A robust slip estimation method for skid-steered mobile robots

Abstract

This paper presents a robust slip estimation method for skid-steered mobile robots when they traverse over rough terrain. An optical flow-based visual sensor looking down the terrain surface is employed to recover motion of a mobile robot by tracking features selected from the terrain surface. The motion states of the mobile robot are initially estimated by the visual sensor, however, the estimates are prone to noise and uncertainty which degrades the accuracy and robustness of estimation. To cope with the noise and uncertainty from the visual sensor, a sliding mode observer (SMO) based on the kinematics model of the skid-steered mobile robot is delicately designed to simultaneously estimate slip parameters. The SMO scheme can give more accurate estimates than the extended Kalman filter (EKF) when the slip of the mobile robot has significant changes at abrupt steering. The complete slip estimation method is independent of terrain parameters and robust in the presence of noise and uncertainty. Experimental results show that the method has confident potential for slip estimation of skid-steered mobile robots.