Provably Safe Robot Navigation with Obstacle Uncertainty
© 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...
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| Այլ հեղինակներ: | |
| Ձևաչափ: | Հոդված |
| Լեզու: | English |
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Robotics: Science and Systems Foundation
2021
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| Առցանց հասանելիություն: | https://hdl.handle.net/1721.1/137695 |
| _version_ | 1826214967648452608 |
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| author | Axelrod, Brian Kaelbling, Leslie Lozano-Perez, Tomas |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Axelrod, Brian Kaelbling, Leslie Lozano-Perez, Tomas |
| author_sort | Axelrod, Brian |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T16:14:04Z |
| format | Article |
| id | mit-1721.1/137695 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:14:04Z |
| publishDate | 2021 |
| publisher | Robotics: Science and Systems Foundation |
| record_format | dspace |
| spelling | mit-1721.1/1376952023-01-10T15:04:42Z Provably Safe Robot Navigation with Obstacle Uncertainty Axelrod, Brian Kaelbling, Leslie Lozano-Perez, Tomas Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science © 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. 2021-11-08T16:07:48Z 2021-11-08T16:07:48Z 2017-07-12 2019-06-04T14:56:17Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137695 Axelrod, Brian, Kaelbling, Leslie and Lozano-Perez, Tomas. 2017. "Provably Safe Robot Navigation with Obstacle Uncertainty." en 10.15607/rss.2017.xiii.023 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Robotics: Science and Systems Foundation MIT web domain |
| spellingShingle | Axelrod, Brian Kaelbling, Leslie Lozano-Perez, Tomas Provably Safe Robot Navigation with Obstacle Uncertainty |
| title | Provably Safe Robot Navigation with Obstacle Uncertainty |
| title_full | Provably Safe Robot Navigation with Obstacle Uncertainty |
| title_fullStr | Provably Safe Robot Navigation with Obstacle Uncertainty |
| title_full_unstemmed | Provably Safe Robot Navigation with Obstacle Uncertainty |
| title_short | Provably Safe Robot Navigation with Obstacle Uncertainty |
| title_sort | provably safe robot navigation with obstacle uncertainty |
| url | https://hdl.handle.net/1721.1/137695 |
| work_keys_str_mv | AT axelrodbrian provablysaferobotnavigationwithobstacleuncertainty AT kaelblingleslie provablysaferobotnavigationwithobstacleuncertainty AT lozanopereztomas provablysaferobotnavigationwithobstacleuncertainty |