Reactive Integrated Motion Planning and Execution
Current motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution....
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| Format: | Article |
| Language: | en_US |
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AAAI Press/International Joint Conferences on Artificial Intelligence
2017
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| Online Access: | http://hdl.handle.net/1721.1/106198 https://orcid.org/0000-0002-4787-4587 https://orcid.org/0000-0002-1737-950X |
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| author | Hofmann, Andreas Helbert, Justin C. Fernandez Gonzalez, Enrique Smith, Scott Williams, Brian |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Hofmann, Andreas Helbert, Justin C. Fernandez Gonzalez, Enrique Smith, Scott Williams, Brian |
| author_sort | Hofmann, Andreas |
| collection | MIT |
| description | Current motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not
practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution. Second, they do not support temporal constraints, which are often important for synchronization between a robot and other actors. Third, because many popular planners generate completely
new trajectories for each planning problem, they do not allow for representing persistent control policy information associated with a trajectory across planning problems. We present Chekhov, a reactive, integrated motion planning and execution system that addresses these
problems. Chekhov uses a Tube-based Roadmap in which the edges of the roadmap graph are families of trajectories called flow tubes, rather than the single trajectories commonly used in roadmap systems.
Flow tubes contain control policy information about how to move through the tube, and also represent the dynamic limits of the system, which
imply temporal constraints. This, combined with an incremental APSP algorithm for quickly finding paths in the roadmap graph, allows Chekhov to operate in rapidly changing environments. Testing in simulation, and with a robot testbed has shown improvement in planning speed and motion predictability over current motion planners. |
| first_indexed | 2024-09-23T12:02:35Z |
| format | Article |
| id | mit-1721.1/106198 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:02:35Z |
| publishDate | 2017 |
| publisher | AAAI Press/International Joint Conferences on Artificial Intelligence |
| record_format | dspace |
| spelling | mit-1721.1/1061982022-09-27T23:43:58Z Reactive Integrated Motion Planning and Execution Hofmann, Andreas Helbert, Justin C. Fernandez Gonzalez, Enrique Smith, Scott Williams, Brian Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Hofmann, Andreas Helbert, Justin C. Fernandez Gonzalez, Enrique Smith, Scott Williams, Brian Current motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution. Second, they do not support temporal constraints, which are often important for synchronization between a robot and other actors. Third, because many popular planners generate completely new trajectories for each planning problem, they do not allow for representing persistent control policy information associated with a trajectory across planning problems. We present Chekhov, a reactive, integrated motion planning and execution system that addresses these problems. Chekhov uses a Tube-based Roadmap in which the edges of the roadmap graph are families of trajectories called flow tubes, rather than the single trajectories commonly used in roadmap systems. Flow tubes contain control policy information about how to move through the tube, and also represent the dynamic limits of the system, which imply temporal constraints. This, combined with an incremental APSP algorithm for quickly finding paths in the roadmap graph, allows Chekhov to operate in rapidly changing environments. Testing in simulation, and with a robot testbed has shown improvement in planning speed and motion predictability over current motion planners. Boeing Company (Contract MIT-BA-GTA-1) 2017-01-05T15:10:31Z 2017-01-05T15:10:31Z 2015-07 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/106198 Hofmann, Andreas et al. "Reactive Integrated Motion Planning and Execution" Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, 25-31 July, 2015. https://orcid.org/0000-0002-4787-4587 https://orcid.org/0000-0002-1737-950X en_US http://ijcai-15.org/index.php/accepted-papers Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf AAAI Press/International Joint Conferences on Artificial Intelligence MIT web domain |
| spellingShingle | Hofmann, Andreas Helbert, Justin C. Fernandez Gonzalez, Enrique Smith, Scott Williams, Brian Reactive Integrated Motion Planning and Execution |
| title | Reactive Integrated Motion Planning and Execution |
| title_full | Reactive Integrated Motion Planning and Execution |
| title_fullStr | Reactive Integrated Motion Planning and Execution |
| title_full_unstemmed | Reactive Integrated Motion Planning and Execution |
| title_short | Reactive Integrated Motion Planning and Execution |
| title_sort | reactive integrated motion planning and execution |
| url | http://hdl.handle.net/1721.1/106198 https://orcid.org/0000-0002-4787-4587 https://orcid.org/0000-0002-1737-950X |
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