org/10.1007/s11370-020-00316-9

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