Online Walking Control System for Humanoids with Short Cycle Pattern Generation

It is shown that frequently updating the motion pattern contributes to maintaining long-term balance while performing online walking control and an extension of the short cycle pattern generation method that can accommodate external forces measured online is presented.

Abstract

The present paper presents an online walking control system that frequently generates and updates dynamically stable motion patterns with a cycle time of 20 ms. We show that frequently updating the motion pattern contributes to maintaining long-term balance while performing online walking control. In addition, the system enables a robot to respond quickly to changes in the commanded walking direction. Using preview control theory, we generate dynamically stable walking patterns. We propose a method to adjust the future desired zero moment point (ZMP) by modifying the foot landing position in order to maintain the dynamic balance of the generated motion pattern. This technique can be used to filter input commands that would result in sudden changes to the foot landing position, which would result in dynamic instability. The method is also used to compensate for errors between the actual and desired ZMP due to disturbances encountered while walking. We also present an extension of the short cycle pattern generation method that can accommodate external forces measured online. Experimental results for activities such as pushing a table are demonstrated on the full-size humanoid HRP-2 to evaluate the performance of the proposed walking control system.