An Optimization Approach to Rough Terrain Locomotion
Abstract
We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and “certificates” that ensure the output of an abstract high-level planner can be realized by deeper layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of rough terrains.
BibTeX
@conference{Zucker-2010-10442,author = {Matthew Zucker and J. Andrew (Drew) Bagnell and Chris Atkeson and James Kuffner},
title = {An Optimization Approach to Rough Terrain Locomotion},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {2010},
month = {May},
pages = {3589 - 3595},
keywords = {legged locomotion, motion planning, machine learning},
}