Trajectory Estimation of Automated Guided Vehicle Based on Linear Modelling Using Ensemble Kalman Filter

Abstract

A robot is a mechanical device that can perform physical tasks, either using human supervision and control,using programs that utilize the principles of artificial intelligence. One type of robot that is widely developed today is theAutomated Guided Vehicle (AGV). One of the principles of artificial intelligence in AGV is, When AGV moves from oneplace to another using path guidance located along the AGV path. The position monitoring system is the most importantpart of the AGV. The navigation system of mobile vehicles can be built using a relating position sensor or using an absoluteposition sensor. Some mobile vehicles in the world of robotics are already accustomed to using position estimation as theirnavigation system. Starting with the preparation of a mathematical model of the AGV movement in the form of a non-linearmodel, then linearization of the non-linear model is carried out with the Jacobi matrix. The linear model above is a platformfor carrying out the navigation and guidance system of the AGV. The main objective of this study is to maintain positionaccuracy continuously applied trajectory estimation to AGV navigation and guidance with the trajectory estimation method,namely the Ensemble Kalman Filter. The simulation results show that by generating 500 ensembles, the best accuracy levelis around 99.45%. Overall, from the three simulations carried out, an accuracy level of around 97.8% - 99.45% was obtained.