LQR-Trees: Feedback motion planning on sparse randomized trees
Recent advances in the direct computation of Lyapunov functions using convex optimization make it possible to efficiently evaluate regions of stability for smooth nonlinear systems. Here we present a feedback motion planning algorithm which uses these results to efficiently combine locally valid...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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MIT Press
2011
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| Online Access: | http://hdl.handle.net/1721.1/64643 https://orcid.org/0000-0002-8712-7092 |
| _version_ | 1811097286544982016 |
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| author | Tedrake, Russell Louis |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Tedrake, Russell Louis |
| author_sort | Tedrake, Russell Louis |
| collection | MIT |
| description | Recent advances in the direct computation of Lyapunov
functions using convex optimization make it possible to
efficiently evaluate regions of stability for smooth nonlinear
systems. Here we present a feedback motion planning algorithm
which uses these results to efficiently combine locally valid
linear quadratic regulator (LQR) controllers into a nonlinear
feedback policy which probabilistically covers the reachable area
of a (bounded) state space with a region of stability, certifying
that all initial conditions that are capable of reaching the goal
will stabilize to the goal. We investigate the properties of this
systematic nonlinear feedback control design algorithm on simple
underactuated systems and discuss the potential for control of
more complicated control problems like bipedal walking. |
| first_indexed | 2024-09-23T16:57:15Z |
| format | Article |
| id | mit-1721.1/64643 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:57:15Z |
| publishDate | 2011 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | mit-1721.1/646432022-10-03T09:22:26Z LQR-Trees: Feedback motion planning on sparse randomized trees Tedrake, Russell Louis Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Tedrake, Russell Louis Tedrake, Russell Louis Recent advances in the direct computation of Lyapunov functions using convex optimization make it possible to efficiently evaluate regions of stability for smooth nonlinear systems. Here we present a feedback motion planning algorithm which uses these results to efficiently combine locally valid linear quadratic regulator (LQR) controllers into a nonlinear feedback policy which probabilistically covers the reachable area of a (bounded) state space with a region of stability, certifying that all initial conditions that are capable of reaching the goal will stabilize to the goal. We investigate the properties of this systematic nonlinear feedback control design algorithm on simple underactuated systems and discuss the potential for control of more complicated control problems like bipedal walking. 2011-06-21T20:16:06Z 2011-06-21T20:16:06Z 2009-06 Article http://purl.org/eprint/type/ConferencePaper 978-0262514637 026251463X http://hdl.handle.net/1721.1/64643 Tedrake, Russ. "LQR-Trees: Feedback motion planning on sparse randomized trees." In Papers of the fifth annual Robotics: Science and Systems conference, June 28-July 1, 2009, University of Washington, Seattle, USA. https://orcid.org/0000-0002-8712-7092 en_US http://www.roboticsproceedings.org/rss05/p3.pdf Robotics: Science and Systems V Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf MIT Press MIT web domain |
| spellingShingle | Tedrake, Russell Louis LQR-Trees: Feedback motion planning on sparse randomized trees |
| title | LQR-Trees: Feedback motion planning on sparse randomized trees |
| title_full | LQR-Trees: Feedback motion planning on sparse randomized trees |
| title_fullStr | LQR-Trees: Feedback motion planning on sparse randomized trees |
| title_full_unstemmed | LQR-Trees: Feedback motion planning on sparse randomized trees |
| title_short | LQR-Trees: Feedback motion planning on sparse randomized trees |
| title_sort | lqr trees feedback motion planning on sparse randomized trees |
| url | http://hdl.handle.net/1721.1/64643 https://orcid.org/0000-0002-8712-7092 |
| work_keys_str_mv | AT tedrakerusselllouis lqrtreesfeedbackmotionplanningonsparserandomizedtrees |