A Behavior Based Control and Learning Approach to Real Robots

Abstract

Programming a real robot to do a given task in unstructured dynamic environments is very challenge. Incomplete information, large learning space, and uncertainty are major obstacles for control in real robots. When programming a real robot in unstructured dynamic environments, it is impossible to predict all the potential situation robots may encounter and specify all robot behaviors optimally in advance. Robots have to learn from, and adapt to their operating environment.In this chapter, we propose to use fuzzy logic to design robot behaviors and use a Markov decision process to model the coordination mechanism in the control and learning of real autonomous robotic systems. Based on the model, a Q-learning approach can be used to learn the behavior coordination. Two real robot applications are implemented by using such an approach, one is a Sony quadruped robot for soccer playing and another is a robotic fish for entertainment. Real robot testing results are provided to verify the proposed approach.KeywordsFuzzy LogicFuzzy RuleFuzzy ControllerAutonomous RobotReal RobotThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.