The ability of artificial immune systems to adapt to varying pathogens makes such systems a suitable choice for various robotic applications. Generally, immunity-based robotic applications map local instantaneous sensory information into either an antigen or a co-stimulatory signal, according to the choice of representation schema. Algorithms then use relevant immune functions to output either evolved antibodies or maturity of dendritic cells, in terms of actuation signals. It is observed that researchers do not try to replicate the biological immunity but select necessary immune functions instead, resulting in an ad-hoc manner these applications are reported. On the other hand, the paradigm shift in robotics research from reactive to probabilistic approaches is also not being reflected in these applications. Authors, therefore, present a detailed review of immuno-inspired robotic applications in an attempt to identify the possible areas to explore. Moreover, the literature has been categorized according to the underlying immuno-definitions. Implementation details have been critically reviewed in terms of corresponding mathematical expressions and their representation schema that include binary, real or hybrid approaches. Limitations of reported applications have also been identified in light of modern immunological interpretations including the danger theory. As a result of this study, authors suggest a renewed focus on innate immunity, action contextualization prior to B/T cell invocation and behavior evolution instead of arbitration. In this context, a multi-tier immunological framework for robotics research, combining innate and adaptive components together is also suggested and skeletonized. 
Immuno-inspired robotic applications: A review