TY - JOUR
T1 - Fault Detection in a Swarm of Physical Robots Based on Behavioral Outlier Detection
AU - Tarapore, Danesh
AU - Timmis, Jon
AU - Christensen, Anders Lyhne
N1 - Funding Information:
Manuscript received January 25, 2019; accepted July 4, 2019. Date of publication August 5, 2019; date of current version December 3, 2019. This paper was recommended for publication by Associate Editor S.-J. Chung and Editor P. Robuffo Giordano upon evaluation of the reviewers’ comments. The work of D. Tarapore was supported by a Marie Curie Intra-European Fellowship (GiFteD-MrS/623620) and in part by the Engineering and Physical Sciences Research Council (EPSRC) New Investigator Award under Grant EP/R030073/1. The work of J. Timmis was supported by the EPSRC under Grant EP/K040820/1. (Corresponding author: Danesh Tarapore.) D. Tarapore is with the School of Electronics and Computer Science, University of Southampton, SO17 1BJ Southampton, U.K., and also with York Robotics Laboratory and the Department of Electronic Engineering, University of York, YO10 5DD York, U.K. (e-mail: [email protected]).
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - The ability to reliably detect faults is essential in many real-world tasks that robot swarms have the potential to perform. Most studies on fault detection in swarm robotics have been conducted exclusively in simulation, and they have focused on a single type of fault or a specific task. In a series of previous studies, we have developed a robust fault-detection approach in which robots in a swarm learn to distinguish between normal and faulty behaviors online. In this paper, we assess the performance of our fault-detection approach on a swarm of seven physical mobile robots. We experiment with three classic swarm robotics tasks and consider several types of faults in both sensors and actuators. Experimental results show that the robots are able to reliably detect the presence of hardware faults in one another even when the swarm behavior is changed during operation. This paper is thus an important step toward making robot swarms sufficiently reliable and dependable for real-world applications.
AB - The ability to reliably detect faults is essential in many real-world tasks that robot swarms have the potential to perform. Most studies on fault detection in swarm robotics have been conducted exclusively in simulation, and they have focused on a single type of fault or a specific task. In a series of previous studies, we have developed a robust fault-detection approach in which robots in a swarm learn to distinguish between normal and faulty behaviors online. In this paper, we assess the performance of our fault-detection approach on a swarm of seven physical mobile robots. We experiment with three classic swarm robotics tasks and consider several types of faults in both sensors and actuators. Experimental results show that the robots are able to reliably detect the presence of hardware faults in one another even when the swarm behavior is changed during operation. This paper is thus an important step toward making robot swarms sufficiently reliable and dependable for real-world applications.
KW - Collective behavior
KW - fault detection
KW - multirobot systems
KW - robot swarms
UR - http://www.scopus.com/inward/record.url?scp=85076409384&partnerID=8YFLogxK
U2 - 10.1109/TRO.2019.2929015
DO - 10.1109/TRO.2019.2929015
M3 - Article
AN - SCOPUS:85076409384
SN - 1552-3098
VL - 35
SP - 1516
EP - 1522
JO - IEEE Transactions on Robotics
JF - IEEE Transactions on Robotics
IS - 6
M1 - 8787875
ER -