To err is robotic, to tolerate immunological: fault detection in multirobot systems
To err is robotic, to tolerate immunological: fault detection in multirobot systems
复制标题
犯错是机器人的事，容忍免疫学的事：多机器人系统中的故障检测
DOI：
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发表时间：
2015
影响因子：
3.4
通讯作者：
Anders Lyhne Christensen
中科院分区：
文献类型：
--
作者：
Danesh Tarapore;Pedro U Lima;Jorge Carneiro;Anders Lyhne Christensen
故障检测和容错是多机器人系统(MRS)领域中最重要也是最未解决的两个问题。高效、长期的运行需要准确、及时地检测和适应行为异常的机器人。大多数现有的容错方法都规定了正常机器人行为的特征，并训练模型来识别这些行为。因此，未被模型识别的行为被标记为异常或故障。使用这些模型的MRS不能很好地过渡到涉及行为时间变化的情景(例如，新行为的在线学习，或对环境扰动的反应)。脊椎动物的免疫系统是一个复杂的分布式系统，能够学习耐受有机体的组织，即使它们在青春期或变态期间发生变化，并对入侵的病原体做出特定的反应，所有这些都不需要遗传上的正常特征。我们提出了一种基于自适应免疫系统模型的通用异常检测方法，并在一群机器人中对该方法进行了评估。我们的结果揭示了对模拟常见机电和软件故障的异常机器人的稳健检测，而与群体行为的时间变化无关。根据群中机器人的数量和行为分类空间的大小，异常检测是可扩展的。
Fault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot behaviours, and train a model to recognize these behaviours. Behaviours unrecognized by the model are consequently labelled abnormal or faulty. MRS employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterization of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space.
影响因子：
2
作者：
DEBOER, RJ;PERELSON, AS
通讯作者：
PERELSON, AS