Asymptotically optimal kinematic design of robots using motion planning
Abstract In highly constrained settings, e.g., a tentacle-like medical robot maneuvering through narrow cavities in the body for minimally invasive surgery, it may be difficult or impossible for a robot with a generic kinematic design to reach all desirable targets while avoiding obst...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2021
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| Online Access: | https://hdl.handle.net/1721.1/131532 |
| _version_ | 1811082251124867072 |
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| author | Baykal, Cenk Bowen, Chris Alterovitz, Ron |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Baykal, Cenk Bowen, Chris Alterovitz, Ron |
| author_sort | Baykal, Cenk |
| collection | MIT |
| description | Abstract
In highly constrained settings, e.g., a tentacle-like medical robot maneuvering through narrow cavities in the body for minimally invasive surgery, it may be difficult or impossible for a robot with a generic kinematic design to reach all desirable targets while avoiding obstacles. We introduce a design optimization method to compute kinematic design parameters that enable a single robot to reach as many desirable goal regions as possible while avoiding obstacles in an environment. Our method appropriately integrates sampling-based motion planning in configuration space into stochastic optimization in design space so that, over time, our evaluation of a design’s ability to reach goals increases in accuracy and our selected designs approach global optimality. We prove the asymptotic optimality of our method and demonstrate performance in simulation for (1) a serial manipulator and (2) a concentric tube robot, a tentacle-like medical robot that can bend around anatomical obstacles to safely reach clinically-relevant goal regions. |
| first_indexed | 2024-09-23T12:00:07Z |
| format | Article |
| id | mit-1721.1/131532 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:00:07Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1315322023-02-24T17:17:35Z Asymptotically optimal kinematic design of robots using motion planning Baykal, Cenk Bowen, Chris Alterovitz, Ron Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Abstract In highly constrained settings, e.g., a tentacle-like medical robot maneuvering through narrow cavities in the body for minimally invasive surgery, it may be difficult or impossible for a robot with a generic kinematic design to reach all desirable targets while avoiding obstacles. We introduce a design optimization method to compute kinematic design parameters that enable a single robot to reach as many desirable goal regions as possible while avoiding obstacles in an environment. Our method appropriately integrates sampling-based motion planning in configuration space into stochastic optimization in design space so that, over time, our evaluation of a design’s ability to reach goals increases in accuracy and our selected designs approach global optimality. We prove the asymptotic optimality of our method and demonstrate performance in simulation for (1) a serial manipulator and (2) a concentric tube robot, a tentacle-like medical robot that can bend around anatomical obstacles to safely reach clinically-relevant goal regions. 2021-09-20T17:20:16Z 2021-09-20T17:20:16Z 2018-06-29 2020-09-24T21:31:14Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131532 en https://doi.org/10.1007/s10514-018-9766-x Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer Science+Business Media, LLC, part of Springer Nature application/pdf Springer US Springer US |
| spellingShingle | Baykal, Cenk Bowen, Chris Alterovitz, Ron Asymptotically optimal kinematic design of robots using motion planning |
| title | Asymptotically optimal kinematic design of robots using motion planning |
| title_full | Asymptotically optimal kinematic design of robots using motion planning |
| title_fullStr | Asymptotically optimal kinematic design of robots using motion planning |
| title_full_unstemmed | Asymptotically optimal kinematic design of robots using motion planning |
| title_short | Asymptotically optimal kinematic design of robots using motion planning |
| title_sort | asymptotically optimal kinematic design of robots using motion planning |
| url | https://hdl.handle.net/1721.1/131532 |
| work_keys_str_mv | AT baykalcenk asymptoticallyoptimalkinematicdesignofrobotsusingmotionplanning AT bowenchris asymptoticallyoptimalkinematicdesignofrobotsusingmotionplanning AT alterovitzron asymptoticallyoptimalkinematicdesignofrobotsusingmotionplanning |