Multi-UAV Flood Monitoring via CVT with Gaussian Mixture of Density Functions for Coverage Control

TL;DR

This paper introduces a novel UAV coverage control method using Gaussian Mixture models within a CVT framework to improve flood monitoring accuracy and coverage efficiency in unknown inundated regions.

Contribution

It proposes a new density modeling approach with GMDF for UAV coverage, enhancing flood area estimation over traditional Gaussian models.

Findings

GMDF-based method achieves higher coverage rates

Improved UAV spatial distribution in flood monitoring

Effective in unknown flood region estimation

Abstract

This study presents a control strategy for coordinating multiple unmanned aerial vehicles (UAVs) to monitor unknown flood regions and estimate the extent of inundation. The proposed method adopts a density-driven coverage framework based on Centroidal Voronoi Tessellation (CVT), in which the density function is modeled using a Gaussian Mixture of Density Functions (GMDF). This formulation provides a more accurate characterization of inundated areas compared to conventional axis-aligned Gaussian models. The performance of the two density modeling approaches is systematically evaluated under different UAV fleet sizes (16, 20, and 24), with multiple simulation trials conducted in the ROS/Gazebo environment. The results show that the GMDF-based formulation consistently achieves higher coverage rates, demonstrating its effectiveness in enhancing flood monitoring and improving UAV spatial distribution.