Learning Improvements in Mobile Robot Behavior with Faulty Sensors Using Bayesian Network

In this paper a new structure based on Bayesian networks is presented to improve mobile robot behavior, in which there exist faulty robot sensors. If a robot likes to follow certain behavior in the environment to reach its goal, it must be capable of making inference and mapping based on prior knowledge and also should be capable of understanding its reactions on the environment over time. Old learning models for knowledge learning, especially on dynamic environment, are quite complex and have uncertainty in sensors. In this paper a new structure based on Bayesian network is presented for knowledge learning on robot behavior when the malfunction sensors exist. In this paper successful door crossing behavior is explained. In this issue the belt of ultrasonic sensors is used to receive environment information. Simulation results show that using the Bayesian network is very effective in robot behavior with faulty sensors.