Selection of two arm-swing strategies for bipedal walking to enhance both stability and efficiency

Abstract

Walking ability, which involves mainly stability and efficiency, is one of the most important issues in humanoid robots. Effective use of a robot’s arms is expected to improve its walking ability under its body constraints. Although several types of arm-swing strategies have been proposed, they are difficult to execute simultaneously. We propose two-stage integration of these strategies to enhance both stability and efficiency. A selection algorithm for locomotion (SAL) selects the appropriate strategy according to the demands of the situation. In the first stage, two strategies are evaluated. One of them, Ro-SAL, entails use and compensation of the moment of the swing leg by hip rotation and arm swing. The other strategy, Su-SAL, entails the support of center of gravity trajectory tracking based on a predictive control. Ro-SAL is effective for the stable state and states with internal model error, whereas Su-SAL is effective in states with external force and environmental complexity. In the second stage of the proposed method (AS-SAL), the robot recognizes the current state and selects the optimal combination of the two arm-swing strategies. As a result, a humanoid robot can exhibit more efficient, stable bipedal walking without falling.