DOI:

https://doi.org/10.14483/23448393.21035

Published:

2023-10-19

Issue:

Vol. 28 No. 3 (2023): September-December

Section:

Systems Engineering

Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications

Enfoque preliminar de una plataforma multi-sensor basada en UAV para aplicaciones de reconocimiento y vigilancia

Authors

Keywords:

Unmanned Aerial Vehicles (UAVs), multi-sensor reconnaissance and surveillance system (MRSS), geospatial intelligence (GEOINT), signals intelligence (SIGINT), measurement and signature intelligence (MASINT) (en).

Keywords:

vehículos aéreos no tripulados (UAV), istema de reconocimiento y vigilancia multisensor (MRSS), inteligencia geoespacial (GEOINT), inteligencia de señales (SIGINT), inteligencia de firmas (MASINT) (es).

Downloads

Abstract Authors Available Metrics References How to Cite

Abstract (en)

Context: Unmanned Aerial Vehicles (UAVs) equipped with remote sensing platforms have become increasingly popular due to their applications in aerial surveillance, environmental control, and disaster response. However, the limited flight range and on-board energy resources of UAVs pose significant challenges to their practical deployment and operating efficiency, which has led to the exploration of energy-efficient platforms for remote sensing.

Method: This paper proposes a preliminary approach for UAV multi-sensor reconnaissance and surveillance platforms (MRSS) that target low energy consumption. The approach implemented four sensor modules controlled by one multi-functional integrated edge computer for control and data collection, which can be interchanged according to battery lifetime requirements.

Results: The main contribution of this work was an analysis of the energy consumption behavior of sensor modules managed by an embedded system with edge computing capabilities as the central control unit.

Conclusions: The high energy consumption associated with modules such as GEOINT leads to deep discharge in excess of 20 % DOD, resulting in a maximum battery degradation of 2,4 years.

Abstract (es)

Contexto: Los vehículos aéreos no tripulados (UAV) equipados con plataformas de sensores remotos se han hecho cada vez más populares debido a sus aplicaciones en vigilancia aérea, control medioambiental y respuesta ante catástrofes. Sin embargo, la limitada autonomía de vuelo y los limitados recursos energéticos a bordo de los UAV plantean importantes retos para su despliegue práctico y su eficiencia operativa, lo que ha llevado a explorar plataformas energéticamente eficientes para la teledetección.

Métodos: En este artículo se propone un enfoque preliminar para plataformas de reconocimiento y vigilancia multisensor (MRSS) dirigidas a un bajo consumo energético. El enfoque implementa cuatro módulos de sensores controlados por un ordenador multifuncional de borde integrado para la recopilación de datos, que pueden intercambiarse en función de los requisitos de duración de la batería.

Resultados: La principal contribución de este trabajo fue un análisis del comportamiento del consumo de energía de los módulos de sensores gestionados por un sistema integrado con capacidad de computación frontera como unidad de control central.

Conclusiones: El elevado consumo de energía asociado a módulos como el GEOINT conduce a una descarga profunda superior a un DOD de 20 % que provoca una degradación máxima de 2,4 años de la batería.

Author Biographies

Nicolás Amézquita-Gómez, Sergio Arboleda University

He received a cum laude degree in Electronic Engineering in 2004 from Universidad Pedagógica y Tecnológica de Colombia. From 2004 to 2019, he worked as a researcher at different universities and technology companies in Spain and the United States as follows. From 2004 to 2008: researcher at the PhD(c) in Artificial Intelligence at Universitat Politècnica de Catalunya and researcher in Mobile Robotics and Intelligent Systems at the Institute of Robotics and Industrial Informatics (IRI)(CSIC-UPC). In 2009, he received a Master’s degree in Computer Security and Intelligent Systems in Sensory Systems Applied to Industry (SSAI). In 2009, he received an excellent cum laude PhD degree in Computer Science and Engineering. Both of these degrees were granted by Universitat Rovira i Virgili in Spain. From 2013 to 2019, he patented the following inventions: Producing video bits for space time video summary, Patent class: to facilitate tuning or selection of video signal (725/38), Patent number application: 20130081082, USA Vilynx Inc. Palo Alto, CA, USA; Patent class: target tracking or detecting (382/103), Patent number application: 10410679, US; Patent class: operator interface (725/37), Patent number application: 8869198, USA, purchased by Apple on October 27th, 2020, Palo Alto, CA, USA. His research interests are computer vision, artificial intelligence, parallel computing for embedded systems, autonomous and robotic systems, and digital signal processing. He is currently researching at the project titled Modular Multi-mission Monitoring System for Remotely Piloted Aircraft Systems (MMMS-RPAS) at the Electronic Engineering Department of Universidad Sergio Arboleda (Bogotá, Colombia).

