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Implementación de un = Dron Anticolisiones Capaz de Seguir una Trayectoria Preestablecida para la Elaboración de un Mapa de la Tasa de Ocupación Vegetal en una Zona

 

 

Implementation Of An Anti-Collision Drone Capable = Of Following A Pre-Established Trajectory For The Elaboration Of A Map Of The Plant Occupation Rate In An Area

 

Fabricio Javier Santacruz Sulca 

Recibido: 10-06-2019 / Revisado: 15-07-2019 /Aceptado: 15-08-2019/ Publicado: 06-09-2= 019

 

Abstract.                                  DOI: https://doi.org/10.33262/cie= nciadigital.v3i3.3.827

 =

An unmanned fl= ight system was implemented, which is able to detect obstacles and avoid collisi= ons. The drone is capable of taking images for further processing, it shows the percentage and size of vegetation in through the plots into a GUI in Matlab software..

Keywords: Drone, Anti-collisions, Precision Farming. 

&nb= sp;

Resumen.

Se implemento= un sistema de vuelo no tripulado, el cual es capaz de detectar obstáculos y ev= itar colisiones. El drone es capaz de tomar imágenes= para su posterior procesamiento con lo cual se logra obtener el porcentaje y tam= año de ocupación vegetal en las parcelas, mostradas gráficamente en una GUI en = el programa de Matlab

Palabras claves: Dron, Anti-colisiones, Agricultura de precisión.

Introducción.

En las últimas dos décadas la agricultura de preci= sión ha ido mejorando sus métodos del tratamiento de la información en los culti= vos y con la incorporación de drones para la recolección de información = (Murugan, Garg, Ahmed, & Singh, 2018), se han obten= ido resultados aceptables con los cuales se puede estimar las condiciones en las que los terrenos se encuentran. Las imágenes aeras de los cultivos nos permiten distinguir entre un suelo cultivado y no cultivad= o, pero aun no se puede clasificar los diferentes tipos de labranza. El uso de drones para las técnicas de visión computarizada nos permite monitorizar las parcelas sin importar su dimensión y de ese modo tomar los correctivos necesarios (Tripicchio, Satler, Dabisias, Ruffaldi, & Avizzano, 2015).

Muchos drones se desplazan por trayectorias predeterminadas con la ayuda de sensores colocados en los puntos a los cual= es deberá llegar y mientras ejecuta la misión, debe ir adquiriendo imágenes en= el trayecto (Moribe, Okada, Kobayashl, & Katayama, 2018). Al usar esta técnica se debe considerar la reducción del consumo de batería para prolong= ar la vida útil de los nodos de sensores. La inclusión de los drones en este c= ampo ha tenido una buena aceptación en países de primer mundo, no obstante, su a= cogida en el resto de la población no ha sido la esperada. Estos problemas se dan debido a la falta de conocimiento del potencial que se puede llegar a tener= con estos equipos para mejorar el tratamiento de los cultivos (Yallappa, Veerangouda, Maski, Palled, & Bheemanna, 2017).

El ambiente en el que operan los drones es mayorme= nte conocido y procurando que se encuentre alejado de obstáculos (Devos, Ebeid, & Manoonpong, 2018), sin embargo,= un desafío importante para el drone es evadir obst= áculos en entornos complejos. Para estos fines los dornes son controlados por un piloto a través de un FPV o vuelan de forma autónoma mediante auto-posición utilizando un GPS, pero debido a limitaciones del GP= S en aéreas cerradas se puede estimar la auto ubicación mediante el uso de senso= res o el procesamiento de imágenes (Morita, Kinjo, & Sato, 2018). Los drones se están volviendo ampliamente disponibles, participando en tareas que implica= n un acceso difícil para los seres humanos. En términos de navegación, los drones son mas versátiles que los vehículos terrestres (Wang, Yadav, & Balakrishnan, 2007), ya que pueden despegar y aterrizar verticalmente además de mantenerse suspendido en el ai= re y detener su movimiento bruscamente.

