Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
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.
2012. , 80 p.