Locomotion selection strategy for multi-locomotion robot based on stability and efficiency

This paper shows improvement of stability and efficiency for mobility using locomotion selection strategy. First strategy is the selection of a gait relying on locomotion rewards. The locomotion reward has been proposed as an indicator for selection algorithm based on Falling Risk and the moving speed. This strategy has achieved a capability of large changes of uncertainties, such as a steep slope. Second strategy is adjustment of moving speed by the extended locomotion reward that explicitly shows the relationship between the moving speed and Falling Risk. The robot aims at the maximum moving speed without a falling, and removes small changes of uncertainties as a result. We performed an experiment in order to confirm effects of two strategies in an environment that includes a rough terrain as a small uncertainty and two steps as a large uncertainty. The robot improved the moving speed about 37.5% from the case of only using the gait selection strategy.