Abstract
Bipedal walking ability, which is mainly composed of mechanical stability and moving efficiency, is one of the most important issues in the field of humanoid robots. An effective use of the arms is expected to improve the walking ability under the constraints from limited body. Multiple usages of the arms are required to adapt the robot to various walking conditions because each usage possesses different utility, respectively. We propose two strategies for arm-swing to enhance stability and efficiency. Both the strategies use the selection algorithm for locomotion (SAL) that determines the optimal locomotion for the various walking conditions. One strategy is a use/compensation of moment of swing-leg by hip-rotation/arm-swing. This strategy, called Ro-SAL, selects a rotation rate of hip depending upon a falling risk. Another strategy is a support of the center of gravity (COG) tracking. This strategy, called Su-SAL, employs a predictive control, and a support weight that determines weights of input and output in the predictive control is also selected depending upon the falling risk. From simulation results, we validate that Ro-SAL is effective in a stable state and a state with internal model error, while Su-SAL is effective in states with external force and environmental complexity.
