O'Keeffe James, Tarapore Danesh, Millard Alan G, Timmis Jon
Department of Electronic Engineering, University of York, York, United Kingdom.
School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom.
Front Robot AI. 2018 Nov 30;5:131. doi: 10.3389/frobt.2018.00131. eCollection 2018.
Previous work has shown that robot swarms are not always tolerant to the failure of individual robots, particularly those that have only partially failed and continue to contribute to collective behaviors. A case has been made for an active approach to fault tolerance in swarm robotic systems, whereby the swarm can identify and resolve faults that occur during operation. Existing approaches to active fault tolerance in swarms have so far omitted fault diagnosis, however we propose that diagnosis is a feature of active fault tolerance that is necessary if swarms are to obtain long-term autonomy. This paper presents a novel method for fault diagnosis that attempts to imitate some of the observed functions of natural immune system. The results of our simulated experiments show that our system is flexible, scalable, and improves swarm tolerance to various electro-mechanical faults in the cases examined.
先前的研究表明，机器人集群并不总是能容忍单个机器人出现故障，尤其是那些只是部分出现故障并继续对集体行为产生影响的机器人。有人提出了一种在群体机器人系统中实现主动容错的方法，即集群能够识别并解决运行过程中出现的故障。然而，目前群体机器人主动容错的现有方法都忽略了故障诊断，我们认为，如果集群要实现长期自主性，故障诊断是主动容错的一个必要特征。本文提出了一种新颖的故障诊断方法，该方法试图模仿自然免疫系统的一些观察到的功能。我们的模拟实验结果表明，我们的系统具有灵活性、可扩展性，并且在所研究的案例中提高了集群对各种机电故障的容忍度。