Sergio Ramiro González-Bautista, Pedagogical and Technological University of Colombia

He is a PhD and a Master in Electronic Engineering from Pontificia Universidad Javeriana. Bachelor in Electronic Engineering from Universidad Pedagógica y Tecnológica de Colombia (UPTC). His research interests are focused on automatic maintenance management systems, cyber-physical architectures for Industry 4.0, and Automated Guided Vehicles (AGVs).

Marco Teran, Sergio Arboleda University

He received a BS degree in Electronic Engineering and a M.Sc. degree in Electronics from the Bauman Moscow State Technical University (Russia). He has experience in research at the "Progress"Scientific esearch Institute in the area of communications and satellite navigation. He served as an ATSEP engineer at Aeronáutica Civil (Colombia). Since 2014, he has been an associate professor of the School of Exact Sciences and Engineering of Universidad Sergio Arboleda (Bogotá, Colombia). His current research interests include indoor positioning, RADAR, and GNSS technologies, as well as Internet of Things (IoT) applications. He is currently pursuing a PhD at Pontificia Universidad Javeriana.

Camilo Salazar, Sergio Arboleda University

He received a BS degree in Electronic Engineering from Escuela Colombiana de Ingenieria Julio Garavito, a specialization in Telecommunications from Universidad Piloto de Colombia, and a Master’s degree in Telecommunications and ICT Regulation from Universidad Santo Tomás. Extensive knowledge in SDR (Software-Defined Radio) development and mobile communications.

John Corredor, Pontificia Universidad Javeriana

He is a PhD from Universidad Autónoma de Barcelona (Spain) with a cum laude degree. He has participated in projects and training courses in the field of Artificial Intelligence and Data Science, particularly in design and development with applications involving Machine Learning, Data Mining, Computer Vision, Performance Prediction in High Performance Computing (HPC), multi-core computer architectures, operating systems, hybrid systems for AI, communication theory, optimization, parallel architectures, robotics, teaching methodologies for engineering, programming paradigms, and high availability computing (HTC), among other topics. Extensive experience in research project management and social impact. He is currently a research professor at the School of Intelligence and Strategic Counterintelligence of the Ministry of Defense of Colombia, as well as at the Faculty of Engineering of Pontificia Universidad Javeriana (Bogotá, Colombia).

References

G. Keaton, "LiDAR surveying technology advantages,"2021. [Online]. Available:https://ecko360industrial.com/2021/06/28/lidar-surveying-technology-advantages/

H. Yao, R. Qin, and X. Chen, Ünmanned aerial vehicle for remote sensing applications — A review,"Remote Sens., vol. 11, no. 12, art. 1443, 2019. https://www.mdpi.com/2072-4292/11/12/1443

C. Yinka-Banjo and O. Ajayi, "Sky-farmers: Applications of unmanned aerial vehicles (UAV) in agriculture,ïn Autonomous Vehicles, G. Dekoulis, Ed., London, UK: IntechOpen, 2019, ch. 6. https://doi.org/10.5772/intechopen.89488

J. Cuaran and J. Leon, Çrop monitoring using unmanned aerial vehicles: A review". Agricultural Reviews, vol. 42, no. 2, pp. 121-132, 2021. https://doi.org/10.18805/ag.R-180

M. Pashine and P. Mundhada, Ïmplementation of real time monitoring in the field of agriculture,"Int. Res. J. Eng. Technol., vol. 8, no. 12, pp. 665-669, 2021. https://www.irjet.net/archives/V8/i12/IRJET-V8I12114.pdf

Z. Yang et al.,ÜAV remote sensing applications in marine monitoring: Knowledge visualization and review,"Sci. Total Environ., vol. 838, part 1, art. 155939, 2022. https://doi.org/10.1016/j. scitotenv.2022.155939

