Decentralized hybrid flocking guidance for a swarm of small UAVs

Abstract

Flocking is defined as flying in groups without colliding into each other through data exchange where each UAV applies a decentralized algorithm. In this paper, hybrid flocking control is proposed by using three types of guidance methods: vector field, Cucker-Smale model, and potential field. Typically, hybrid flocking control using several methods can lead to generating conflicting commands and thus degrading the performance of the mission. To address this issue, the adaptive CuckerSmale model is proposed. Besides, we use social learning particle swarm optimization to determine the optimal weightings between guidance methods. It is verified through numerical simulations that the optimal weighting for missions such as loitering and target tracking results in effective flocking.