La segmentación de imágenes es encontrar grupos de píxeles que” van juntos” (Jain, Ray, & Bhavsar, 2015), entonces se = ha propuesto la intervención de un operador humano en la segmentación de imáge= nes de modo que sea interactiva. Los filtros de imágenes son de importancia en = el momento de reconocer determinados pixeles para su reconstrucción y manipula= ción (Jian, Bin, Hua, Lei, & Li, 2013). En el procesamiento de imágenes un desafío que se presenta es lidiar con las variaciones de intensidad originadas por la iluminación entre ellas = (Rivaz, Karimaghaloo, & Collins, 2014), por lo tanto= , es necesario estimar un margen de tolerancia para poder estimar la similitud de los pixeles en cada una de las imágenes procesadas.

Metodología.

La implementación del drone anticolisiones capaz de seguir una trayectoria para obtener imágenes de una zona cumple con los objetivos planteados en el proyecto de investigación, es por ello por lo que, para su desarrollo se usa los métodos de observación y experimentación, es decir estrategias basadas en la experiencia, la practic= a y el análisis de los hechos, con el fin de llegar a la solución eficaz del problema planteado.

 

El problema se divide en varias etapas= muy bien estructuradas, es decir pequeños problemas mas sencillos, esto con la finalidad de lograr la completa resolución del problema principal de manera optima y teniendo en cuenta que cada etapa lleva su tiempo de construcción.=

 

Elección del kit de desarrollo

Para elegir el kit adecuado se tomaron= en consideración las carteristas que cada uno de los dos kits mas accesibles poseen y finalmente optando por el kit de Intel Aero RTF.  En la Figura 1 se muestra el ensamble f= inal del drone junto con el sistema de prevención de colisión.

 

Tabla 1. Características de los elementos que poseen los kits SLAMdrunk e Intel= Aero

=  

Parrot SLAMdunk= =

Intel Aero Ready to Fly=

Procesador

NVIDIA= Tegra K1

Intel = Atom x7-z8750

RAM / Memoria

2 GB DDR3 / 16 GB

4 GB DDR3 / 32 GB

Sensores

IMU, Magnetómetro, barómetro, ultrasónico

IMU, Magnetómetro, Barómetro

Cámaras

Resolución 1500 x1500 a 60 FPS=

RGB 8MP y VGA monocromático, RealSense R200=

Controlador de vuelo=

 --------------------------------<= /o:p>

Contro= lador Intel Aero, SMT 32

Sistema Operativo

Ubuntu, ROS

Linux Yocto, soporte para Ubuntu/ ROS=

Aplicación

Sistem= a para navegación Autónoma, evasión de obstáculos y mapeo 3D, aplicable a drones= y otra clase de robots.

Desarr= ollo y prueba de aplicaciones para drones, soporte de tecnología Intel RealSense para interacción con el usuario, detección de objetos y mapeo 3D

 

Tabla 2. Comparación entre SLAMdrunk e Intel Aero

 

Parrot SLAMdunk= =

Intel Aero RTF

Mayor capacidad de procesamiento

NO

SI

Soport= e para varios sensores

SI

SI

Mayor soporte para cámaras

NO

SI

Sistema Operativo de código abierto

SI

SI

Contro= lador de vuelo incluido

NO

SI

Versatilid= ad para desarrollar sistemas de navegación autónoma

SI

NO

Partes y componentes necesarios para montaje de un UAV

NO

SI

 

Figura 1. Ensamblaje final del drone

 

 

Elaborado por:= Grupo de Investigación.

 

Procesamiento de Imágenes

 

Para la adquisición de las imágenes de= las misiones realizadas y su debido procesamiento, se utilizó la cámara Xioma YI Lite debido a su precio y Angulo de captura = de 35mm el cual nos permite obtener imágenes de 120 grados.<= /p>

 

Algoritmo del reconocimiento panorámic= o

Entrada: n imágenes desordenadas.=

  1. Extraer l= as características SIFT de todas las n imágenes.
  2. Encontrar= k vecinos mas cercanos para cada característica usando un árbol k-d=
  3. Para cada imagen:

= a)      Seleccionar m imágenes de coincidencia de candidatos.

