Provably Safe Trajectory Optimization in the Presence of Uncertain Convex Obstacles
Name
2003.07811.pdf
Description
Submitted version
Size
845.47 KB
Format
Adobe PDF
Checksum (MD5)
ac61b4da92f958cfd27389461c37ef01
Author(s)
Dawson, Charles
Jasour, Ashkan
Hofmann, Andreas
Williams, Brian
Date Issued
2020
Journal
IEEE International Conference on Intelligent Robots and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Dawson, Charles, Jasour, Ashkan, Hofmann, Andreas and Williams, Brian. 2020. "Provably Safe Trajectory Optimization in the Presence of Uncertain Convex Obstacles." IEEE International Conference on Intelligent Robots and Systems.
Version
Original manuscript
Abstract
© 2020 IEEE. Real-world environments are inherently uncertain, and to operate safely in these environments robots must be able to plan around this uncertainty. In the context of motion planning, we desire systems that can maintain an acceptable level of safety as the robot moves, even when the exact locations of nearby obstacles are not known. In this paper, we solve this chance-constrained motion planning problem using a sequential convex optimization framework. To constrain the risk of collision incurred by planned movements, we employ geometric objects called ϵ-shadows to compute upper bounds on the risk of collision between the robot and uncertain obstacles. We use these ϵ-shadow-based estimates as constraints in a nonlinear trajectory optimization problem, which we then solve by iteratively linearizing the non-convex risk constraints. This sequential optimization approach quickly finds trajectories that accomplish the desired motion while maintaining a user-specified limit on collision risk. Our method can be applied to robots and environments with arbitrary convex geometry; even in complex environments, it runs in less than a second and provides provable guarantees on the safety of planned trajectories, enabling fast, reactive, and safe robot motion in realistic environments.
MIT Department
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/IROS45743.2020.9341193
