This study aims to design a nervous system model to drive the realistic muscle-driven legs for the locomotion of a quadruped robot. We evaluate our proposed nervous system model with a hind leg simulated model and robot. We apply a two-level central pattern generator 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 central pattern generator receives sensory feedback from leg loading. A cat simulated model and a robot with two hind legs, each with three joints driven by six muscle models, are controlled by our nervous system model. Even though their hind legs are forced backward at a wide range of speeds, they can adapt to the speed variation by autonomously adjusting its stride and cyclic duration without changing any parameters or receiving any descending inputs. In addition to the autonomous speed adaptation, the cat hind leg robot switched from a trot-like gait to a gallop-like gait while speeding up. These features can be observed in existing animal locomotion tests. These results demonstrate that our nervous system is useful as a valid and practical legged locomotion controller.

In this study, we discuss omni-directional walking of a quadruped robot to walk on a slope. A method for successive gait-transition on a slope with specified body posture is introduced. Using this method, a trade-off between the moving speed and the body posture has been verified. Simulations and experiments showed that the proposed method for stable slope walking of a quadruped robot is valid.

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.

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