Frequent walking pattern generation that uses estimated actual posture for robust walking control

This paper presents a novel robust framework for online walking control that uses the estimated actual posture of a robot for frequent pattern generation. The motion status in the absolute coordinate system is estimated by using an attitude sensor, and the estimated status is used as the initial condition of the next online pattern generation, so that the walking pattern generator can effectively maintain the balance of actual walking. Since the initial conditions are decided from the estimated actual posture, the pattern generated in each cycle is discontinuous to the one generated in the previous cycle. Therefore, a local sensor feedback controller is developed that can execute discontinuous segments of the trajectory and control the ground reaction force. Damping control of the foot position by using position-controlled leg joints is adopted to attain the desired ground reaction force. In addition, damping control of the torso inclination is implemented in the local sensor feedback to suppress the divergence of motion from that commanded in the absolute coordinate system. The proposed framework is implemented on the full-size humanoid HRP-2, and validated through walking control experiments.