Obstacle avoidance learning for a multi-agent linked robot in the real world

Abstract

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.