Camera-based Adaptive Line Formation and Dynamic Leader-Following Optimization (CALF-DLFO) for Drone Swarms in Real-time Updated Digital Twins

The swift advancement of drone technology presents new challenges in data analysis and integration as deployments increase. This paper addresses the complexities of managing multiple drone video feeds and controlling autonomous drone swarms to enhance situational awareness through the use of real-time updated digital twins for drone swarm command and control. We aim to enhance the effectiveness of drone swarm operations by introducing a novel method, Camera-based Adaptive Line Formation and Dynamic Leader-following Optimization (CALF-DLFO). We investigate the impact of this novel control method on area coverage and drone distribution, considering factors such as formation direction, speed, separation distance, and adjustments. Simulation experiments demonstrate a significant improvement in area coverage compared to prior methods which focused on isolated segments. Our camera-based adaptive line formation, combined with efficient drone control mechanisms, not only enhances coverage but also provides a promising approach to scalable and automated drone swarm management.

I. Introduction

Over the last decade, there has been a notable increase in the utilization of Unmanned Aerial Vehicles (UAVs), commonly known as drones [1]. This rise has been particularly significant across various industries, ranging from agriculture to filmmaking [2], [3]. The widespread adoption of drones is attributed to their versatility, cost-effectiveness, and increased availability and ease of access to drones. This growth has led to the emergence of drone swarms, which consist of multiple synchronized UAVs operating collectively to accomplish complex tasks such as environmental monitoring and search and rescue operations. The utilization of swarm control algorithms, e.g., inspired by [4], has become essential for coordinating the actions of individual drones within the swarm.