VBCA: A Virtual Forces Clustering Algorithm for Autonomous Aerial Drone Systems

TL;DR

VBCA is a novel 3-D clustering algorithm for autonomous drone positioning that uses virtual forces inspired by molecular geometry, enabling scalable, self-organized, and efficient coverage with improved performance over existing methods.

Contribution

This paper introduces VBCA, a virtual forces-based clustering algorithm for autonomous drone systems, providing scalable, localized, and near-optimal 3-D coverage solutions.

Findings

VBCA achieves up to 40% better volume coverage than existing approaches.

The algorithm enables self-organization and topology reconfiguration in dynamic drone networks.

VBCA maintains network connectivity with a central drone during topology changes.

Abstract

We consider the positioning problem of aerial drone systems for efficient three-dimensional (3-D) coverage. Our solution draws from molecular geometry, where forces among electron pairs surrounding a central atom arrange their positions. In this paper, we propose a 3-D clustering algorithm for autonomous positioning (VBCA) of aerial drone networks based on virtual forces. These virtual forces induce interactions among drones and structure the system topology. The advantages of our approach are that (1) virtual forces enable drones to self-organize the positioning process and (2) VBCA can be implemented entirely localized. Extensive simulations show that our virtual forces clustering approach produces scalable 3-D topologies exhibiting near-optimal volume coverage. VBCA triggers efficient topology rearrangement for an altering number of nodes, while providing network connectivity to the central drone. We also draw a comparison of volume coverage achieved by VBCA against existing approaches and find VBCA up to 40\% more efficient.