Adaptive behavior acquisition for a distributed autonomous swimming robot based on real-world learning

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.