Traction Estimation and Control for Mobile Robots

Mobile Robots are used to venture through types of environments where wheel slip is a threat. Wheel slip is a hazard to mobile robots in that it introduces error in dead reckoning measurement and in some instances causes the robot to halt is forward progress. To compensate for traction loss several methods are used to determine the terrain characteristics. One of these methods is Pacejka’s Tire Model. The slope of Pacejka’s Tire Model can be used to determine when traction loss occurs. One step toward realizing the slope of Pacejka’s Tire Model is achieving a good estimate of wheel slip. We present a unique traction estimation algorithm that estimates traction loss by measuring the wheel slip velocity and its derivative. Our algorithm estimates the wheel slip velocity and its derivative by coupling the dynamics of a wheel with the dynamics of a vehicle. Estimates of the wheel slip velocity and its derivative are accomplished using onboard sensors. To obtain an accurate estimate of the wheel slip velocity and its derivative, we propose a modified Kalman Filter that fuses a system model of a DC motor with an estimate of the disturbances acting on the system model. Using the wheel slip velocity and its derivative a neighborhood can be defined between two instances in time that estimates when traction loss occurring. ii With means of estimating traction loss, we propose a traction control law that provides the ability of tracking a desired reference while mitigating traction loss. To solve the tracking problem we propose a robust tracking controller that provides the ability of following a defined path and rejecting unmodled disturbance. To mitigate traction loss we propose a continuous robust traction controller to maximize traction forces by containing wheel slip and its derivative to a neighborhood. The unique aspect of our traction controller is it works jointly with our proposed tracking controller.