Locomotion of snake-like robots using adaptive neural oscillators

This paper proposes a CPG-based control architecture using a frequency-adaptive oscillator for undulatory locomotion of snake-like robots. The control architecture consists of a network of neural oscillators that generates desired oscillatory output signals with specific phase lags. A key feature of the proposed architecture is a self-adaptation process that modulates the parameters of the CPG to adapt the motion of the robot to varying coefficients of body-ground friction. This process is based on the frequency-adaptation rule of the oscillator that is designed to learn the periodicity of sensory feedback signals. It has an important meaning of establishing a closed-loop CPG much more robust against environmental and/or system parameter changes. We verify the validity of the proposed locomotion control system employing a simulated snake-like robot moving over terrains with different friction coefficients with a constant velocity.