Energy Efficiency of Force-Sensor-Controlled Humanoid-Robot Walking on Indoor Surfaces

We report an energy-efficiency analysis for the walking pattern of a humanoid robot on different indoor surfaces with different walking speeds. The walking efficiency is measured through experiments for the maximum distance, which can be covered by the robot following specific walking patterns. For this purpose, we developed an energy-measurement-circuit (EMC) to measure power and energy consumption. Two different walking surfaces (i.e. hard and soft-surfaces) and three different walking speeds (i.e. 220 frames/stride (slow-speed), 190 frames/stride (medium-speed) and 160 frames/stride (fast-speed)) were used. The walking pattern was generated by the robot-operating-software platform (ROSP) and the robot controller (i.e. RCB-4HV). Pizo-resistive-membrane force sensors (PRMFS) below the robot feet were used for walking-pattern recording. From the measurement data, it is observed that the humanoid robot with one battery charge can cover on the hard-surface maximum distances of 67.3 m for slow-speed, 77.07 m for medium-speed and 96.24 m for fast-speed. In comparison, the maximum distances on the soft-surface are only 36.94 m for slow-speed, 44.07 m for medium-speed and 55.23 m for high-speed, meaning about 80% higher energy consumption for a given identical distance. It is also observed, that the energy consumption during walking on the hard-surface for 1-meter distance covered (i.e. 181.19 J for slow-speed, 171.13 J for medium-speed and 166.68 J for fast-speed) is comparatively lesser than on the soft-surface (i.e. 365.78 J for slow-speed, 325.23 J for medium-speed and 310.15 J for fast-speed). Our experiments show, that the energy consumption (in %) during walking is substantially smaller on hard surfaces than on soft surfaces, namely, 50.46% for slow-speed, 47.38% for medium-speed and 46.25% for fast-speed. It is further shown, that the fast-speed-walking pattern on a hard surface has the highest energy efficiency among the six analyzed walking conditions. The obtained results are useful for energy-efficient walking-pattern recognition in future-generation artificial-intelligence-enabled humanoid-robot design.