LQR-Trees: Feedback motion planning on sparse randomized trees
Author(s)Tedrake, Russell Louis
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Robotics: Science and Systems V
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.
Author's final manuscript