Immune-inspired search strategies for robot swarms

Detection of targets distributed randomly in space is a task common to both robotic and biological systems. Levy search has previously been used to characterize T cell search in the immune system. We use a robot swarm to evaluate the effectiveness of a Levy search strategy and map the relationship between search parameters and target configurations. We show that the fractal dimension of the Levy search which optimizes search efficiency depends strongly on the distribution of targets but only weakly on the number of agents involved in search. Levy search can therefore be tuned to the target configuration while also being scalable. Implementing search behaviors observed in T cells in a robot swarm provides an effective, adaptable, and scalable swarm robotic search strategy. Additionally, the adaptability and scalability of Levy search may explain why Levy-like movement has been observed in T cells in multiple immunological contexts.