Nonholonomic virtual constraints and gait optimization for robust walking control

Abstract

A key challenge in robotic bipedal locomotion is the design of feedback controllers that function well in the presence of uncertainty, in both the robot and its environment. This paper addresses the design of feedback controllers and periodic gaits that function well in the presence of modest terrain variation, without over-reliance on perception and a priori knowledge of the environment. Model-based design methods are introduced and subsequently validated in simulation and experiment on MARLO, an underactuated three-dimensional bipedal robot that is of roughly human size and is equipped with an inertial measurement unit and joint encoders. Innovations include an optimization method that accounts for multiple types of disturbances and a feedback control design that enables continuous velocity-based posture regulation via nonholonomic virtual constraints. Using a single continuously defined controller taken directly from optimization, MARLO traverses sloped sidewalks and parking lots, terrain covered with randomly thrown boards, and grass fields, all while maintaining average walking speeds between 0.9 and 0.98 m/s and setting a new precedent for walking efficiency in realistic environments.