Organizational learning model for adaptive collective behaviors in multiple robots

Abstract

This paper proposes a novel organizational learning model in which multiple robots acquire their own functions for adaptive collective behaviors through local interactions among their neighbors and form an organizational structure to complete given tasks without global explicit control mechanisms or communication methods. In this paper, we focus on emergent processes in which robots dynamically form an organizational structure by acquiring their own appropriate functions to complete given tasks effectively and also focus on how organizational knowledge supports robots to reform their organizational structure. Through intensive simulations of truss construction by multiple robots, the following experimental results have suggested: (1) robots in our model acquire their own appropriate functions without global explicit control mechanisms or communication methods and form an organizational structure which completes given tasks in less steps than those with a centralized control system, and (2) organizational ...