From fireflies to fault-tolerant swarms of robots
IEEE Transactions on Evolutionary Computation
Distributed sensor analysis for fault detection in tightly-coupled multi-robot team tasks
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Towards group transport by swarms of robots
International Journal of Bio-Inspired Computation
Abnormality detection in multiagent systems inspired by the adaptive immune system
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In this paper, we study a new approach to fault detection for autonomous robots. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data from three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of number of false positives and time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot's sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task, and we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.