= b)      Encontrar coincidencias de características geométricas utilizando RANSAC para resolve= r la homografía entre pares de imágenes.

= c)      Verificar que las imágenes coincidan con el modelo probabilístico.<= /p>

  1. Buscar componentes conectados de coincidencias de imágenes.=
  2. Para cada componente conectado:

= a)      Realizar el ajuste del paquete para resolver la rotación y la distancia focal.<= /o:p>

= b)      Renderizar panorámicas usando cultivadas.

Salida: imagen panorámica.<= /span>

 

Con la ayuda de Matlab se procede a implementar el algoritmo y con la ayuda de la librería VLFEAT se pudo conse= guir resultados favorables debido a que posee librerías como: FISHER, VECTOR, VL= AD, SIFT, MSER, entre otras:

 

Componentes Principales En El Reconocimiento De Imágenes

 

Los procesos principales son el algori= tmo de Stitching y el algoritmo de Segmentación, enton= ces se debe proceder de la siguiente forma:

 

Con los valores de cada pixel se puede determinar el porcentaje y tamaño aproximado de cada imagen panorámica únicamente se debe tomar en cuenta todos los aspectos que se presentan en l= as ecuaciones (1) y (2).

 

+

 

 

Figura 2. Pruebas de similitud en las imágenes.

 

 

Elaborado por:= Grupo de Investigación.

 

 

 

Donde:

 

- E: Escala de la imagen.

- H: Altura desniv= el del terreno.

- hv: Altura de vuelo del dron sobre el terreno.

- f: Distancia foc= al de la cámara.

- n: Numero de pix= eles segmentados.

- r: Resolución de= la imagen en pixeles por pulgada.

- fc: Factor de conversión de pulgada a m.

- vegetación: Área de ocupación vegetal.

Para un mejor análisis de resultados e interpretación de este valor se procedió a dividir a toda la zona en sector= es mas pequeños como se muestra en la figura 3, que nos permitirán conocer de mejor manera en que parte se encuentra la mayor y menor concentración de vegetación.

 

Sistema Anticolisiones Basado En Un Se= nsor Ultrasónico

Para realizar una misión con las debid= as precauciones es necesario contar con un sistema que permita detectar obstáculos, evaluarlos y evitarlos para poder proteger el drone ante posibles amenazas en espacios cerrados. Al utilizar un sensor ultrasón= ico situado en la parte frontal del fuselaje del drone y con una distancia de detecciondeobjetosenalmenos4metros, se pudo hacer una intervención en la señal del transmisor del dispositivo, desactivando los motores y generando un aterrizaje de emergencia. La acción de parar progresivamente las revoluciones en los motores se efectuó alterando el val= or del potenciómetro que controla el movimiento vertical del drone, la señal a cargo de permitir que la tarea se lleve a cabo fue enviada y recibida por antenas NFR14l01 junto con arduinos nano, que controlan la comunicación entre las antenas.

 

Figura 3. Interfaz del programa de reconstrucción, subdivisión y determinación de la población vegetal

 

Elaborado por:= Grupo de Investigación.

Figura 4. D.F. del transmisor del sistema de paro de emergencia

Elaborado por:= Grupo de Investigación.

Las figuras (3) y (4) muestran el principio de funcionamiento del transmisor y receptor del sistema anticolisiones

Figura 5. Receptor del sistema de paro= de emergencia

Elaborado por:= Grupo de Investigación.

Planeación de la misión mediante una estación de control en tierra.

Para realizar la planificación de la misión se procedió a utilizar el programa QGround Control el cual tiene soporte para PX4. Se debe establecer una conexión con= el drone con la finalidad de poder monitorear de manera = remota el estado actual del drone en vuelo. Planear la misión en este software se basa en determinar los puntos que debe recorrer = el drone con la ayuda conjunta de un GPS integrado en el mismo.

