Automatic Guided Vehicle (AGV) tracking model estimation with Ensemble Kalman Filter

Many industries have adopted Automatic Guided Vehicles (AGV) into production lines such as automobile factories, food processing, woodworking, and other factories. Therefore, the problem of tracking the trajectory of the AGV system needs to be solved to meet the needs of the industry. The accuracy of a system model is strongly influenced by the completeness of the state in the dynamic system. So that, an estimator is needed to meet the state requirements that cannot be measured. We derive the mathematical model of Automatic Guided Vehicle (AGV) with some assumptions, so we can obtain the non-linear AGV trajectory model. Then, we discrete the non-linear model at first before estimating it with Ensemble Kalman Filter (EnKF) algorithm. In the simulation only two states out of five are observable, so we use observations on literal velocity and yaw rate of AGV system. We use RMSE to validate the accuracy of the EnKF algorithm. The simulation results show that the non-linear AGV model that has been derived can be estimated well with the EnKF algorithm.