Fault Detection of Multi-Wheeled Robot Consensus Based on EKF

Abstract

Synchronizing a network of robots in consensus is an important task for cooperative work. Detecting faults in a network of robots in consensus is a much more important task. In considering a formation of Wheeled Mobile Robots (WMRs) in a master–slave architecture modeled by graph theory, the main objective of this study was to detect and isolate a fault that appears on a robot of this formation in order to remove it from the formation and continue the execution of the assigned task. In this context, we exploit the extended Kalman filter (EKF) to estimate the state of each robot, generate a residual, and deduce whether a fault exists. The implementation of this technique was proven using a Matlab simulator.

Keywords

• wheeled mobile robot network
• consensus
• graph theory
• master–slave system
• fault detection and isolation (FDI)
• extended Kalman filter