Lim, Seunghan (Agency for Defense Development), Song, Yeongho (UNIST), Choi, Joonwon (UNIST), Myung, Hyunsam (Agency for Defense Development), Lim, Heungsik (Agency for Defense Development), Oh, Hyondong (UNIST)

Decentralized Hybrid Flocking Guidance for a Swarm of Small UAVs

Scheduled for presentation during the Regular Session "Networked Swarms" (TuD26T2), Tuesday, November 26, 2019, 16:20−16:40, Room T2

2019 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS), November 25-27, 2019, Cranfield University, Cranfield, UK

This information is tentative and subject to change. Compiled on April 20, 2024

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.