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In this paper, we present the first stage of our research strategy to develop an immune-inspired solution for detecting anomalies in a foraging swarm robotic system with an immuno-engineering approach. Within immuno-engineering, the initial stage of our research involves the understanding of problem domain, namely anomaly detection, in a foraging swarm(More)
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(More)
— Previous research in supervised and unsupervised anomaly detection normally employ a static model of normal behaviour (normal-model) throughout the lifetime of the system. However, there are real world applications such as swarm robotics and wireless sensor networks where what is perceived as normal behaviour changes accordingly to the changes in the(More)
Error detection and recovery are important issues in swarm robotics research, as they are a means by which fault tolerance can be achieved. Our previous work has looked at error detection for single failures in a swarm robotics scenario with the Receptor Density Algorithm. Three modes of failure to the wheels of individual robots was investigated and(More)
This paper presents an initial investigation on studying an immune systems response, the granuloma formation, for inspirations on the development of energy sharing strategies for swarm robotic systems. Granuloma formation is a process in which unwanted substances are removed by immune systems. To better understand the components and the processes in(More)
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