Fault tolerance in a multiple robots organization based on an organizational learning model

Abstract

This paper investigates the ability of reorganization in our organizational learning model to maintain the collective performance of multiple robots in terms of fault tolerance. In real applications using these robots, when the membership of robots is changed according to situation or some robots become defective or inoperative, it is necessary for those robots that remain to reform their organization in order to continue to complete given tasks. Through intensive simulations on the same truss construction task, the following experimental results were obtained: (1) Our model enables robots to continue to complete given tasks by reforming their organization, when a membership of robots is changed or some faulty robots are removed, and (2) The number of steps before operation does not increase very much as compared with the steps after operation.