Trajectory tracking and fault detection algorithm for automatic guided vehicle based on multiple positioning modules

• Published in: International journal of control, automation, and systems Vol. 14; no. 2; pp. 400 - 410
• Main Authors: Pratama, Pandu Sandi, Gulakari, Amruta Vinod, Setiawan, Yuhanes Dedy, Kim, Dae Hwan, Kim, Hak Kyeong, Kim, Sang Bong
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.04.2016; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Algorithms; Analysis; Automated guided vehicles; Autonomous vehicles; Control; Control systems; Controllers; Engineering; Fault detection; Faults; Kalman filters; Lasers; Mathematical models; Mechatronics; Modules; Navigation systems; Noise; Position tracking; Regular Papers; Residues; Robotics; Sensors; Studies; Trajectories; Vehicles; Wheels; 제어계측공학; multiple positioning; extended Kalman filter; Automatic guided vehicle; fault detection
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-014-0294-y

This paper presents an implementation and experimental validation of trajectory tracking and fault detection algorithm for sensors and actuators of Automatic Guided Vehicle (AGV) system based on multiple positioning modules. Firstly, the system description and the mathematical modeling of the differential drive AGV system are described. Secondly, a trajectory tracking controller based on the backstepping method is proposed to track the given trajectory. Thirdly, a fault detection algorithm based on the multiple positioning modules is proposed. The AGV uses encoders, laser scanner, and laser navigation system to obtain the position information. To understand the characteristics of each positioning module, their modeling are explained. The fault detection method uses two or more positioning systems and compares them using Extended Kalman Filter (EKF) to detect an unexpected deviation effected by fault. The pairwise differences between the estimated positions obtained from the sensors are called as residue. When the faults occur, the residue value is greater than the threshold value. Fault isolation is obtained by examining the biggest residue. Finally, to demonstrate the capability of the proposed algorithm, it is applied to the differential drive AGV system. The simulation and experimental results show that the proposed algorithm successfully detects the faults when the faults occur.