Robust phase-space planning for agile legged locomotion over various terrain topologies

Abstract

In this study, we present a framework for phasespace planning and control of agile bipedal locomotion while robustly tracking a set of non-periodic keyframes. By using a reduced-order model, we formulate a hybrid planning framework where the center-of-mass motion is constrained to a general surface manifold. This framework also proposes phase-space bundles to characterize robustness and a robust hybrid automaton to effectively design planning algorithms. A newly defined phasespace locomotion manifold is used as a Riemannian metric to measure the distance between the disturbed state and the planned manifold. Based on this metric, a dynamic programming based hybrid controller is introduced to produce robust locomotions. The robustness of the proposed framework is validated by using simulations of rough terrain locomotion recovery from external disturbances. Additionally, the agility of this framework is demonstrated by using simulations of the dynamic locomotion over random rough terrains.