Fuzzy gain scheduled EKF for model-based Skid-Steered Mobile Robot

Abstract

This article describes an approach to autonomous robotic for agricultural applications. Technological setup aims at stable navigation based on estimation through Extended Kalman filtering (EKF), to enforce robust Skid-Steered Mobile Robot (SSMR) navigation. The scientific contribution is the implementation of two model-based estimators, using EKF algorithms, one on a nonlinear model, and one on a piece-wise linearized robot model. The later is a Fuzzy Gain Scheduled (FGS)-based development. The process is taking into account tire-road modelling of friction forces in order to improve model performance. State estimation and correction using sensor data fusion (Odometry-IMU-GPS) is considered, to improve the SSMR control in critical motions, reducing inherent drifts due to skid-steer properties; for the purpose of better regulation and tracking control designs. Whilst the experimental results demonstrated the usefulness of FGS approach for optimal EKF estimation, further modelling and live testing are required to determine robot ability to cope with different scenarios in naturally varying environment.