Areas Division and Multiple UAV Coverage Path Planning For Gas Distribution Map

Abstract

Scientific researchers working on multi-UAVs agree that they provide a lot of advantages over using just one. Indeed, a multi-UAV system can handle more complex operations on larger surfaces with better efficiency. Collecting information and sharing it with each other still makes it possible to tackle new missions. However, the most delicate task is to ensure optimal and efficient planning. In this work, a fleet of drones (quadcopters) is deployed in a polluted area to ensure spatial sampling of this region of interest (ROI). The purpose is to gather data and rebuild the Gas Distribution Map (GDM) and/or how the pollution assessment is dispersed. First, after an adequate environmental simulation, the division of the ROI between UAVs is addressed. Then, according to the desired sampling resolution, a grid of measurement points is established in each resulting sub-region. After that, the aerial coverage mission is modeled as a Traveling Salesman Problem (TSP) and resolved by adapting Genetic Algorithms (GA). The last step consists of data collection and the GDM reconstruction. Through several simulation scenarios, the proposed techniques show that they can offer effective solutions for several coverage applications, especially for GDM.