Robot Error Detection Using an Artificial Immune System
EH '03 Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Automatic Synthesis of Fault Detection Modules for Mobile Robots
AHS '07 Proceedings of the Second NASA/ESA Conference on Adaptive Hardware and Systems
Modelling the Tunability of Early T Cell Signalling Events
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Design of fault-tolerant computers
AFIPS '67 (Fall) Proceedings of the November 14-16, 1967, fall joint computer conference
T Cell Receptor Signalling Inspired Kernel Density Estimation and Anomaly Detection
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
A modified dendritic cell algorithm for on-line error detection in robotic systems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
The application of a dendritic cell algorithm to a robotic classifier
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Parameter optimisation in the receptor density algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Swarm robotics: from sources of inspiration to domains of application
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
The Receptor Density Algorithm
Theoretical Computer Science
Towards a robust feedback system for coordinating a hierarchical multi-robot system
Robotics and Autonomous Systems
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Swarm robotics is an example of a complex system with interactions among distributed autonomous robots as well with the environment. Within the swarm there is no centralised control, behaviour emerges from interactions between agents within the swarm. Agents within the swarm exhibit time varying behaviour in dynamic environments, and are subject to a variety of possible anomalies. The focus within our work is on specific faults in individual robots that can affect the global performance of the robotic swarm. We argue that classical approaches for achieving tolerance through implicit redundancy is insufficient in some cases and additional measures should be explored. Our contribution is to demonstrate that tolerance through explicit detection with statistical techniques works well and is suitable due to its lightweight computation.