Abstract:

Since early twentieth century, human continues to imagine the future of humanoid robots. As the time going on, we always wish humanoid robots can run, jump, act like human, and even be better than human. Because of the limitations of material technology and actuators, the ratio of weight and power output of robots cannot reach the same level as human. Until now, due to the improvement of technology and the accumulated experiences, researchers in America and Japan start to demonstrate that their new robots can run and jump smoothly and stably. And the robots nowadays start to be capable of these difficult tasks. However, in the aspect of motion planning and pattern generation, the researchers in academy and in industry use their own robot platforms and algorithms to develop their motion control and planning systems. These systems have their own advantages and limitations. In view of this, an optimized walking pattern generator for humanoid robots is proposed in this dissertation. The proposed pattern generator can solve walking patterns with arbitrary assigned COG (Center of Gravity) height trajectory and 3D ZMP (Zero Moment Point) trajectory in real-time. Thus, walking pattern generation with arbitrary assigned ground height status is achievable. Based on the proposed walking pattern generator and Newton-Euler dynamics, a cost function is designed to optimize COG height trajectory with given ZMP trajectory. An optimized 3D COG walking pattern generator can be achieved in this dissertation. In addition, state machine based distributed control system with USB-to-CAN-bus interface is used to construct a real-time robot control system. Using this system, the performance of the proposed walking pattern generator is also verified.