A Fuzzy Ensemble Method With Deep Learning for Multi-Robot System

Abstract

In a multi-robot system, situation assessment evaluates the current situation quantitatively to help decision-makers make the best decision. Conventional situation assessment methods ignore the initiative of each robot, so it often encounters bottlenecks. Collaborative intelligence shows better performance than a single global decision. To address this problem, this work introduces a deep learning-based fuzzy adaptive method (DLFA) to achieve the real-time situation assessment for a multi-robot system. The proposed method employs the shortest path faster algorithm to achieve information sharing between agents. The shortest path faster algorithm ensures that the agent distributes its state information to its teammates in the fastest way. Each agent gets the information from teammates and treats their state as the observation of the scene. Deep neural network maps current observations into a local situation assessment result by combining a large number of nonlinear processing layers. Finally, each local assessment result is regarded as a brick to construct the final situation assessment via a fuzzy ensemble method. Experimental results show that the proposed method outperforms competitors.