Ventana de planeación de misión:<= /o:p>

Main Toolbar: Ubicada en la parte superior de la pantalla = de la figura 6. Muestra los datos principales de la misión, como la distancia a recorrer, tiempo de la misión, altitud del drone, entre otros.

Figura 6. Ventana de herramientas de configuración de una misión.

Elaborado por:= Grupo de Investigación.

Plan Tools: Son las herramientas que se usan para crear la trayectoria de la misión, definiendo los puntos de recorrido ”Waypoints” = y el punto de finalización de la ruta.

Command Editor: Ubicado en la parte derecha de la ventana. En esta herramienta se puede modificar los parámetros de vuelo que va a recorrer el= drone en cada uno de los puntos definidos en la herra= mienta de planeación, como es la velocidad, altura y tiempo de espera.<= /span>

 

Una vez definidos los puntos de trayectoria y procurando un desplazamiento en línea recta junto con los parámetros de vuelo como velocidad de desplazamiento y altura, se procede a cargar la misión en el drone. Para recorrer la = misión establecida, el transmisor debe tener un canal configurado en modo ”Mission” para que de tal modo se pueda mandar a ejecu= tar la misma.

Resultados

= Análisis de la reconstrucción de imágenes.

= Para la captura de las imágenes se utiliza la cámara deportiva Xiaomi, situado 90 grados con respecto al fuselaje del dron con un time l= apse de 3s, logrando obtener imágenes con un solapamiento aproximado de 60% entre imágenes. Para este ejemplo se capturo una secuencia de 5 imágenes, las cua= les son almacenadas en la memoria microSD de la cámara para posteriormente cargarlas a la computadora de manera manual una vez terminada la misión de vuelo.

= Figura 7. Secuencia de imágenes obtenidas en la misión.

=

Elaborado por:= Grupo de Investigación.

= Cabe mencionar que el programa soporta cambios en parámetros de rotación, escala= y el orden de las imágenes. En la prueba realizada se estima la robustez del algoritmo, procediendo a cambiar el orden de la secuencia inicial de imágen= es, obteniendo un resultado similar al original, debido a que el algoritmo trat= a de buscar la imagen central dentro de la secuencia y en base a esta, se proced= e a la unión con las demás imágenes. También se procedió a variar el tamaño y la orientación de una de las imágenes, se utilizaron imágenes de 3968x2976 pix= eles y una imagen de 1786x1340 pixeles, con la cual probamos que el tamaño y la orientación de la imagen no afecta al resultado final.

= Figura 8. Imagen reconstruida.

Elaborado por:= Grupo de Investigación.

= En la tabla 3 se realizo una comparación entre las diferentes pruebas realizad= as, obteniendo los siguientes resultados.

Tabla 3. Comparación de resultados de la reconstrucción de imágenes, variando parámetros.

CARPETA=

(MISION)=

NÚMERO

IMAGENES=

PARAMETRO VARIADO=

TIEMPO

DE SALIDA=

(SEGUNDOS)=

RESOLUCION DE SALIDA=

(PIXELES)=

RESULTADO=

1

4

NINGUNO

00:09.69

2897 x 1508

Aceptable

7

4

DESORDEN

00:10.94

2578 x 1582

Aceptable

8

4

TAMAÑO

00:08.60

2964 x 1585

Aceptable

9

4

ORIENTACION

00:09.29

2567 x 1459

Aceptable

= Sistema anticolisiones.=          

= De acuerdo con las pruebas establecidas con el drone se ha establecido el rango máximo en la detección de objetos en el sensor ultrasónico con una distancia de detección aproximada de 4m debido a la velocidad con la que los motores disminuyen sus revoluciones cuando se llev= a a efecto el bloqueo del acelerador en el transmisor del = drone se puede definir la distancia de separación con el obstáculo en la que el d= ron efectuó el aterrizaje. Se puede estimar la distancia de descenso que descri= be el dron pudiendo así conocer la altura máxima en la que el sistema será funcional, como se muestra en la figura 9.

