Locomotion of snake-like robots using adaptive neural oscillators
Jae-Kwan Ryu, Nak Young Chong, Bum Jae You, Henrik I. Christensen, Intelligent Service Robotics, January 2010, Volume 3, Issue 1.
Abstract
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.
How Multibody Dynamics Simulation Technology is Used
A proposed CPG-based control architecture enhanced performance of lateral undulatory locomotion of snake-like robots. This method was verified by running RecurDyn simulations over a variety of different ground friction conditions. The lateral undulatory locomotion was proven to be an effective method of locomotion for snake-like robots.
