How groups of cooperative foragers can achieve efficient and robust
collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality
trade-offs and swarm-size-dependent foraging strategies. Here we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly
simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments
conducted with more capable real ants that sense pheromone concentration and
follow its gradient. One key feature of our controllers is a control parameter which
balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for
distance and quality of resources, as well as overcrowding, and predicts a swarmsize-dependent strategy. We test swarms implementing our controllers against our
optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates
the sufficiency of simple individual agent rules to generate sophisticated collective
foraging behaviour