Using Bayesian Network for Robot Behavior with Sensor Fault

摘要

This paper presents the framework that is combination Bayesian networks and fault detection that all of them learning a behavior of the robot. Fault removing is performed by changing the Bayesian network structure using interpreted evidences from robot sensors. Both simulation and real robot show that these frameworks can perform door crossing behavior properly by using prior knowledge and sensors data and it is robust to the changing of map. The motivation for this research comes from the need for an Instrument Fault Detection and Isolation (IFDI) method that is applicable to robotics. We apply Bayesian network to the task of fault diagnosis in a complex behavior of mobile robot like door crossing. Bayesian networks have important features such as representing direct causal dependency and ability to imitate thinking like human.