In order to achieve an autonomous system which can adaptively behave through learning in the real world, we have constructed a distributed autonomous swimming robot that consists of mechanically linked multi-agent and adopts adaptive oscillator method that was developed as a general decision making for distributed autonomous systems. One of the aims of using this system is to verify whether the robot could complete a target approaching including obstacle avoidance. For this purpose, we introduce a modified Q-learning in which plural Q-tables are used alternately according to dead-lock situations. By using this system, as a result, the robot acquires a stable target approaching and obstacle avoiding behavior.

Proposes the construction of a "strong" autonomous mobile robot, which can acquire environment oriented behavior through learning, as a distributed autonomous system. It is thought that such a system has many advantages over other systems in terms of adaptability to the environment and so on. However, the potential of this type of system has yet to be demonstrated in experiments under real-world conditions. We conducted an experiment to determine whether a distributed autonomous swimming robot could acquire target-approaching behavior on a water surface which was set as the robot's work space. As a result, from a fairly simple coding, the robot acquired the reproducible target-approaching behavior using only local learning even in cases where a partial fault occurred, and the acquired actions also enabled the robot to approach the target in an environment with a narrow gate.