CHOMP: gradient optimization techniques for efficient motion planning

  • Authors:

  • Nathan Ratliff;Matt Zucker;J. Andrew Bagnell;Siddhartha Srinivasa

  • Affiliations:

  • The Robotics Institute, Carnegie Mellon University;The Robotics Institute, Carnegie Mellon University;The Robotics Institute, Carnegie Mellon University;Intel Research, Pittsburgh, PA

  • Venue:
  • ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
  • Year:
  • 2009

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Abstract

Existing 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 both optimizes higher-order dynamics and is able to converge over a wider range of input paths relative to previous path optimization strategies. In particular, we relax the collision-free feasibility prerequisite on input paths required by those strategies. As a result, CHOMP can be used as a standalone motion planner in many real-world planning queries. We demonstrate the effectiveness of our proposed method in manipulation planning for a 6-DOF robotic arm as well as in trajectory generation for a walking quadruped robot.