Algoritmo para la detección de formas aplicable a la estimación solar

J. M. Aguilar-López; R. A. García; E. F. Camacho

doi:10.4995/riai.2021.14765

Autores/as

J. M. Aguilar-López Universidad de Sevilla
R. A. García Universidad de Sevilla
E. F. Camacho Universidad de Sevilla

DOI:

https://doi.org/10.4995/riai.2021.14765

Palabras clave:

Estimación, Robots móviles, Algoritmo de dos capas

Resumen

En este artículo se presenta un algoritmo híbrido bio-inspirado para la detección de formas aplicado a la estimación solar en plantas solares. Se tiene como objetivo localizar y caracterizar la forma de una nube sobre una planta solar basándose en medidas de niveles bajos de la irradiancia con una pequeña flota de vehículos aéreos no tripulados (UAVs en inglés) equipados con sensores capaces de medir la irradiancia directa normal. El algoritmo híbrido propuesto se inspira y adapta las ideas del algoritmo de optimización de colonia de hormigas (ant colony optimization, ACO) y también usa un algoritmo estándar de cobertura de área, separándose el campo de la planta solar en dos mallados, uno para cada capa del algoritmo, para encontrar el área afectada por la nube. Cuando un UAV localiza la zona de baja irradiancia, los otros van a ayudarle. Dicho equipo delimita el borde de la nube usando conceptos de técnicas de procesamiento de imágenes. Finalmente, se prueba el algoritmo propuesto mediante simulaciones.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

J. M. Aguilar-López, Universidad de Sevilla

Departamento de lngeniería de Sistemas y Automática

R. A. García, Universidad de Sevilla

Departamento de lngeniería de Sistemas y Automática

E. F. Camacho, Universidad de Sevilla

Departamento de lngeniería de Sistemas y Automática

Citas

Aasen, H., Burkart, A., Bolten, A., Bareth, G., 2015. Generating 3d hyperspectral information with lightweight uav snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing 108, 245-259. https://doi.org/10.1016/j.isprsjprs.2015.08.002

Acar, E. U., Choset, H., 2000. Critica! point sensing in unknown environments. In: Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065). Vol. 4. IEEE, pp. 3803-3810.

Acar, E. U., Choset, H., Rizzi, A. A., Atkar, P. N., Hull, D., 2002. Morse decompositions for coverage tasks. The international journal of robotics research 21 (4), 331-344. https://doi.org/10.1177/027836402320556359

Anderson, B. D., Pidan, B., Yu, C., Walle, D., 2008. Uav formation control: Theory and application. In: Recent advances in learning and control. Springer, pp. 15-33. https://doi.org/10.1007/978-1-84800-155-8_2

Ashley, T., Carrizosa, E., Fernández-Cara, E., 2017. Optimisation of aiming strategies in solar power tower plants. Energy 137, 285-291. https://doi.org/10.1016/j.energy.2017.06.163

Avenar, G. S., Pereira, G. A., Pimenta, L. C., Iscold, P., 2015. Multi-uav routing for area coverage and remote sensing with mínimum time. Sensors 15 (11), 27783-27803. https://doi.org/10.3390/s151127783

Bar-Cohen, Y., 2006. Biomimetics?using nature to inspire human innovation. Bioinspiration & biomimetics 1 (1), Pl. https://doi.org/10.1088/1748-3182/1/1/P01

Camacho, E. F., Gallego, A., 2013. Optimal operation in solar trough plants: A case study. Solar Energy 95, 106-117. https://doi.org/10.1016/j.solener.2013.05.029

Camacho, E. F., Soria, M. B., Rubio, F. R., Martínez, D., 2012. Control of Solar Energy Systems. Springer Science & Business Media. https://doi.org/10.1007/978-0-85729-916-1

Cesetti, A., Frontoni, E., Mancini, A., Zingaretti, P., Longhi, S., 2010. A visionbased guidance system for uav navigation and safe landing using natural landmarks. Joumal of intelligent and robotic systems 57 (1-4), 233. https://doi.org/10.1007/s10846-009-9373-3

Choi, Y., Choi, Y., Briceno, S., Mavris, D. N., 2020. Energy-constrained multiuav coverage path planning for an aerial imagery mission using column generation. Journal oflntelligent & Robotic Systems 97 (1), 125-139. https://doi.org/10.1007/s10846-019-01010-4

Choset, H., Pignon, P., 1998. Coverage path planning: Toe boustrophedon cellular decomposition. In: Field and service robo tics. Springer, pp. 203-209. https://doi.org/10.1007/978-1-4471-1273-0_32

Coombes, M., Chen, W.-H., Liu, C., 2017. Boustrophedon coverage path planning for uav aerial surveys in wind. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 1563-1571. https://doi.org/10.1109/ICUAS.2017.7991469

Current, J. R., Schilling, D. A., 1989. Toe covering salesman problem. Transportation science 23 (3), 208-213. https://doi.org/10.1287/trsc.23.3.208

Daus, P. H., 1932. Toe march meeting of the southem california section. The American Mathematical Monthly 39 (7), 373-374. https://doi.org/10.1080/00029890.1932.11987331

Dil Technology lnc., 2015. Phantom 3 pro user manual. https: //dl.djicdn.com/downloads/phantom_3/en/Phantom_3_Professional_User_Manual __ V1. 6 .pdf, accessed: 2021-01-18.

Dorigo, M., 1991. Ant colony optimization?new optimization techniques in engineering. by Onwubolu, GC, and BV Babu, Springer-Verlag Berlín Heidelberg, 101-117.

