In this paper, a tracking algorithm for the autonomous navigation of the automated guided vehicles (AGVs) operated in a container terminal is investigated. The navigation system is based on sensors that detect range and bearing. The navigation algorithm used is an interacting multiple model algorithm to detect other AGVs and avoid obstacles using information obtained from the multiple sensors. In order to detect other AGVs (or obstacles), two kinematic models are derived: A constant velocity model for linear motion and a constant-speed turn model for curvilinear motion. For the constant-speed turn model, an unscented Kalman filter is used because of the drawbacks of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.
In this paper, a tracking algorithm for the autonomous navigation of the automated guided vehicles (AGVs) operated in a container terminal is investigated. The navigation system is based on sensors that detect range and bearing. The navigation algorithm used is an interacting multiple model algorithm to detect other AGVs and avoid obstacles using information obtained from the multiple sensors. In order to detect other AGVs (or obstacles), two kinematic models are derived: A constant velocity model for linear motion and a constant-speed turn model for curvilinear motion. For the constant-speed turn model, an unscented Kalman filter is used because of the drawbacks of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.