A Framework for Planning and Controlling Non-Periodic Bipedal Locomotion

TL;DR

This paper introduces a theoretical framework for planning and controlling agile, non-periodic bipedal locomotion using a hybrid phase-space approach, enhancing robustness and terrain adaptability.

Contribution

It proposes a novel hybrid phase-space planning and control framework specifically designed for non-periodic gait generation and disturbance robustness in bipedal robots.

Findings

Successfully tracks non-periodic apex states over various terrains

Demonstrates robustness to external disturbances

Enables non-periodic bouncing maneuvers on disjointed terrains

Abstract

This study presents a theoretical framework for planning and controlling agile bipedal locomotion based on robustly tracking a set of non-periodic apex states. Based on the prismatic inverted pendulum model, we formulate a hybrid phase-space planning and control framework which includes the following key components: (1) a step transition solver that enables dynamically tracking non-periodic apex or keyframe states over various types of terrains, (2) a robust hybrid automaton to effectively formulate planning and control algorithms, (3) a phase-space metric to measure distance to the planned locomotion manifolds, and (4) a hybrid control method based on the previous distance metric to produce robust dynamic locomotion under external disturbances. Compared to other locomotion frameworks, we have a larger focus on non-periodic gait generation and robustness metrics to deal with disturbances. Such focus enables the proposed control framework to robustly track non-periodic apex states over various challenging terrains and under external disturbances as illustrated through several simulations. Additionally, it allows a bipedal robot to perform non-periodic bouncing maneuvers over disjointed terrains.