| dc.description.abstract | This thesis presents a control design approach, which uses human data in the development of bipedal robotic control techniques for multiple locomotion behaviors. Insight into the fundamental behaviors of human locomotion is obtained through the examination of experimental human data for level walking, stair ascending, stair descending and running. Specifically, it is shown that certain outputs of the human, independent of locomotion terrain, can be characterized by a single function, termed the extended canonical human function. Through feedback linearization, human-inspired locomotion controllers are leveraged to drive the outputs of the simulated robot, via the extended canonical human function, to the outputs from human locomotion. An optimization problem, subject to the constraints of partial hybrid zero dynamics, is presented which yields parameters of these controllers that provide the best fit to human data while simultaneously ensuring stability of the controlled bipedal robot. The resulting behaviors are stable locomotion on flat ground, upstairs, downstairs and running | these four locomotion modes are termed “motion primitives”. A second optimization is presented, which yields controllers that evolve the robot from one motion primitive to another | these modes of locomotion are termed “motion transitions”. A directed graph consisting these motion primitives and motion transitions has been constructed for the stable motion planning of bipedal locomotion. A final simulation is given, which shows the controlled evolution of a robotic biped as it transitions through each mode of locomotion over a pyramidal staircase. | en |
