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My thesis work develops feedback behaviors for underactuated legged robots, via a dimensionality reduction in the parameters used to control complex behaviors. Using this control approach, I have developed real-time feedback behaviors that allow a legged robot to climb surfaces such as vertical exterior building walls as well as other challenging surfaces, such as tree trunks.
Rather than working with the full configuration space of a legged robot’s joints, I model typical behaviors using open-loop gaits—patterns of leg motions that, in the absence of any feedback, can produce effective locomotion—and introduce parametrizations of gaits on which layers of feedback are added. Within this basic paradigm of mixing feedforward and feedback, I have added another control decomposition, whereby one control component performs sensor-based feedback, modifying a gait based upon sensor measurements. With a time-varying gait, another controller then performs gait regulation, closely monitoring the gait a robot uses, to keep the system near safe, known gaits. My thesis work seeks to provide useful solutions for each of these types of control.
Focusing specifically on the temporal parameters of gaits, I develop control laws that modify the gait timing as a robot locomotes. The result is temporally adaptive behaviors that offer greater robustness and flexibility, when compared to traditional legged behaviors and gaits that remain fixed in their gait timing.
To add sensor-based feedback, I develop controllers that try to balance foot forces while climbing. I incorporate gait regulation with specifically designed potential functions whose minima are associated with gait limit cycles.
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