Fault detection in autonomous robots

In this dissertation, we study two new approaches to fault detection for autonomous robots. The first approach involves the synthesis of software components that give a robot the capacity to detect faults which occur in itself. Our hypothesis is that hardware faults change the flow of sensory data a...

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Main Author: Christensen, Anders Lyhne
Other Authors: Dorigo, Marco
Format: Doctoral Thesis
Language:fr
Published: Universite Libre de Bruxelles 2008
Subjects:
Online Access:http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210508
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spelling ndltd-ulb.ac.be-oai-dipot.ulb.ac.be-2013-2105082018-04-11T17:34:50Z info:eu-repo/semantics/doctoralThesis info:ulb-repo/semantics/doctoralThesis info:ulb-repo/semantics/openurl/vlink-dissertation Fault detection in autonomous robots Christensen, Anders Lyhne Dorigo, Marco Bersini, Hugues Lima, Pedro Birattari, Mauro Stutzle, Thomas Bontempi, Gianluca Lanzi, Pier Parker, Lynne Universite Libre de Bruxelles Université libre de Bruxelles, Faculté des sciences appliquées – Informatique, Bruxelles 2008-06-27 fr In this dissertation, we study two new approaches to fault detection for autonomous robots. The first approach involves the synthesis of software components that give a robot the capacity to detect faults which occur in itself. 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 in three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots both 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 the number of false positives and the time it takes to detect a fault.<p>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. Finally, we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.<p><p>The second approach involves the use of firefly-inspired synchronization to allow the presence of faulty robots to be determined by other non-faulty robots in a swarm robotic system. We take inspiration from the synchronized flashing behavior observed in some species of fireflies. Each robot flashes by lighting up its on-board red LEDs and neighboring robots are driven to flash in synchrony. The robots always interpret the absence of flashing by a particular robot as an indication that the robot has a fault. A faulty robot can stop flashing periodically for one of two reasons. The fault itself can render the robot unable to flash periodically.<p>Alternatively, the faulty robot might be able to detect the fault itself using endogenous fault detection and decide to stop flashing.<p>Thus, catastrophic faults in a robot can be directly detected by its peers, while the presence of less serious faults can be detected by the faulty robot itself, and actively communicated to neighboring robots. We explore the performance of the proposed algorithm both on a real world swarm robotic system and in simulation. We show that failed robots are detected correctly and in a timely manner, and we show that a system composed of robots with simulated self-repair capabilities can survive relatively high failure rates.<p><p>We conclude that i) fault injection and learning can give robots the capacity to detect faults that occur in themselves, and that ii) firefly-inspired synchronization can enable robots in a swarm robotic system to detect and communicate faults.<p> Sciences de l'ingénieur Informatique générale Autonomous robots Swarm intelligence Fault-tolerant computing Robots autonomes Intelligence collective Tolérance aux fautes (Informatique) Autonomous Robots Fault Detection Fault Injection Synchronization Swarm Robotics 1 v. Doctorat en Sciences de l'ingénieur info:eu-repo/semantics/nonPublished local/bictel.ulb.ac.be:ULBetd-05202008-163248 local/ulbcat.ulb.ac.be:822656 http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210508 No full-text files
collection NDLTD
language fr
format Doctoral Thesis
sources NDLTD
topic Sciences de l'ingénieur
Informatique générale
Autonomous robots
Swarm intelligence
Fault-tolerant computing
Robots autonomes
Intelligence collective
Tolérance aux fautes (Informatique)
Autonomous Robots
Fault Detection
Fault Injection
Synchronization
Swarm Robotics
spellingShingle Sciences de l'ingénieur
Informatique générale
Autonomous robots
Swarm intelligence
Fault-tolerant computing
Robots autonomes
Intelligence collective
Tolérance aux fautes (Informatique)
Autonomous Robots
Fault Detection
Fault Injection
Synchronization
Swarm Robotics
Christensen, Anders Lyhne
Fault detection in autonomous robots
description In this dissertation, we study two new approaches to fault detection for autonomous robots. The first approach involves the synthesis of software components that give a robot the capacity to detect faults which occur in itself. 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 in three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots both 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 the number of false positives and the time it takes to detect a fault.<p>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. Finally, we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.<p><p>The second approach involves the use of firefly-inspired synchronization to allow the presence of faulty robots to be determined by other non-faulty robots in a swarm robotic system. We take inspiration from the synchronized flashing behavior observed in some species of fireflies. Each robot flashes by lighting up its on-board red LEDs and neighboring robots are driven to flash in synchrony. The robots always interpret the absence of flashing by a particular robot as an indication that the robot has a fault. A faulty robot can stop flashing periodically for one of two reasons. The fault itself can render the robot unable to flash periodically.<p>Alternatively, the faulty robot might be able to detect the fault itself using endogenous fault detection and decide to stop flashing.<p>Thus, catastrophic faults in a robot can be directly detected by its peers, while the presence of less serious faults can be detected by the faulty robot itself, and actively communicated to neighboring robots. We explore the performance of the proposed algorithm both on a real world swarm robotic system and in simulation. We show that failed robots are detected correctly and in a timely manner, and we show that a system composed of robots with simulated self-repair capabilities can survive relatively high failure rates.<p><p>We conclude that i) fault injection and learning can give robots the capacity to detect faults that occur in themselves, and that ii) firefly-inspired synchronization can enable robots in a swarm robotic system to detect and communicate faults.<p> === Doctorat en Sciences de l'ingénieur === info:eu-repo/semantics/nonPublished
author2 Dorigo, Marco
author_facet Dorigo, Marco
Christensen, Anders Lyhne
author Christensen, Anders Lyhne
author_sort Christensen, Anders Lyhne
title Fault detection in autonomous robots
title_short Fault detection in autonomous robots
title_full Fault detection in autonomous robots
title_fullStr Fault detection in autonomous robots
title_full_unstemmed Fault detection in autonomous robots
title_sort fault detection in autonomous robots
publisher Universite Libre de Bruxelles
publishDate 2008
url http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210508
work_keys_str_mv AT christensenanderslyhne faultdetectioninautonomousrobots
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