Biologically Based Control of a Fleet of Unmanned Aerial Vehicles Facing Multiple Threats

This paper addresses a set of multiagent, unmanned, aerial vehicles (UAVs) in a mission within a threat-prone environment. Each UAV is considered as a nonholonomic, nonlinear model moving in a two dimensional space. The system is composed of a fleet of UAVs, competing UAVs and a target. The approach proposed in this paper is based on a biological model representing collective behavior in predator-prey systems. The fleet of UAV's (prey) is moving cohesively towards a target and they can come under attack from competing UAV's (predators) which are also expected to avoid a certain area around the target, treating it as an obstacle that they have to avoid. Prey and predators are expected to obey certain social behaviors within their respective species that include coherent motion, separation to avoid collision, and alignment. The prey are also connected through an adaptable network. During attacks, this networked population of prey could be divided into several small groups that are still connected and naturally observe the same social behavior. To identify these groups and their members, the density-based algorithm DBSCAN is used. The aim of this work is to use this biologically inspired model along with a robust feedback linearization controller to achieve both target pursuance and effective evasion from two predators. Simulation results demonstrate the different aspects and features provided in the proposed approach.