Fault detection algorithm for automatic guided vehicle based on multiple positioning modules

This paper presents implementation and experimental validation of fault detection algorithm for sensors and motors of Automatic Guided Vehicle (AGV) system based on multiple positioning modules. In this paper, firstly the system description and mathematical model of differential drive AGV system are described. Then, characteristics of each positioning modules are explained. On the next step, the fault detection based on multiple positioning modules is proposed. The fault detection method uses two or more positioning systems and compares them to detect unexpected deviation effected by drift or different characteristics of each positioning systems. For fault detection algorithm, an Extended Kalman Filter (EKF) is used. EKF calculates the measurement probability distribution of the AGV position for nonlinear models driven by Gaussian noise. Using the probability distribution of innovation obtained from EKF, it is possible to test if the measured data are fit with the models. When the faults such as sensors malfunction, wheel slip or motor broken, the models will not be valid and the innovation will not be Gaussian and white. The pairwise differences between the estimated positions obtained from sensors are called as residue. Fault isolation is obtained by examining the biggest residue. Finally, to demonstrate the capability of the proposed algorithm, the algorithm is implemented on a differential drive AGV system, which uses encoder, laser scanner, and laser navigation system to obtain position information. The experimental result shows that the proposed algorithm successfully detects faults when the faults occur.