The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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 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.

Keywords

High-dimensional motion planning, trajectory optimization, covariant optimization, functional gradients, mobile manipulation, quadrupedal locomotion

Notes

Associated Center(s) / Consortia: Quality of Life Technology Center, National Robotics Engineering Center, and Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Personal Robotics

Text Reference

Nathan Ratliff, Matthew Zucker, J. Andrew (Drew) Bagnell, and Siddhartha Srinivasa, "CHOMP: Gradient Optimization Techniques for Efficient Motion Planning," IEEE International Conference on Robotics and Automation (ICRA), May, 2009.

BibTeX Reference

@inproceedings{Ratliff_2009_6285,
   author = "Nathan Ratliff and Matthew Zucker and J. Andrew (Drew) Bagnell and Siddhartha Srinivasa",
   title = "CHOMP: Gradient Optimization Techniques for Efficient Motion Planning",
   booktitle = "IEEE International Conference on Robotics and Automation (ICRA)",
   month = "May",
   year = "2009",
}

The Robotics Institute

Menu