@inproceedings{e828e77483ef4ef79365c2f0889a5a9f,
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.",
keywords = "Genetic testing method, Swarm robotics",
author = "Hao Wei and Jon Timmis and Rob Alexander",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Congress on Evolutionary Computation, CEC 2017 ; Conference date: 05-06-2017 Through 08-06-2017",
year = "2017",
month = jul,
day = "5",
doi = "10.1109/CEC.2017.7969408",
language = "English",
series = "2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings",
publisher = "IEEE Press",
pages = "929--935",
booktitle = "2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings",
address = "United States of America",
}