Figura 9. Valores de separación del drone al momento de g= enerar el paro de emergencia.

Elaborado por:= Grupo de Investigación.

= Con una calibración de descenso aproximada de 30 grados se pudo definir el valo= r de la pendiente y las distancias a las cuales el drone se encontrará al final del proceso. Por lo tanto, las estimaciones de las distancias se muestran en la tabla 4.

Tabla 4. Valores tolerables en los cuales el sistema basado en ultrasónico será eficiente

Distancia de detección (metros)

Altura del drone Y= (metros)

Distancia X

Distancia de descenso recorrida Z (metros)

Distancia de separaci= ón d (metros)

Aceptación con relaci= ón a la altura

4

5

4,33=

6,61=

-0,33

NULA=

4

4

3,46=

5,29=

0,54=

BAJA=

4

3

2,60=

3,97=

1,40=

ALTA=

4

2

1,73=

2,65=

2,27=

ALTA=

4

1

0,87=

1,32=

3,13=

ALTA=

=  

= En el momento de realizar las pruebas para la comparación de valores estimados= vs. valores reales, se obtuvieron los siguientes resultados mostrados en la tab= la 5.

= Tabla 5. Valores estimados y reales de distancias de separación en el sistema anticolisiones.

=  

 =

Distancia de separacion entre el obstaculo

 

Altura      (metros)

Posición calculada (metr= os)

Posición real (metros)

Tiempo de aterrizaje aproximado(segundos)

Velocidad de descenso (metros/segundo)

3.5

0.98

1.10

6=

0.81

3=

1.27

1.35

6=

0.70

2.5

1.55

1.62

5=

0.67

2=

1.85

1.95

4=

0.62

1.5

2.13

2.20

3=

0.71

1=

2.43

2.50

2=

0.60

0.5

2.71

2.79

1=

0.67

=  

= Planeación de la misión.

= El tiempo máximo de vuelo es de 10 minutos en promedio, utilizando una batería= de 4s, 3000 mAh, y una tasa de descarga de 30-40C. Este tiempo depende de fact= ores como la velocidad de desplazamiento, la ruta a seguir y velocidad del vient= o, entre otras condiciones ambientales. Esto nos da como resultado un tiempo aproximado de 8 minutos de vuelo. La velocidad de vuelo es de 2 m/s, la cap= tura de imágenes se lo realizo mediante la función de temporización en un lapso = de 3 segundos, para tener un solapamiento mayor al 70%. La altura de vuelo se determina en función del ancho del terreno, para que el ancho del terreno s= ea cubierto por una sola imagen. La finalidad es definir la trayectoria mas sencilla para el dron, es decir una línea recta obteniendo un menor numero = de imágenes para procesar. Una vez configurada la ruta como se muestra en la figura 10, se procede a cargarla en el dron y a ejecutarla para la captura = de las imágenes.

= Figura 10. Definición de la trayectoria a seguir.

Elaborado por:= Grupo de Investigación.

= En la figura 11 se muestran los valores de distancia estimados por el GPS en <= span class=3DSpellE>Qground Control y los valores Reales medidos de manera manual.

= Figura 11. Valores reales y estimados en las trayectorias.

Elaborado por: Grupo de Investigaci= ón.

