Logo Repositorio Institucional

Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/34340
Title: UAV-Based air pollutant source localization using combined metaheuristic and probabilistic methods
Authors: Minchala Avila, Luis Ismael
Youmin, Zhang
Yungaicela Naula, Noe Marcelo
Garza Castañón, Luis Eduardo
metadata.dc.ucuenca.correspondencia: Garza Castañón, Luis Eduardo, legarza@itesm.mx
Keywords: Bayesian
Air pollution
UAV
Source location
Metaheuristic
metadata.dc.ucuenca.areaconocimientofrascatiamplio: 2. Ingeniería y Tecnología
metadata.dc.ucuenca.areaconocimientofrascatidetallado: 2.2.1 Ingeniería Eléctrica y Electrónica
metadata.dc.ucuenca.areaconocimientofrascatiespecifico: 2.2 Ingenierias Eléctrica, Electrónica e Información
metadata.dc.ucuenca.areaconocimientounescoamplio: 07 - Ingeniería, Industria y Construcción
metadata.dc.ucuenca.areaconocimientounescodetallado: 0714 - Electrónica y Automatización
metadata.dc.ucuenca.areaconocimientounescoespecifico: 071 - Ingeniería y Profesiones Afines
Issue Date: 2019
metadata.dc.ucuenca.volumen: vol. 9
metadata.dc.source: Applied Sciences (Switzerland)
metadata.dc.identifier.doi: 10.3390/app9183712
metadata.dc.type: ARTÍCULO
Abstract: 
Air pollution is one of the greatest risks for the health of people. In recent years, platforms based on Unmanned Aerial Vehicles (UAVs) for the monitoring of pollution in the air have been studied to deal with this problem, due to several advantages, such as low-costs, security, multitask and ease of deployment. However, due to the limitations in the flying time of the UAVs, these platforms could perform monitoring tasks poorly if the mission is not executed with an adequate strategy and algorithm. Their application can be improved if the UAVs have the ability to perform autonomous monitoring of the areas with a high concentration of the pollutant, or even to locate the pollutant source. This work proposes an algorithm to locate an air pollutant’s source by using a UAV. The algorithm has two components: (i) a metaheuristic technique is used to trace the increasing gradient of the pollutant concentration, and (ii) a probabilistic component complements the method by concentrating the search in the most promising areas in the targeted environment. The metaheuristic technique has been selected from a simulation-based comparative analysis between some classical techniques. The probabilistic component uses the Bayesian methodology to build and update a probability map of the pollutant source location, with each new sensor information available, while the UAV navigates in the environment. The proposed solution was tested experimentally with a real quadrotor navigating in a virtual polluted environment. The results show the effectiveness and robustness of the algorithm.
Description: 
Air pollution is one of the greatest risks for the health of people. In recent years, platforms based on Unmanned Aerial Vehicles (UAVs) for the monitoring of pollution in the air have been studied to deal with this problem, due to several advantages, such as low-costs, security, multitask and ease of deployment. However, due to the limitations in the flying time of the UAVs, these platforms could perform monitoring tasks poorly if the mission is not executed with an adequate strategy and algorithm. Their application can be improved if the UAVs have the ability to perform autonomous monitoring of the areas with a high concentration of the pollutant, or even to locate the pollutant source. This work proposes an algorithm to locate an air pollutant's source by using a UAV. The algorithm has two components: (i) a metaheuristic technique is used to trace the increasing gradient of the pollutant concentration, and (ii) a probabilistic component complements the method by concentrating the search in the most promising areas in the targeted environment. The metaheuristic technique has been selected from a simulation-based comparative analysis between some classical techniques. The probabilistic component uses the Bayesian methodology to build and update a probability map of the pollutant source location, with each new sensor information available, while the UAV navigates in the environment. The proposed solution was tested experimentally with a real quadrotor navigating in a virtual polluted environment. The results show the effectiveness and robustness of the algorithm.
URI: http://dspace.ucuenca.edu.ec/handle/123456789/34340
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85072396469&origin=inward
metadata.dc.ucuenca.urifuente: https://www.mdpi.com/2076-3417/9/18
ISSN: 2076-3417
Appears in Collections:Artículos

Files in This Item:
File Description SizeFormat 
documento.pdfdocument13.93 MBAdobe PDFThumbnail
View/Open


This item is protected by original copyright



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Centro de Documentacion Regional "Juan Bautista Vázquez"

Biblioteca Campus Central Biblioteca Campus Salud Biblioteca Campus Yanuncay
Av. 12 de Abril y Calle Agustín Cueva, Telf: 4051000 Ext. 1311, 1312, 1313, 1314. Horario de atención: Lunes-Viernes: 07H00-21H00. Sábados: 08H00-12H00 Av. El Paraíso 3-52, detrás del Hospital Regional "Vicente Corral Moscoso", Telf: 4051000 Ext. 3144. Horario de atención: Lunes-Viernes: 07H00-19H00 Av. 12 de Octubre y Diego de Tapia, antiguo Colegio Orientalista, Telf: 4051000 Ext. 3535 2810706 Ext. 116. Horario de atención: Lunes-Viernes: 07H30-19H00