Overview
Full-model nonlinear MPC on a planar biped.
Abstract
The ability to generate dynamic walking in real- time for bipedal robots with input constraints and underactua- tion has the potential to enable locomotion in dynamic, complex and unstructured environments. Yet, the high-dimensional na- ture of bipedal robots has limited the use of full-order rigid body dynamics to gaits which are synthesized offline and then tracked online. In this work we develop an online nonlinear model predictive control approach that leverages the full-order dynamics to realize diverse walking behaviors. Additionally, this approach can be coupled with gaits synthesized offline via a desired reference to enable a shorter prediction horizon and rapid online re-planning, bridging the gap between online reactive control and offline gait planning. We demonstrate the proposed method, both with and without an offline gait, on the planar robot AMBER-3M in simulation and on hardware.