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Safe and efficient high dimensional motion planning in space-time with time parameterized prediction

Author(s)
Li, Shen; Shah, Julie A
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
In this work, we propose an algorithm that can plan safe and efficient robot trajectories in real time, given time-parameterized motion predictions, in order to avoid fast-moving obstacles in human-robot collaborative environments. Our algorithm is able to reduce the robot configuration space and the time domain significantly by constructing a Lazy Safe Interval Probabilistic Roadmap based on a pre-planned path. The algorithm then plans efficient obstacle-avoidance strategies within the space-time roadmap. We benchmarked our algorithm by evaluating the performance of a simulated 6-joint manipulator attempting to avoid a quickly moving human hand, using a dataset collected from human experiments. We compared our algorithm's performance with those of 8 variations of prior state-of-the-art planners. Results from this empirical evaluation indicate that our method generated safe plans in 97.5% of the evaluated situations, achieved a planning speed 30 times faster than the benchmarked methods that planned in the time domain without space reduction, and accomplished the minimal solution execution time among the benchmarked planners with a similar planning speed.
Date issued
2019
URI
https://hdl.handle.net/1721.1/125893
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Citation
Li, Shen, and Julie A. Shah, "Safe and efficient high dimensional motion planning in space-time with time parameterized prediction." 2019 International Conference on Robotics and Automation (ICRA), May 20-24, 2019, Montreal, QC, IEEE, 2019: 5012-18 doi 10.1109/ICRA.2019.8793580 ©2019 Author(s)
Version: Author's final manuscript
ISBN
978-1-5386-6027-0
ISSN
2577-087X

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