Instruction System for Autonomous Distributed Agents Using Miniature Robots

Abstract

Studies of autonomous and decentralized robot systems have mainly focused on efficient coordination controllers that lead an individual robot contributes to a society or a group which the robot belongs to based on a status evaluation of the society. It is sometimes difficult to design the controller beforehand because of unpredictable disturbances of the environment, condition changing, and so on. Learning approaches for the cooperative behavior in the society are attractable because of their adaptation capability, however, it takes, in general, an unpractical long learning time to reach an acceptable level of social behavior as the result of individual robot behavior learning from scratch. Therefore, it is reasonable to introduce human instructions in order to realize a practical and efficient adaptive controller for the autonomous distributed robots with minimum autonomy. However, conventional human interactions sometimes are not intuitive or effective for the multi-robot systems. In this paper, a human-friendly intuitive instruction system for autonomous distributed mobile robots is proposed. The instruction system includes miniature robots that can be picked up and moved by hands so that the human operators can intuitively instruct cooperative behavior to the autonomous distributed robots.