An IMM algorithm with federated information mode‐matched filters for AGV

In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) is presented. The developed navigation algorithm is an interacting multiple‐model (IMM) algorithm used to detect other AGVs using fused information from multiple sensors. In order to detect other AGVs, two kinematic models were derived: A constant‐velocity model for linear motion, and a constant‐speed turn model for curvilinear motion. In the constant‐speed turn model, a nonlinear information filter (IF) is used in place of the extended Kalman filter (KF). Being equivalent to the KF algebraically, the IF is extended to N‐sensor distributed dynamic systems. The model‐matched filter used in multi‐sensor environments takes the form of a federated nonlinear IF. In multi‐sensor environments, the information‐based filter is easier to decentralize, initialize, and fuse than a KF‐based filter. In this paper, the structural features and information‐sharing principle of the federated IF are discussed. The performance of the suggested algorithm using a Monte Carlo simulation is evaluated under the three navigation patterns. Copyright © 2006 John Wiley & Sons, Ltd.