Evolving Test Environments to Identify Faults in Swarm Robotics Algorithms

Hao Wei; Jonathan Ian Timmis; Robert David Alexander

Evolving Test Environments to Identify Faults in Swarm Robotics Algorithms

Hao Wei, Jonathan Ian Timmis, Robert David Alexander

Research output: Contribution to conference › Paper › peer-review

Abstract

Swarm robotic systems are often considered to be dependable. However, there is little empirical evidence or theoretical analysis showing that dependability is an inherent property of all swarm robotic system. Recent literature has identified potential issues with respect to dependability within certain types of swarm robotic algorithms. There appears to be a dearth of literature relating to the testing of swarm robotic systems; this provides motivation for the development of the novel testing methods for swarm robotic systems presented in this paper. We present a search based approach, using genetic algorithms, for the automated identification of unintended behaviors during the execution of a flocking type algorithm, implemented on a simulated robotic swarm. Results show that this proposed approach is able to reveal faults in such flocking algorithms and has the potential to be used in further swarm robotic applications.

Original language	English
Publication status	Published - 6 Jun 2017
Event	IEEE Congress on Evolutionary Computation 2017 - Donostia - San Sebastián, Spain Duration: 5 Jun 2017 → 8 Jun 2017

Conference

Conference	IEEE Congress on Evolutionary Computation 2017
Abbreviated title	CEC
Country/Territory	Spain
City	Donostia - San Sebastián
Period	5/06/17 → 8/06/17

Bibliographical note

©2017 Crown. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

Access to Document

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©2017 Crown. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details
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Cite this

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title = "Evolving Test Environments to Identify Faults in Swarm Robotics Algorithms",

abstract = "Swarm robotic systems are often considered to be dependable. However, there is little empirical evidence or theoretical analysis showing that dependability is an inherent property of all swarm robotic system. Recent literature has identified potential issues with respect to dependability within certain types of swarm robotic algorithms. There appears to be a dearth of literature relating to the testing of swarm robotic systems; this provides motivation for the development of the novel testing methods for swarm robotic systems presented in this paper. We present a search based approach, using genetic algorithms, for the automated identification of unintended behaviors during the execution of a flocking type algorithm, implemented on a simulated robotic swarm. Results show that this proposed approach is able to reveal faults in such flocking algorithms and has the potential to be used in further swarm robotic applications. ",

author = "Hao Wei and Timmis, {Jonathan Ian} and Alexander, {Robert David}",

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AU - Timmis, Jonathan Ian

AU - Alexander, Robert David

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N2 - Swarm robotic systems are often considered to be dependable. However, there is little empirical evidence or theoretical analysis showing that dependability is an inherent property of all swarm robotic system. Recent literature has identified potential issues with respect to dependability within certain types of swarm robotic algorithms. There appears to be a dearth of literature relating to the testing of swarm robotic systems; this provides motivation for the development of the novel testing methods for swarm robotic systems presented in this paper. We present a search based approach, using genetic algorithms, for the automated identification of unintended behaviors during the execution of a flocking type algorithm, implemented on a simulated robotic swarm. Results show that this proposed approach is able to reveal faults in such flocking algorithms and has the potential to be used in further swarm robotic applications.

AB - Swarm robotic systems are often considered to be dependable. However, there is little empirical evidence or theoretical analysis showing that dependability is an inherent property of all swarm robotic system. Recent literature has identified potential issues with respect to dependability within certain types of swarm robotic algorithms. There appears to be a dearth of literature relating to the testing of swarm robotic systems; this provides motivation for the development of the novel testing methods for swarm robotic systems presented in this paper. We present a search based approach, using genetic algorithms, for the automated identification of unintended behaviors during the execution of a flocking type algorithm, implemented on a simulated robotic swarm. Results show that this proposed approach is able to reveal faults in such flocking algorithms and has the potential to be used in further swarm robotic applications.

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