Bipedal locomotion is well suited for mobile robotics because it promises to allow robots to traverse difficult terrain and work effectively in man-made environments. Despite this inherent advantage, however, no existing bipedal robot achieves human-level performance in multiple environments. 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 dissertation addresses the design of feedback controllers and periodic gaits that function well in the presence of modest terrain variation, without reliance on perception or 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 roughly human size and has six actuators and thirteen degrees of freedom. Innovations include virtual nonholonomic constraints that enable continuous velocity-based posture regulation and an optimization method that accounts for multiple types of disturbances and more heavily penalizes deviations that persist during critical stages of walking. 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 0.9-0.98 m/s and setting a new precedent for walking efficiency in realistic environments.
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Nonholonomic Virtual Constraints and Gait Optimization for Robust Robot Walking Control.
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