Aggregation-mediated collective perception and action in a group of miniature robots

Abstract

We introduce a novel case study in which a group of miniaturized robots screen an environment for undesirable agents, and destroy them. Because miniaturized robots are usually endowed with reactive controllers and minimalist sensing and actuation capabilities, they must collaborate in order to achieve their task efficiently. In this paper, we show how aggregation can mediate both collective perception and action while maintaining the scalability of the algorithm. First, we demonstrate the feasibility of our approach by implementing it on a real group of Alice mobile robots, which are only two centimeters in size. Then, we use a combination of both realistic simulations and macroscopic models in order to find optimal parameters that maximize the number of undesirable cells destroyed while minimizing the impact on the healthy population. Finally, we discuss the limitations of these models, both in terms of accuracy, computational cost, and scalability, and we outline the importance of an appropriate multi-level modeling methodology to ensure the relevance and the faithfulness of such models.