NDLI: Fault detection in autonomous robots based on fault injection and learning

Content Provider	Springer Nature Link
Author	Christensen, Anders Lyhne O’Grady, Rehan Birattari, Mauro Dorigo, Marco
Copyright Year	2007
Abstract	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.
Starting Page	49
Ending Page	67
Page Count	19
File Format	PDF
ISSN	09295593
Journal	Autonomous Robots
Volume Number	24
Issue Number	1
e-ISSN	15737527
Language	English
Publisher	Springer US
Publisher Date	2007-11-17
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fault detection Fault injection Learning Model-free Mobile robots Simulation and Modeling Mechanical Engineering Computer Imaging, Vision, Pattern Recognition and Graphics Electronic and Computer Engineering Automation and Robotics Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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