=  

Conclusiones

  • Se ha investigado los principales conceptos sobre l= os vehículos aéreos no tripulados, logrando determinar que un vehículo ti= po Quadcopter es la mejor opción para desarrollar el proyecto, debido a q= ue podemos recorrer el terreno a menores velocidades logrando capturar imágenes mas estables y sin desenfoque, además, si se llega a presenta= r un obstáculo, el vehículo es capaz de efectuar un aterrizaje de emergenci= a.
  • Se logro establecer una comunicación mediante una r= ed wifi entre el dron y una computa- dora en tierra, usando el protocolo = de comunicación MAVLink y el programa QGround Control, consiguiendo preestablecer la r= uta que sigue el dron para capturar las imágenes y realizar un monitoreo y control de los parámetros de vuelo.
  • Se implemento un sistema anticolisiones con la ayud= a de un sensor ultrasónico y los módulos NRF24l01 junto con Arduino, al env= iar y recibir datos continuamente sobre la presencia de un posible obstácu= lo, switchgeando al acelerador del transmisor y redu= ciendo las revoluciones de los motores hasta desarmarlos.
  • Mediante un algoritmo de segmentación y procesamien= to de imágenes, se determino el numero de pixeles correspondientes a las plantas existentes en un terreno, y utilizando este valor, se logro determinar el área ocupada por el cultivo y el porcentaje de ocupación vegetal en relación con el área real del terreno.
  • Se verifico que el dron implementado puede seguir u= na trayectoria preestablecida, además, que las imágenes capturadas presen= tan condiciones aceptables y que el valor resultante del procesamiento de imágenes, tienen valores proporcionales al aérea total de la zona dond= e se llevo a efecto el estudio

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= Moribe, T., Okada, H., Kobayashl, K., & Katayama, M. (2018). Combination of a wireless sensor network and drone using infrared thermometers for smart agriculture. In CCNC 2018 - 2018 15th IEEE Annu= al Consumer Communications and Networking Conference. https://doi.org/10.1109/CCNC.2018.8319300

= Morita, M., Kinjo, H., & Sato, S. (2018). Autonomous flight drone for infrastructure (transmission line) inspection (3). In ICIIBMS 2017= - 2nd International Conference on Intelligent Informatics and Biomedical Sciences. https://doi.org/10.1109/ICIIBMS.2017.8279692

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= Tripicchio, P., Satler, M., Dabisias, G., Ruffaldi, E., & Avizza= no, C. A. (2015). Towards Smart Farming and Sustainable Agriculture with Dron= es. In Proceedings - 2015 International Conference on Intelligent Environments, IE 2015. https://doi.org/10.1109/IE.2015.29<= /span>

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Yallappa, D., Veerangouda, M., Maski, D., Palled,= V., & Bheemanna, M. (2017). Development and evaluation of drone mounted sprayer for pesticide applications to crops. In GHTC 2017 - IEEE Global Humanitarian Technology Conference, Proceedings. https://doi.org/10.1109/GHTC.2017.8239330

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PARA CITAR EL ARTÍCULO INDEXADO.

 

 

Santacruz Sulca, F., Cabrera Aguayo, F., Olmedo Carrillo, W., & Tacuri<= /span> Auquilla, J. (2019). Implementación de un Dron Anticolisiones Capaz de Segu= ir una Trayectoria Preestablecida para la Elaboración de un Mapa de la Tasa de Ocupación Vegetal en una Zona. Ciencia Digital, 3(3.3), 356-3= 74. https://doi.org= /10.33262/cienciadigital.v3i3.3.827

 


 

 

 

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El artículo queda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autoriz= ado por el director de la Revista Cien= cia Digital.

 

 

 

 

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[1] Escue= la Superior Politécnica de Chimborazo, Facultad de Informática y  Electrónica. Riobamba, Ecuador. fabrici= o.santacruz@espoch.edu.ec

[2]Escuela Superior Politécnica de Chimborazo, Facult= ad de Informática y  Electrónica. Riob= amba, Ecuador. fausto.cabrera@espoch.edu.ec

[3] Escuela Superior Politécnica de Chimborazo, Facul= tad de Informática y  Electrónica. Riob= amba, Ecuador. wilson.olmedo@espoch.edu.ec

[4] Escuela Superior Politécnica de Chimborazo, Facul= tad de Informática y  Electrónica. Riob= amba, Ecuador. jhony.tacuri@espoch.edu.ec

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www.cienciadigital.org

                                                 =                                                                       ISSN: 2602-8085

                                                 =                           Vol. 3, N°3.3, p. 356 - 374, septiembre, 20= 19

Educación Inclusiva                                                   <= /span>                                                =                              Página 162=

 

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