Monitoring malfunction in multirobot formation with a neural network detector

Abstract

The detection of the malfunction of an individual robot is an essential issue in the control of multirobot systems. In this paper, a malfunction is defined in terms of a robot’s motion performance. A monitoring system to detect robot malfunctions is developed in the form of a centralized high-level planner. The monitoring system collects data on the positions and any formation errors of each robot and evaluates working performance in terms of current and past states. A back-propagation neural-network-based controller is developed to evaluate the status of the robots, i.e. working or failed. The neural network is trained using both positive and negative examples. Simulations and experiments are performed to demonstrate the effectiveness of the proposed approach.