Galceran, E., Carreras, M., 2013. A survey on coverage path planning for robotics. Robotics and Autonomous systems 61 (12), 1258-1276. https://doi.org/10.1016/j.robot.2013.09.004

García, R., Orihuela, L., Millán, P., Rubio, F., Ortega, M., 2020. Guaranteed estimation and distributed control of vehicle formations. International Joumal of Control, 1-24. https://doi.org/10.1080/00207179.2020.1714074

Jin, J., Tang, L., 2010. Optima! coverage path planning for arable farming on 2d surfaces. Transactions of the ASABE 53 (1), 283-295. https://doi.org/10.13031/2013.29488

Johnson, D. S., McGeoch, L. A., 1997. The traveling salesman problem: A case study in local optimization. Local search in combinatoria! optimization 1 (1), 215-310. https://doi.org/10.2307/j.ctv346t9c.13

Kuhn, P., Wilbert, S., Prahl, C., Schüler, D., Haase, T., Hirsch, T., Wittmann, M., Ramirez, L., Zarzalejo, L., Meyer, A., et al., 2017. Shadow camera system for the generation of solar irradiance maps. Solar Energy 157, 157-170. https://doi.org/10.1016/j.solener.2017.05.074

Law, E. W., Prasad, A. A., Kay, M., Taylor, R. A., 2014. Direct normal irradiance forecasting and its application to concentrated solar thermal output forecasting-a review. Solar Energy 108, 287-307. https://doi.org/10.1016/j.solener.2014.07.008

Nouri, B., Kuhn, P., Wilbert, S., Prahl, C., Pitz-Paal, R., Blanc, P., Schmidt, T., Yasser, Z., Santigosa, L. R., Heineman, D., 2018. Nowcasting of dni maps for the solar field based on voxel carving and individual 3d cloud objects from ali sky images. In: AIP Conference Proceedings. Vol. 2033. AIP Publishing LLC, p. 190011.

https://doi.org/10.1063/1.5067196

Ntawumenyikizaba, A., Viet, H. H., Chung, T., 2012. An online complete coverage algorithm for cleaning robots based on boustrophedon motions and a* search. In: 2012 8th Intemational Conference on Information Science and Digital Content Technology (ICIDT2012). Vol. 2. IEEE, pp. 401-405.

Oksanen, T., Visala, A., 2009. Coverage path planning algorithms for agricultural field machines. Journal offield robotics 26 (8), 651-668. https://doi.org/10.1002/rob.20300

Rokhmana, C. A., 2015. The potential of uav-based remate sensing for supporting precision agriculture in indonesia. Procedía Environmental Sciences 24 (2015), 245-253. https://doi.org/10.1016/j.proenv.2015.03.032

Sánchez, A., Gallego, A., Escaño, J., Camacho, E., 2018. Temperature homogenization of a solar trough field for performance improvement. Solar Energy 165, 1-9. https://doi.org/10.1016/j.solener.2018.03.001

Sánchez, A., Gallego, A., Escaño, J., Camacho, E. F., 2019. Thermal balance of large scale parabolic trough plants: A case study. Solar Energy 190, 69-81. https://doi.org/10.1016/j.solener.2019.08.001

Savant, S., 2014. A review on edge detection techniques for image segmentation. Intemational Joumal of Computer Science and Information Technologies 5 (4), 5898-5900.

Sheng, H., Chao, H., Coopmans, C., Han, J., McKee, M., Chen, Y., 2010. Lowcost uav-based thermal infrared remote sensing: Platform, calibration and applications. In: Proceedings of2010 IEEE/ASME Intemational Conference on Mechatronic and Embedded Systems and Applications. IEEE, pp. 38-43. https://doi.org/10.1109/MESA.2010.5552031

Silvagni, M., Tonoli, A., Zenerino, E., Chiaberge, M., 2017. Multipurpose uav for search and rescue operations in mountain avalanche events. Geomatics, Natural Hazards and Risk 8 (1), 18-33. https://doi.org/10.1080/19475705.2016.1238852

Sobel, l., Feldman, G., 1968. A 3x3 isotropic gradient operator for irnage processing. a talk at the Stanford Artificial Project in, 271-272. Technologies, S. M., 2019. Sun Sensor NANO-ISSX/c technical specifications. http://www. solar-mems. com/ smt_pdf /NANO_ Technical_Specif ications. pdf, accessed: 2020-08-18.

Xiong, C., Chen, D., Lu, D., Zeng, Z., Lian, L., 2019. Path planning ofmultiple autonomous marine vehicles for adaptive sampling using voronoi-based ant colony optimization. Robotics and Autonomous Systems 115, 90-103. https://doi.org/10.1016/j.robot.2019.02.002

Xu, L., Wang, Z., Yuan, G., Sun, F., Zhang, X., 2015. Thermal performance of parabolic trough solar collectors under the condition of dramatically varying dni. Energy Procedía 69, 218-225. https://doi.org/10.1016/j.egypro.2015.03.025

Yfantis, E., 2019. A uav with autonomy, pattem recognition for forest fire prevention, and ai for providing advice to firefighters fighting forest fires. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, pp. 0409--0413. https://doi.org/10.1109/CCWC.2019.8666471

Zhang, Z., Schwartz, S., Wagner, L., Miller, W., 2000. A greedy algorithm for aligning dna sequences. Joumal ofComputational biology 7 (1-2), 203-214. https://doi.org/10.1089/10665270050081478