Learning to Avoid Objects and Dock with a Mobile Robot

Abstract

In this paper we describe a novel robot learning method that enables a mobile robot equipped with sonar and IR light sensors to automatically acquire the ability to negotiate objects and dock by simply interacting with the environment. We achieve this by providing the robot with sonar and IR sensors for detecting objects and the relative direction of IR beacons placed in the environment. A set of fuzzy associative maps (FAMs) is also provided to the robot for learning associations between sonar sensor data, immediate trajectories and appropriate velocities for traversing trajectories. Learning is performed in real time without the credit assignment problem by training each FAM with training data acquired from sonar sensors and the robots interactions with the environment.