Funnel libraries for real-time robust feedback motion planning
We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider scenarios where these plans must be generated in real time, because constraints such as obsta...
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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SAGE Publications
2021
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| Online Access: | https://hdl.handle.net/1721.1/130014 |
| _version_ | 1826208412593029120 |
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| author | Majumdar, Anirudha Tedrake, Russell L |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Majumdar, Anirudha Tedrake, Russell L |
| author_sort | Majumdar, Anirudha |
| collection | MIT |
| description | We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider scenarios where these plans must be generated in real time, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Our approach is to pre-compute a library of "funnels" along different maneuvers of the system that the state is guaranteed to remain within (despite bounded disturbances) when the feedback controller corresponding to the maneuver is executed. We leverage powerful computational machinery from convex optimization (sums-of-squares programming in particular) to compute these funnels. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances. We demonstrate and validate our method using extensive hardware experiments on a small fixed-wing airplane avoiding obstacles at high speed (∼12 mph), along with thorough simulation experiments of ground vehicle and quadrotor models navigating through cluttered environments. To our knowledge, these demonstrations constitute one of the first examples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in real time in environments with complex geometric constraints. |
| first_indexed | 2024-09-23T14:05:03Z |
| format | Article |
| id | mit-1721.1/130014 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:05:03Z |
| publishDate | 2021 |
| publisher | SAGE Publications |
| record_format | dspace |
| spelling | mit-1721.1/1300142022-09-28T18:18:40Z Funnel libraries for real-time robust feedback motion planning Majumdar, Anirudha Tedrake, Russell L Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider scenarios where these plans must be generated in real time, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Our approach is to pre-compute a library of "funnels" along different maneuvers of the system that the state is guaranteed to remain within (despite bounded disturbances) when the feedback controller corresponding to the maneuver is executed. We leverage powerful computational machinery from convex optimization (sums-of-squares programming in particular) to compute these funnels. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances. We demonstrate and validate our method using extensive hardware experiments on a small fixed-wing airplane avoiding obstacles at high speed (∼12 mph), along with thorough simulation experiments of ground vehicle and quadrotor models navigating through cluttered environments. To our knowledge, these demonstrations constitute one of the first examples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in real time in environments with complex geometric constraints. 2021-03-01T16:00:36Z 2021-03-01T16:00:36Z 2017-06 2017-05 2019-07-15T16:48:40Z Article http://purl.org/eprint/type/JournalArticle 0278-3649 1741-3176 https://hdl.handle.net/1721.1/130014 Majumdar, Anirudha and Russ Tedrake. "Funnel libraries for real-time robust feedback motion planning." International Journal of Robotics Research 36, 8 (June 2017): 947-982 © 2017 The Author(s) en http://dx.doi.org/10.1177/0278364917712421 International Journal of Robotics Research Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf SAGE Publications arXiv |
| spellingShingle | Majumdar, Anirudha Tedrake, Russell L Funnel libraries for real-time robust feedback motion planning |
| title | Funnel libraries for real-time robust feedback motion planning |
| title_full | Funnel libraries for real-time robust feedback motion planning |
| title_fullStr | Funnel libraries for real-time robust feedback motion planning |
| title_full_unstemmed | Funnel libraries for real-time robust feedback motion planning |
| title_short | Funnel libraries for real-time robust feedback motion planning |
| title_sort | funnel libraries for real time robust feedback motion planning |
| url | https://hdl.handle.net/1721.1/130014 |
| work_keys_str_mv | AT majumdaranirudha funnellibrariesforrealtimerobustfeedbackmotionplanning AT tedrakerusselll funnellibrariesforrealtimerobustfeedbackmotionplanning |