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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| Format: | Article |
| Language: | en_US |
| Published: |
MIT Press
2011
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| Online Access: | http://hdl.handle.net/1721.1/64643 https://orcid.org/0000-0002-8712-7092 |
| Summary: | 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. |
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