Teledetección para cultivos agrícolas basada en un cuadricóptero de bajo costo
DOI:
https://doi.org/10.18046/syt.v13i34.2092Palabras clave:
Cuadricóptero, teledetección, agricultura de precisión, AR Drone, control de altura, planificador de ruta.Resumen
Este artículo presenta una propuesta para recolectar información de cultivos agrícolas mediante un cuadricóptero de bajo costo, llamado AR Drone 2.0. Para lograr el objetivo se diseña un sistema de teledetección que enmarca desafíos identificados en la presente investigación, tales como, la adquisición de fotografías aéreas de todo un cultivo y la navegación del AR Drone en zonas no planas. El proyecto se encuentra en una fase temprana de desarrollo. La primera etapa indaga la plataforma y las herramientas hardware y software para construir el prototipo propuesto; la segunda, describe los experimentos de desempeño de los sensores de estabilidad y altura del AR Drone, con el fin de diseñar una estrategia de control de altura en cultivos no planos. Además, se evalúan algoritmos de planificación de ruta basados en la ruta más corta mediante grafos (Dijkstra, A* y propagación de frente de onda) usando un cuadricóptero simulado. La implementación de los algoritmos de la ruta más corta es el comienzo de la cobertura total de un cultivo. Las observaciones del comportamiento del cuadricóptero en el simulador Gazebo y las pruebas reales, demuestran la viabilidad de ejecutar el proyecto, usando el AR Drone como plataforma de un sistema de teledetección para agricultura de precisión.
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Tanveer, M. H., Hazry, D., Warsi, F. A., & Joyo, M. K. (2013). Stabilized controller design for attitude and altitude controlling of quad-rotor under diisturbance and noisy conditios. American Journal of Applied Sciences, 10(8), 819-831.
Turner, D., Lucieer, A., & Watson, C. (2011). Development of an Unmanned Aerial Vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery. En Proceedings of 34th International Symposium on Remote Sensing of Environment (pp.1-4). ISPRS. Retrieved from http://www.isprs.org/proceedings/2011/ISRSE-34/211104015Final00547.pdf
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ArduPilot autopilot suite (n.d.). APM Multiplataform Autopilot DRONECODE. (3DRobotics) Recuperado el 12 de Mayo de 2015, de http://ardupilot.com/
Arkin, R.C. (1990). Integrating behavioral, perceptual, and world knowledge in reactive navigation. Robotics and Autonomous Systems, 6(1), 105-122.
Bayar, V., Akar, B., Yayan, U., Yavuz, H. S., & Yazici, A. (2014). Fuzzy logic based design of classical behaviors for mobile robots in ROS middleware. In Innovations in Intelligent Systems and Applications (INISTA), Proceedings (pp.162-169). IEEE.
Bongiovanni, R., & Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5(4), 359-387.
Bristeau, P. J., Callou, F., Vissiere, D., & Petit, N. (2011, August). The navigation and control technology inside the ar. drone micro uav. In Preprints of the 18th IFAC World Congress Milano (Italy) August 28 - September 2, 2011, (Vol. 18, No. 1, pp.1477-1484).
Chee, K., & Zhong, Z. (2013). Control, navigation and collision avoidance for an unmanned aerial vehicle. Sensors and Actuators A: Physical, 66-76.
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische mathematik, 1(1), 269-271.
Galceran, E., & Carreras, M. (2013). A survey on coverage path planning for robotics. Robotics and Autonomous Systems, 61(12), 1258-1276.
Gómez-Candón, D., De Castro, A., & López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 5(1), 44-56.
Grenzdorffer, G., Engel, A., & Teichert, B. (2008). The photogrammetric potential of low-cost UAVs in forestry and agriculture. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 31(B3), 1207-1214.
Guclu, A., & Arikan, K. B. (2012). Attitude and altitude control of an outdoor quadrotor [doctoral dissertation]. Atilim University: Ankara, Turkey.
Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. Systems Science and Cybernetics, IEEE Transactions on, 4(2), 100-107.
Jannoura, R., Brinkmann, K., Uteau, D., Bruns, C., & Joergensen, R. G. (2015). Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter. Biosystems Engineering, 129, 341-351.
Jian-Guo, G., & Jun, Z. (2008). Altitude control system of autonomous airship based on fuzzy logic. In Systems and Control in Aerospace and Astronautics, 2008. (pp.1-5). IEEE.
Ji-hua, M., & Bing-fang, W. (2008). Study on the crop condition monitoring methods with remote sensing. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B8), 945-950.
Joseph, G. (2005). Fundamentals of remote sensing. Hyderabad, India: Universities Press.
Li, J., & Li, Y. (2011). Dynamic analysis and PID control for a quadrotor. Mechatronics and Automation (ICMA), 2011 International Conference on (pp. 573-578). IEEE.
Mehranpour, M. R., Emamgholi, O., Shahri, A., & Farrokhi, M. (2013). A new fuzzy adaptive control for a Quadrotor flying robot. En Fuzzy Systems (IFSC), 2013 13th Iranian Conference on (págs. 1-5). IEEE.
Meier, L., Camacho, J., Godbolt, B., Goppert, J., Heng, L., Lizarraga, M., ... & Tridgell, A. (2010). QGroundControl: Ground Control Station for Small Air-Land-Water Autonomous Unmanned Systems. Retrieved from: http://qgroundcontrol.org/
Pignon, P., & Choset, H. (1998). Coverage path planning: The boustrophedon cellular decomposition. In Field and Service Robotics (pp. 203-209). London, UK: Springer.
Primicerio, J., Di Gennaro, S. F., Fiorillo, E., Genesio, L., Lugato, E., Matese, A., & Vaccari, F. P. (2012). A flexible unmanned aerial vehicle for precision agriculture. Precision Agriculture, 13(4), 517-523.
Shengyi, Y., Kunqin, L., & Jiao, S. (2009). Design and simulation of the longitudinal autopilot of uav based on self-adaptive fuzzy pid control. In Computational Intelligence and Security, 2009. (pp. 634-638). IEEE.
Skiena, S. S. (1998). The algorithm design manual: Text. New york, NY: Springer Verlag.
Sugiura, R., Noguchi, N., & Ishii, K. (2005). Remote-sensing technology for vegetation monitoring using an unmanned helicopter. Biosystems Engineering, 90(4), 369-379.
Tailanian, M., Paternain, S., Rosa, R., & Canetti, R. (2014). Design and implementation of sensor data fusion for an autonomous quadrotor. In Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International (pp.1431-1436). IEEE.
Tanveer, M. H., Hazry, D., Warsi, F. A., & Joyo, M. K. (2013). Stabilized controller design for attitude and altitude controlling of quad-rotor under diisturbance and noisy conditios. American Journal of Applied Sciences, 10(8), 819-831.
Turner, D., Lucieer, A., & Watson, C. (2011). Development of an Unmanned Aerial Vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery. En Proceedings of 34th International Symposium on Remote Sensing of Environment (pp.1-4). ISPRS. Retrieved from http://www.isprs.org/proceedings/2011/ISRSE-34/211104015Final00547.pdf
Valente, J. (2011). An aerial robotic framework to address area coverage in precision agriculture practices [doctoral dissetation]. Universidad Politécnica de Madrid: Spain.
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2015-09-30
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Investigación científica y tecnológica
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Esta publicación está licenciada bajo los términos de la licencia CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.es)
