Optimal use of arm-swing for bipedal walking control

Walking capability composed of stability and efficiency is one of the most important issues in the field of humanoid robots. An effective swing of the arms is expected to enhance the walking capability under the constraints from the limited body. We propose an arm-swing method to enhance the stability and efficiency by selecting optimal arm-swing strategy depending on the walking conditions. In this research, we select the optimal strategy between the support of the center of gravity (COG) tracking for stability and the walk without arm-swing for efficiency. To support the COG tracking, we employ a predictive control. States are defined as an inverted pendulum model and inputs are given as an inertial force of arm-swing. Input and output weights in the predictive control are adjustable by a support weight introduced in this paper. Selection of the optimal support weight by a selection algorithm for locomotion (Su-SAL) switches the two strategies by adjusting the ratio of input and output (I/O) weights. Su-SAL maximizes the efficiency while keeping the stability in comparison with the case of the constant support weight.