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LQR-Trees: Feedback motion planning on sparse randomized trees

Author(s)
Tedrake, Russell Louis
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Abstract
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.
Date issued
2009-06
URI
http://hdl.handle.net/1721.1/64643
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Robotics: Science and Systems V
Publisher
MIT Press
Citation
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.
Version: Author's final manuscript
ISBN
978-0262514637
026251463X

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