Compass gait revisited: A human data perspective with extensions to three dimensions

Abstract

To better understand human walking, three bipedal robotic models-starting with the compass gait biped and increasing in complexity to a 3D kneed biped-are studied with controllers of human-inspired design; these controllers are derived from experimental data measuring the kinematics of human test subjects. The collected data are examined in an attempt to classify some of the most fundamental behaviors underlying human walking; it is found that a subset of functions on the kinematics of humans can be represented as a single class of functions. The control scheme uses feedback linearization to track the human output functions on a robot. A state-based parameterization for time is introduced to make these human functions time-invariant. Simulation results indicate the existence of locally exponentially stable periodic orbits for each model of interest; these orbits represent stable, steady-state walking gaits. The application of the human-inspired control approach results in “humanlike” walking as supported by agreement between the outputs of the robot models and humans.