Swarm robotic systems are heavily inspired by observations of social insects. This often leads to robustness being viewed as an inherent property of them. However, this has been shown to not always be the case. Because of this, fault detection and diagnosis in swarm robotic systems is of the utmost importance for ensuring the continued operation and success of the swarm. This paper provides an overview of recent work in the field of exogenous fault detection and diagnosis in swarm robotics, focusing on the four areas where research is concentrated: immune system, data modelling, and blockchain-based fault detection methods and local-sensing based fault diagnosis methods. Each of these areas have significant advantages and disadvantages which are explored in detail. Though the work presented here represents a significant advancement in the field, there are still large areas that require further research. Specifically, further research is required in testing these methods on real robotic swarms, fault diagnosis methods, and integrating fault detection, diagnosis and recovery methods in order to create robust swarms that can be used for non-trivial tasks.
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