Artificial invariant subspace with potential functions for humanoid robot balancing

Xiang Deng, Fei Miao, Daniel D. Lee

Abstract: Existing trajectory planning based locomotion algorithms lack the analytic tools to fully comprehend energy based movements that would allow for full stability and mobility. Such drawbacks make humanoid robots' locomotion sensitive to external disturbances and compromise robots' agility in unstructured environment. In this work, we specifically focus on the push recovery problem for humanoid robots. We propose an approach to design a nonlinear controller that is robust to external disturbances. It allows the state of the rigid body dynamics to asymptotically converge to the subspace that meet the criteria of balancing, based on the properties of artificial invariant subspace and potential functions. Our algorithm is completely adaptive in real time without requiring trajectory planning in advance. We demonstrate the robustness of the proposed algorithm base on extensive push recovery experiments on the DARWIN-OP robot platform on flat terrains.