Collective Cognition and Sensing in Robotic Swarms via an Emergent Group-Mind

Algorithms for robotic swarms often involve programming each robot with simple rules that cause complex group behavior to emerge out of many individual interactions. We study an algorithm with emergent behavior that transforms a robotic swarm into a single unified computational meta-entity that can be programmed at runtime. In particular, a swarm-spanning artificial neural network emerges as wireless neural links between robots self-organize. The resulting artificial group-mind is trained to differentiate between spatially heterogeneous light patterns it observes by using the swarm's distributed light sensors like cells in a retina. It then orchestrates different coordinated heterogeneous swarm responses depending on which pattern it observes. Experiments on real robot swarms containing up to 316 robots demonstrate that this enables collective decision making based on distributed sensor data, and facilitates human-swarm interaction.