Chance Constrained Motion Planning for High-Dimensional Robots
Name
ICRA_2019.pdf
Description
Accepted version
Size
498.16 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Dai, Siyu
Schaffert, Shawn
Jasour, Ashkan
Hofmann, Andreas
Williams, Brian C
Date Issued
August 2019
Journal
Proceedings - IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Dai, Siyu, Schaffert, Shawn, Jasour, Ashkan, Hofmann, Andreas and Williams, Brian C. 2019. "Chance Constrained Motion Planning for High-Dimensional Robots." Proceedings - IEEE International Conference on Robotics and Automation, 2019-May.
Version
Author's final manuscript
Abstract
© 2019 IEEE. This paper introduces Probabilistic Chekov (p-Chekov), a chance-constrained motion planning system that can be applied to high degree-of-freedom (DOF) robots under motion uncertainty and imperfect state information. Given process and observation noise models, it can find feasible trajectories which satisfy a user-specified bound over the probability of collision. Leveraging our previous work in deterministic motion planning which integrated trajectory optimization into a sparse roadmap framework, p-Chekov shows superiority in its planning speed for high-dimensional tasks. P-Chekov incorporates a linear-quadratic Gaussian motion planning approach into the estimation of the robot state probability distribution, applies quadrature theories to waypoint collision risk estimation, and adapts risk allocation approaches to assign allowable probabilities of failure among waypoints. Unlike other existing risk-aware planners, p-Chekov can be applied to high-DOF robotic planning tasks without the convexification of the environment. The experiment results in this paper show that this p-Chekov system can effectively reduce collision risk and satisfy user-specified chance constraints in typical real-world planning scenarios for high-DOF robots.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2019.8793660