P. Maddikunta, S. Hakak, M. Alazab, S. Bhattacharya, T. Gadekallu, W. Khan, and Q. Pham, Ünmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges,"IEEE Sens. J.,vol. 21, no. 16, pp. 17608-17619, 2021. https://doi.org/10.1109/JSEN.2021.3049471

J. Pavón, J. Gómez-Sanz, A. Fernández-Caballero, and J. Valencia-Jiménez, "Development of intelligent multisensor surveillance systems with agents,"Robotics Auton. Syst., vol. 55, no. 12, pp. 892-903, 2007. https://doi.org/10.1016/j.robot.2007.07.009

H.Abdullahi, F. Mahieddine, and R. Sheriff, "Technology impact on agricultural productivity: A review of precision agriculture using unmanned aerial vehicles,ïn Int. Conf. Wireless Sat. Syst., 2015, pp. 388-400. https://doi.org/10.1007/978-3-319-25479-1_29

S. Liaghat and S. Balasundram, .A review: The role of remote sensing in precision agriculture,"American J. Agricul. Biolog. Sci., vol. 5, no. 1, pp. 50-55, 2010. https://thescipubcom/pdf/ajabssp.2010.50.55.pdf

N. Yin, R. Liu, B. Zeng, and N. Liu,. A review: UAV-based remote sensing,"IOP Conf. Ser. Mater. Sci. Eng., vol. 490, art. 062014, 2019. https://iopscience.iop.org/article/10.1088/1757-899X/490/6/062014/pdf

A. Opeyemi and N. David, An RGB sensor-based aerial robotic platform for sustainable precision agriculture,"2020. [Online]. Available>https://ic-sd.org/wp-content/uploads/2020/11/Abioye-Ayodeji.pdf

S. Tzafestas, Introduction To mobile robot control, Amsterdam, The Netherlands: Elsevier, 2014. https://doi.org/10.1016/B978-0-12-417049-0.00012-2

P. Velusamy, S. Rajendran, R. Mahendran, S. Naseer, M. Shafiq, and J. Choi, Ünmanned aerial vehicles (UAV) in precision agriculture: Applications and challenges,"Energies,vol. 15, no. 1, art. 217, 2022. https://doi.org/10.3390/en15010217

A. Feng, J. Zhou, E. Vories, K. Sudduth, and M. Zhang, "Yield estimation in cotton using UAV-based multi-sensor imagery", Biosyst. Eng., vol. 193, pp. 101-114, 2020. https://doi.org/10.1016/j. biosystemseng.2020.02.014

A. Matese and S. Di Gennaro, "Practical applications of a multisensor UAV platform based on multispectral, thermal, and RGB high-resolution images in precision viticulture,""Agriculture, vol. 8, no. 7, art. 116, 2018. https://doi.org/10.3390/agriculture8070116

S. Di Gennaro et al., "Multisensor approach to assess vineyard thermal dynamics combining high-resolution unmanned aerial vehicle (UAV) remote sensing and wireless sensor network (WSN) proximal sensing,"Scientia Horticulturae, vol. 221, pp. 83-87, 2017. https://doi.org/10.1016/j. scienta.2017.04.024

P. Katsigiannis, L. Misopolinos, V. Liakopoulos, T. Alexandridis, and G. Zalidis, .An autonomous multi-sensor UAV system for reduced-input precision agriculture applications,ïn 2016 24th Med. Conf. Control Automation (MED), 2016, pp. 60-64. https://doi.org/10.1109/MED.2016.7535938

M. Nagai, T. Chen, A. Ahmed, and R. Shibasaki, ÜAV borne mapping by multi sensor integra-tion,"2008. [Online]. Available: https://www.isprs.org/proceedings/xxxvii/congress/1_pdf/207.pdf

F. Liu, J. Shan, B. Xiong, and F. Zheng, . A real-time and multi-sensor-based landing area recognition system for UAVs,"Drones, vol. 6, no. 5, art. 118, 2022. https://doi.org/10.3390/drones6050118

M. Stein, S. Bargoti, and J. Underwood, Ïmage based mango fruit detection, localisation and yield estimation using multiple view geometry,"Sensors, vol. 16, no. 11, art. 1915, 2016. https://doi.org/10.3390/s16111915

