Safe and efficient high dimensional motion planning in space-time with time parameterized prediction
Name
Li_ICRA_2019.pdf
Description
Accepted version
Size
3.92 MB
Format
Adobe PDF
Checksum (MD5)
fd68531ce1432f4c5db2bf54307cf62e
Author(s)
Li, Shen
Shah, Julie A
Date Issued
2019
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
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2019.8793580
