Robust Walking Control of Humanoids on Unknown Rough Terrain via Short Cycle Motion Generation Using Absolute Position Estimates

Abstract

This paper presents a novel robust framework for online walking control that can handle unknown terrain. The actual motion of the robot in the absolute coordinate system is estimated and used as the initial conditions of short cycle online walking pattern generation in the framework so that the walking pattern generation is put into the feedback loop of the balance maintenance of the actual walking. We used attitude sensor system for estimating the absolute motion, and the walking patterns are generated at 20[ms] cycle by using full body dynamic model. 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 the torso motion from that commanded in the absolute coordinate system. The proposed framework is implemented on the full-size humanoid HRP-2, and validated through the experiments including walking on many types of unknown terrain with up to 10 degrees slope uncertainty and over 20[mm] in height uncertainty.