M. Maimaitijiang et al., Ünmanned aerial system (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine,"ISPRS J. Photogramm. Remote Sens., vol. 134, pp. 43-58, 2017. https://doi.org/10.1016/j.isprsjprs.2017.10.011

G. Bai, Y. Ge, W. Hussain, P. Baenziger, and G. Graef, .A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding,"Comp. Electronics Agricul., vol. 128, pp. 181-192, 2016. https://doi.org/10.1016/j.compag.2016.08.021

A. Comar, P. Burger, B. Solan, B. Frederic, F. Daumard, and H. Jean-François, .A semi-automatic system for high throughput phenotyping wheat cultivars in-field conditions: Description and first results,"Funct. Plant Biol., vol. 39, no. 11, pp. 914-924, 2012. https://doi.org/10.1071/fp12065

P. Andrade-Sánchez et al., "Development and evaluation of a field-based high-throughput phenotyping platform,"Funct. Plant Biol., vol. 41, no. 1, pp. 68-79, 2014. https://doi.org/10. 1071/fp13126

E. Torres-Garzón, J. Guevara, and Y. Mendoza-Prieto, .Estudio sobre el desarrollo de un prototipo para monitoreo del espectro radioelectrónico utilizando SDR,"Revista Perspectivas En Inteligencia, vol. 14, no. 23, pp. 303-323, 2022. https://doi.org/10.47961/2145194X.344

M. Terán, J. Aranda, J. Marín, E. Uchamocha, and G. Corzo-Ussa, .A methodology for signals intelligence using non-conventional techniques and software-defined radio,"presented at the 2021 IEEE Col. Conf. Comm. Comp. (COLCOM), Cali, Colombia, May 26-28, 2021. https://doi.org/10. 1109/COLCOM52710.2021.9486297

B. Sharma and G. Ritchie, "High-throughput phenotyping of cotton in multiple irrigation environments,"Crop Sci., vol. 55, no. 2, pp. 958-969, 2015. https://doi.org/10.2135/cropsci2014.04.0310

R. Xu, C. Li, and S. Bernardes, "Development and testing of a UAV-based multi-sensor system for plant phenotyping and precision agriculture,"Remote Sens., vol. 13, no. 17, art. 3517, 2021. https://www.mdpi.com/2072-4292/13/17/3517

O. Montesinos-López et al., "Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data,"Plant Meth., vol. 13, art. 4, 2017. https://doi.org/10.1186/s13007-016-0154-2

S. Fei et al., ÜAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat,"Prec. Agricul., vol. 24, pp. 187-212, 2022. https://doi.org/10.1007/s11119-022-09938-8

M. Vlaminck, L. Diels, W. Philips, W. Maes, R. Heim, B. Wit, and H. Luong, .A multisensor UAV payload and processing pipeline for generating multispectral point clouds,"Remote Sens., vol. 15, no. 6, art. 1524, 2023. https://doi.org/10.3390/rs15061524

X. Li and A. Savkin, "Networked unmanned aerial vehicles for surveillance and monitoring: A survey,"Future Internet, vol. 13, no. 7, art. 174, 2017. https://doi.org/10.3390/fi13070174

A. Gupta, H. Gupta, P. Kumari, R. Mishra, S. Saraswat, and T. Dutta, .A real-time precision agriculture monitoring system using mobile sink in WSNs”, in 2018 IEEE Int. Conf. Adv. Net. Telecom. Syst. (ANTS), Indore, India, December 16-19, 2018. https://doi.org/10.1109/ANTS.2018.8710048

Y. Mendoza-Prieto, S. Sopo, and E. Torres-Garzón, .Evaluación de captadores fotovoltaicos para suministro complementario de energía en aeronaves remotamente tripuladas,"Revista Perspectivas en Inteligencia, vol. 12, no. 21, pp. 261-274, 2021. https://doi.org/10.47961/2145194X.226

J. Machado, "Software defined radio: Basic principles and applications,"Revista Facultad de Ingeniería, vol. 24, no. 38, pp. 79-96. http://www.scielo.org.co/pdf/rfing/v24n38/v24n38a07.pdf

