Collaboration among a group of self-autonomous mobile robots with diversified personalities - learning others' personalities results in better task efficiency

Abstract

In a simulated world, a group of robots with diversified personalities performs a task where they carry several pieces of baggage to the destination. In this simulation, each robot makes a mutual concession to avoid collision and helps each other to carry some of baggage too big to be carried by one robot. Moreover, each robot makes learning the personalities of other robot in this simulation. We verify the influence that the learning the personalities exerts on the task efficiency. We give each robot three kinds of personalities; positivity, tenderness and temper. From the result, it was found that each robot has more versatile actions when each robot learns the personalities of the other robots, and the versatile actions of the robot result in better task efficiency. Thus, in this study, the learning of the others' personalities worked effectively, as in the case of biological evolution.