Model predictive control of a walking bipedal robotusing online optimization

Abstract

Humanoid robotics is a challenging and promising research field. Legged locomotion is one of the most important aspects of it. In spite of the progress achieved in the last years in control of walking robots, many problems are yet to be resolved. The inherent complexity of such robots makes their control a difficult task even on the modern hardware. In order to address this issue approximate models and high performance algorithms are employed. This thesis is focused on the model predictive control of a walking bipedal robot, which is approximated by an inverted pendulum, using online optimization. A special emphasis is made on the solvers that exploit the structure of quadratic optimization problems in the context of model predictive control. Two methods for solution of these problems are implemented: primal active set and primal logarithmic barrier methods. They are tested and compared in a simulation and on a humanoid robot. A software module for control of the Nao humanoid robot is developed for this purpose.