K. Mallon, F. Assadian, and B. Fu, .Analysis of on-board photovoltaics for a battery electric bus and their impact on battery lifespan,"Energies, vol. 10, no. 7, art. 943, 2017. https://doi.org/10. 3390/en10070943

I. Acero et al., "Method for validating CubeSat satellite EPS through power budget analysis aligned With mission requirements,"IEEE Access, vol. 11, pp. 43316-43332, 2023. https://doi.org/10. 1109/ACCESS.2023.3271596

C. Wu, J. Fu, X. Huang, X. Xu, and J. Meng, "Lithium-ion battery health state prediction based on VMD and DBO-SVR,"Energies, vol. 16, no. 10, art. 3993, 2023. https://doi.org/10.3390/en16103993

How to Cite

APA

Amézquita-Gómez, N., González-Bautista, S. R., Teran, M., Salazar, C., Corredor, J., and Corzo, G. D. (2023). Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications. Ingeniería, 28(3), e21035. https://doi.org/10.14483/23448393.21035

ACM

[1]
Amézquita-Gómez, N. et al. 2023. Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications. Ingeniería. 28, 3 (Oct. 2023), e21035. DOI:https://doi.org/10.14483/23448393.21035.

ACS

(1)
Amézquita-Gómez, N.; González-Bautista, S. R.; Teran, M.; Salazar, C.; Corredor, J.; Corzo, G. D. Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications. Ing. 2023, 28, e21035.

ABNT

AMÉZQUITA-GÓMEZ, Nicolás; GONZÁLEZ-BAUTISTA, Sergio Ramiro; TERAN, Marco; SALAZAR, Camilo; CORREDOR, John; CORZO, Germán Darío. Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications. Ingeniería, [S. l.], v. 28, n. 3, p. e21035, 2023. DOI: 10.14483/23448393.21035. Disponível em: https://revistas.udistrital.edu.co/index.php/reving/article/view/21035. Acesso em: 28 nov. 2023.

Chicago

Amézquita-Gómez, Nicolás, Sergio Ramiro González-Bautista, Marco Teran, Camilo Salazar, John Corredor, and Germán Darío Corzo. 2023. “Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications”. Ingeniería 28 (3):e21035. https://doi.org/10.14483/23448393.21035.

Harvard

Amézquita-Gómez, N. (2023) “Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications”, Ingeniería, 28(3), p. e21035. doi: 10.14483/23448393.21035.

IEEE

[1]
N. Amézquita-Gómez, S. R. González-Bautista, M. Teran, C. Salazar, J. Corredor, and G. D. Corzo, “Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications”, Ing., vol. 28, no. 3, p. e21035, Oct. 2023.

MLA

Amézquita-Gómez, Nicolás, et al. “Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications”. Ingeniería, vol. 28, no. 3, Oct. 2023, p. e21035, doi:10.14483/23448393.21035.

Turabian

Amézquita-Gómez, Nicolás, Sergio Ramiro González-Bautista, Marco Teran, Camilo Salazar, John Corredor, and Germán Darío Corzo. “Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications”. Ingeniería 28, no. 3 (October 19, 2023): e21035. Accessed November 28, 2023. https://revistas.udistrital.edu.co/index.php/reving/article/view/21035.

Vancouver

1.
Amézquita-Gómez N, González-Bautista SR, Teran M, Salazar C, Corredor J, Corzo GD. Preliminary Approach for UAV-Based Multi-Sensor Platforms for Reconnaissance and Surveillance applications. Ing. [Internet]. 2023 Oct. 19 [cited 2023 Nov. 28];28(3):e21035. Available from: https://revistas.udistrital.edu.co/index.php/reving/article/view/21035

Download Citation

Visitas

35

Dimensions


PlumX


Downloads

Download data is not yet available.

License

Copyright (c) 2023 Nicolás Amézquita-Gómez, Sergio Ramiro González-Bautista, Marco Teran, Camilo Salazar, John Corredor, Germán Darío Corzo

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

From the edition of the V23N3 of year 2018 forward, the Creative Commons License "Attribution-Non-Commercial - No Derivative Works " is changed to the following:

Attribution - Non-Commercial - Share the same: this license allows others to distribute, remix, retouch, and create from your work in a non-commercial way, as long as they give you credit and license their new creations under the same conditions.