Autonomous gait modulation in a cat hind leg simulation model based on the spinal nervous system

Abstract

This study aims to design a nervous system model to drive the realistic muscle-driven legs for quadrupedal robot locomotion. In this paper, we evaluate the nervous system model. We apply a two-level central pattern generator (CPG) for each leg, which generates locomotion rhythms and reproduces cat-like leg trajectories by driving different sets of the muscles at any timing during one cycle of moving the leg. The CPG received a sensory feedback of leg loading. A cat model, which has two hind legs with three joints driven by six muscle models, is controlled by our nervous system model. The cat's hind leg model was led at an arbitrary speed by active wheel attached in front of its torso. Then, this model changed own stride length and cycle duration in proportion to its speed and kept walking without changing any parameters, when the locomotion speed is forcibly increased by an external force of active wheel. In particular, it indicates that this CPG adapts to changes in the physical state due to external factors without a involvement of a higher brain, because we did not change the descending signal intensity from the higher brain at this time. Since similar phenomena have been reported in animal experiments, our results demonstrate that our nervous system may be an appropriate model.