A Bio-Inspired Transportation Network for Scalable Swarm Foraging

Scalability is a significant challenge for robot swarms. Generally, larger groups of cooperating robots produce more inter-robot collisions, and in swarm robot foraging, larger search arenas result in larger travel costs. This paper demonstrates a scale-invariant swarm foraging algorithm that ensures that each robot finds and delivers targets to a central collection zone at the same rate regardless of the size of the swarm or the search area. Dispersed mobile depots aggregate locally collected targets and transport them to a central place via a hierarchical branching transportation network. This approach is inspired by ubiquitous fractal branching networks such as tree branches and animal cardiovascular networks that deliver resources to cells and determine the scale and pace of life. We demonstrate that biological scaling laws predict how quickly robots forage in simulations of up to thousands of robots searching over thousands of square meters. We then use biological scaling to predict the capacity of depot robots that overcome scaling constraints to produce scale-invariant robot swarms. We verify the claims for large swarms in simulation and implement a simple depot design in hardware.