The control of field robots in varying and uncertain terrain conditions presents a challenge for autonomous navigation. Online estimation of the wheel-terrain slip characteristics is essential for generating the accurate control predictions necessary for tracking trajectories in off-road environments. Receding horizon estimation (RHE) provides a powerful framework for constrained estimation, and when combined with receding horizon control (RHC), yields an adaptive optimisation-based control method. Presently, such methods assume slip to be constant over the estimation horizon, while our proposed structured blocking approach relaxes this assumption, resulting in improved state and parameter estimation. We demonstrate and compare the performance of this method in simulation, and propose an overlapping-block strategy to ameliorate some of the limitations encountered in applying noise-blocking in a receding horizon estimation and control (RHEC) context.
Slip estimation for a skid-steered mobile robot is investigated in @cite_10 , using a KM for analysis of the wheel-slip relationships with respect to the robot motion. Longitudinal slip is modelled as a multiplicative factor, and an extended Kalman filter (EKF) design incorporating kinematic constraints is used to improve estimation accuracy.
@cite_10
2134349615
Skid-steered mobile robots are widely used because of their simple mechanism and high reliability. Understanding the kinematics and dynamics of such a robotic platform is, however, challenging due to the complex wheel ground interactions and kinematic constraints. In this paper, we develop a kinematic modeling scheme to analyze the skid-steered mobile robot. Based on the analysis of the kinematics of the skid-steered mobile robot, we reveal the underlying geometric and kinematic relationships between the wheel slips and locations of the instantaneous rotation centers. As an application example, we also present how to utilize the modeling and analysis for robot positioning and wheel slip estimation using only low-cost strapdown inertial measurement units. The robot positioning and wheel slip-estimation scheme is based on an extended Kalman filter (EKF) design that incorporates the kinematic constraints for accuracy enhancement. The performance of the EKF-based positioning and wheel slip-estimation scheme are also presented. The estimation methodology is tested and validated experimentally on a robotic test bed.