Integration of multi sensors for adaptive walking of a quadruped robot

Abstract

We have been trying to induce a quadruped robot to walk with medium walking speed on irregular terrain based on biological concepts. A PD-controller at joints constructs virtual spring-damper systems as the visco-elasticity model of a muscle. The neural system model consists of a CPG (central pattern generator) and reflexes. A CPG receives sensory input and changes the period of its own active phase. The desired angle and P-gain of each joint in the virtual spring-damper systems is switched based on the phase signal of the CPG. CPGs , the motion of the virtual spring-damper system of each leg and the rolling motion of the body are mutually entrained through rolling motion feedback to CPGs, and can generate adaptive walking. The adaptation on irregular terrain based on multi sensors including vision is integrated in the neural system centering the CPGs. MPEG footage of these experiments can be seen at: http://www.kimura.is.uec.ac.jp.