Locomotion Trajectory Generation and Dynamic Control for Bipedal Walking Robots

Abstract

In this thesis, a method for joint trajectory generation to achieve optimized stable locomotion for bipedal robots is first proposed and referred to as Genetic Algorithm Optimized Fourier Series Formulation. This method is used to generate the basic motion patterns for joint motion coordination. Then, a soft motion control is investigated and applied to walking on various terrains and 3D motions. The results show that stable and robust limit cycle behaviors can be achieved. In addition, a high-level motion adjustment agent based on the Truncated Fourier series formulation has been developed to adjust the stride-frequency, step-length and walking posture in a straightforward manner. Given these functionalities, human walking behaviors have been achieved to a good extent. Furthermore, two motion-balance strategies have been proposed and demonstrated for prolong 3D walking motions. The entire bipedal walking control algorithm has shown its generality for different postures and for robots with different physical properties.