Formica ex Machina: Ant Swarm Foraging from Physical to Virtual and Back Again

Ants use individual memory and pheromone communication to achieve effective collective foraging. We implement these strategies as distributed search algorithms in robotic swarms. Swarms of simple robots are robust, scalable and capable of exploring for resources in unmapped environments. We test the ability of individual robots and teams of three robots to collect tags distributed at random and in clustered distributions. Teams of three robots that forage based on individual memory without communication collect RFID tags from all three distributions approximately twice as fast as a single robot using the same strategy. Adding pheromone-like communication in the teams of three robots improves foraging success. Our simulation system mimics the foraging behaviors of the robots and replicates our results, with slight improvements in the three robot teams. Simulated swarms of 30 and 100 robots collect tags 8 and 22 times faster than teams of three robots. This work demonstrates the feasibility of programming large robotic swarms for collective tasks such as retrieval of dispersed resources, mapping and environmental monitoring. It also lays a foundation for evolving collective search algorithms in silico and then implementing those algorithms in machina in robust and scalable robotic swarms.