Control and Coordination for Swarm of UAVs Under Multi-Predator Attack

Abstract

This paper presents the implementation of a cooperative prey hunting strategy for a swarm of unmanned aerial vehicles (UAVs) inspired by the collective behavior of fish and predators. The swarm of UAVs are modelled using diffusion and adaptation algorithms, which enable self-organization and create mobile adaptive networks. Nodes in the mobile adaptive networks interact with each other locally to solve issues of distributed processing and inference. The predator motion is modelled using a state transition model. The study provides insights into the dynamic network structures that arise during interactions between a swarm of fish and predators. The dynamic model of the swarm of fish and predators are modeled using the unicycle model on a coplanar surface. A Lyapunov-based Backstepping controller is designed to ensure that the UAVs track their trajectories accurately. From the simulation results, it can be observed that both the swarm UAVs and the predator successfully tracks the designed navigation path while foraging, evading, and attacking, mimicking the air combat scenario.