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Generic, scalable and decentralized fault detection for robot swarms

Generic, scalable and decentralized fault detection for robot swarms
Generic, scalable and decentralized fault detection for robot swarms
Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system’s capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation.
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Anders Lyhne, Christensen
97736755-1c9b-4472-99b4-98e1b4a9c647
Timmis, Jon
b68f4b8e-6192-4caf-858d-8185f6e7c66f
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Anders Lyhne, Christensen
97736755-1c9b-4472-99b4-98e1b4a9c647
Timmis, Jon
b68f4b8e-6192-4caf-858d-8185f6e7c66f

Tarapore, Danesh, Anders Lyhne, Christensen and Timmis, Jon (2017) Generic, scalable and decentralized fault detection for robot swarms. PLoS ONE, 12 (8). (doi:10.1371/journal.pone.0182058).

Record type: Article

Abstract

Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system’s capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation.

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Accepted/In Press date: 11 July 2017
e-pub ahead of print date: 14 August 2017

Identifiers

Local EPrints ID: 413206
URI: https://eprints.soton.ac.uk/id/eprint/413206
PURE UUID: 7fc550ee-55a4-4d10-bb04-c6bb9d795b06
ORCID for Danesh Tarapore: ORCID iD orcid.org/0000-0002-3226-6861

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Date deposited: 17 Aug 2017 16:30
Last modified: 12 Nov 2019 01:28

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