LQR-Trees: Feedback motion planning on sparse randomized trees
Name
Tedrake_LQR-trees.pdf
Size
490.03 KB
Format
Adobe PDF
Checksum (MD5)
d42fc44ced30f007067e982deca211a5
Author(s)
Tedrake, Russell Louis
Date Issued
June 2009
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
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://www.roboticsproceedings.org/rss05/p3.pdf
