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My Ph.D. from Carnegie Mellon University is titled “Gait Regulation Control Techniques for Robust Legged Locomotion”. In that research I pursued control techniques to actively monitor and control the gait a legged robot uses during locomotion. The application of the research was controlling a legged robot while climbing challenging vertical surfaces, such that the robot must actively control its leg grasp and recovery from slips during climbing.
As a robot recovers from errors during climbing (see gait control for examples of feedback controllers), the robot’s gait drifts away from its preferred gait. I have built CPG-based dynamical systems that converge to preferred gait timings. The design of these control systems, in the form of potential functions on complex spaces, is a critical portion of my thesis research.
The timing of legs in a gait is topologically equivalent to a high-dimensional torus, on which I have noted the existence of obstacles sets, corresponding to incorrect groups of legs recirculating together, thus losing static stability. Using these obstacles as a basis, I have built control laws that reshape the torus in order to introduce stable limit cycles at desired gaits, yet converge to these gaits while attempting to avoid the obstacles specified. Furthermore, by guaranteeing certain convergence properties, I am able to use these systems to perform gait switching, allowing the robot to actively change amongst gaits while locomoting. The algorithms and control laws that I have developed for gait regulation run in real-time, owing to use of a cell decomposition of the torus, paired with a simple hybrid control approach, to guarantee certain convergence properties.
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