CHOMP: Gradient optimization techniques for efficient motion planning

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.

I. Introduction

In recent years, sampling-based planning algorithms have met with widespread success due to their ability to rapidly discover the connectivity of high-dimensional configuration spaces. Planners such as Probabilistic Roadmap (PRM) and Rapidly-exploring Random Tree (RRT) algorithms, along with their descendents, are now used in a multitude of robotic applications [15], [16]. Both algorithms are typically deployed as part of a two-phase process: first find a feasible path, and then optimize it to remove redundant or jerky motion.