Provably Safe Robot Navigation with Obstacle Uncertainty
Name
axelrod-rss-17.pdf
Description
Accepted version
Size
3.63 MB
Format
Adobe PDF
Checksum (MD5)
82f4d5ec6ec4013a1ff27ea8efb5fd65
Author(s)
Axelrod, Brian
Kaelbling, Leslie
Lozano-Perez, Tomas
Date Issued
July 12, 2017
Publisher
Robotics: Science and Systems Foundation
Citation
Axelrod, Brian, Kaelbling, Leslie and Lozano-Perez, Tomas. 2017. "Provably Safe Robot Navigation with Obstacle Uncertainty."
Version
Author's final manuscript
Abstract
© 2017 MIT Press Journals. All rights reserved. As drones and autonomous cars become more widespread it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy is safe with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We present efficient algorithms that can prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method's ability to evaluate if a trajectory or policy is safe. We then use these safety checking methods to design a safe variant of the RRT planning algorithm.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.15607/rss.2017.xiii.023
