Distributed task architecture of UAV swarm based on potential field direction

Abstract

Unmanned Aerial Vehicle (UAV) swarm surveillance has many advantages: flexible deployment, no casualties, high swarm survival rate, and high cost-effectiveness. It has become a force that we cannot ignore on the battlefield. As the key technology to ensure the survival rate of UAV swarms and improve detection efficiency, mission planning technology is the basis for realizing the autonomous detection of UAV swarms in the future. This paper introduces the method of UAV distributed mission planning. The mainstream UAV planning methods are discussed. We focus on the improved artificial potential field (IAPF) approach. The modeling method of discrete rasterization of task space is adopted in complex scenes of multiple target types. Compared with the simulation results of hybrid artificial potential field and ant colony optimization (HAPF-ACO), the superiority of the proposed method in search performance is verified.