Perceptron-Learning For Scalable And Transparent Dynamic Formation In Swarm-On-Swarm Shepherding

Swarm guidance, such as the case of guiding a group of sheep away from a field, is a challenging task. As the swarm size increases, it becomes necessary that multiple control points, or sheepdogs, are needed to guide the swarm. In this paper, a swarm of unmanned aerial vehicles (UAVs) acts as a moving safety network (aka a formation) that not only guides the sheep swarm, but also prevents them from dispersing or reversing to the other side of the field. We investigate two types of formations. The first type acts as a baseline, maintains fixed distances from the sheep swarm, and relies on fixed predefined angular structure relative to the sheep's global centre of mass (GCM). The second type is dynamic, where the force vector to control the UAV and the individual distance of each UAV from the sheep's GCM are controlled by a Perceptron, with the weights optimized by a particle swarm optimization algorithm. We evolve five Perceptrons to specialize in relative positions in the formation, which fixes the space cost for the optimization algorithm, while allowing the size of the swarm of UAVs to scale up. We demonstrate that the use of Perceptron-networks for dynamic control scheme reduces the total distance travelled by the UAVs, is transparent when interpreted with Hinton diagrams, and transferable to a larger number of UAVs.