CHOMP: Gradient optimization techniques for efficient motion planning

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 converges over a wider range of input paths and is able to optimize higherorder 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 w...

• Type: Journal
• Year: 2009
• Where: ICRA
• Authors: Nathan D. Ratliff, Matt Zucker, J. Andrew Bagnell, Siddhartha Srinivasa
• Keywords: 6-DOF Robotic Arm, Continuous Path Refinement, ICRA 2009, Robotics, Standalone Motion Planner