Predicting Fault Behaviors of Networked Control Systems Using Deep Learning for Mobile Robots

Abstract

The field of robotics research is continuously expanding at an ever-increasing rate. So much so, that as a systems' complexity grows, so too does the amount of possible points of failure. In recent years, these systems have been integrated together to create systems of systems, dramatically increasing the fragility of these networked systems, also known as a swarm. This paper presents a method for abstracting the fault of a networked control system, namely a system of mobile robots, into general feature sets and producing the capability of predicting the present fault as well as the compensation thereof.