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CHOMP: Gradient Optimization Techniques for Efficient Motion Planning
journal contribution
posted on 2009-01-01, 00:00 authored by Nathan Ratliff, Matt Zucker, J. Andrew Bagnell, Siddhartha SrinivasaExisting high-dimensional motion planning
algorithms are simultaneously overpowered and underpowered. In
domains sparsely populated by obstacles, the heuristics used by
sampling-based planners to navigate “narrow passages” can be
needlessly complex; furthermore, additional post-processing is
required to remove the jerky or extraneous motions from the
paths that such planners generate. In this paper, we present
CHOMP, a novel method for continuous path refinement that
uses covariant gradient techniques to improve the quality of
sampled trajectories. Our optimization technique converges
over a wider range of input paths and is able to optimize higher-
order dynamics of trajectories than previous path optimization
strategies. As a result, CHOMP can be used as a standalone
motion planner in many real-world planning queries. The
effectiveness of our proposed method is demonstrated in manipulation planning for a 6-DOF robotic arm as well as in
trajectory generation for a walking quadruped robot.