STATE ESTIMATION FOR AUTONOMOUS GUIDED VEHICLE USING THE EXTENDED KALMAN FILTER

Abstract

This paper presents a four-wheel drive autonomous guided vehicle (AGV) system designed to transport unmanned control transportation (UCT) standard cargo containers in seaport environments. A model vehicle is simulated and experimented in laboratory and in a prepared road at speed up to 3 m/s. The navigation system is based on the use of encoder, gyro, and transponders at known locations in the environment. A general method for the construction of a positioning system is proposed, which is based on an extended Kalman filter (EKF) and commercially available navigation sensors in an absolute coordinate of AGV. The kinematics model and observation models are adapted for EKF application. Simulation result shows good performances of the AGV state estimator.

This paper presents a four-wheel drive autonomous guided vehicle (AGV) system designed to transport unmanned control transportation (UCT) standard cargo containers in seaport environments. A model vehicle is simulated and experimented in laboratory and in a prepared road at speed up to 3 m/s. The navigation system is based on the use of encoder, gyro, and transponders at known locations in the environment. A general method for the construction of a positioning system is proposed, which is based on an extended Kalman filter (EKF) and commercially available navigation sensors in an absolute coordinate of AGV. The kinematics model and observation models are adapted for EKF application. Simulation result shows good performances of the AGV state estimator.