WO2017099568A1 - Procédé de planification de survol de polygones irréguliers à l'aide d'au moins deux véhicules aériens sans pilotes pour l'agriculture de précision par analyse multispectrale et hyperspectrale d'images aériennes - Google Patents

Procédé de planification de survol de polygones irréguliers à l'aide d'au moins deux véhicules aériens sans pilotes pour l'agriculture de précision par analyse multispectrale et hyperspectrale d'images aériennes Download PDF

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Publication number
WO2017099568A1
WO2017099568A1 PCT/MX2015/000165 MX2015000165W WO2017099568A1 WO 2017099568 A1 WO2017099568 A1 WO 2017099568A1 MX 2015000165 W MX2015000165 W MX 2015000165W WO 2017099568 A1 WO2017099568 A1 WO 2017099568A1
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WO
WIPO (PCT)
Prior art keywords
polygon
multispectral
unmanned aerial
aerial vehicles
planning
Prior art date
Application number
PCT/MX2015/000165
Other languages
English (en)
Spanish (es)
Inventor
Jose Antonio Pacheco Sanchez
Original Assignee
Jose Antonio Pacheco Sanchez
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jose Antonio Pacheco Sanchez filed Critical Jose Antonio Pacheco Sanchez
Priority to PCT/MX2015/000165 priority Critical patent/WO2017099568A1/fr
Publication of WO2017099568A1 publication Critical patent/WO2017099568A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01B—SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL
    • A01B79/00—Methods for working soil
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03B—APPARATUS OR ARRANGEMENTS FOR TAKING PHOTOGRAPHS OR FOR PROJECTING OR VIEWING THEM; APPARATUS OR ARRANGEMENTS EMPLOYING ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ACCESSORIES THEREFOR
    • G03B37/00—Panoramic or wide-screen photography; Photographing extended surfaces, e.g. for surveying; Photographing internal surfaces, e.g. of pipe
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture

Definitions

  • the present invention relates to a method of flying over predefined crop fields using two or more computer-controlled drones and scanning to achieve photographic photography.
  • UAVs unmanned aerial vehicles
  • helicopters helicopters and multicopters
  • a permanent challenge is how to better control UAVs for each of these particular uses or in the performance of different tasks.
  • a UAV that is receiving more and more attention is the multirotor or multicopter.
  • This UAV is a helicopter with more than two rotors and multicopters often use fixed pitch motors, so that the movement control of the vehicle is achieved by varying the relative speed of each rotor to change the thrust and torque produced by each rotor.
  • multirotor aircraft Due to its ease of construction and control, multirotor aircraft are frequently used in aircraft model and radio control projects such that it provides a low-budget option for the creation of aerial photography and videos.
  • UAVs can carry as one payload one or more cameras and be remotely controlled to move over a specific geographic object or area.
  • unmanned aerial vehicles that need to be controlled in a centralized or organized way to perform the task.
  • numerous drones or unmanned aerial vehicles such as multicopters or flying robots can be used to provide surveillance of a geographical area.
  • the Swarm control can be used to control unmanned aerial vehicles while flying over the specific geographical area.
  • a swarm can be thought of as a system of self-organizing particles with numerous autonomous, reflective agents (for example, unmanned aerial vehicles are the particles in this case) whose collective movements are determined by local influences, such as wind and obstacles Like another UAV nearby.
  • Unmanned aerial vehicles are independent and are often controlled at the local level, which may include communication with a nearby UAV to determine which of them moves or if both should move to avoid an impending collision. Collisions are a problem since unmanned aerial vehicles move independently and randomly and will often have cross roads in the shared airspace. The swarm allows unmanned aerial vehicles to fly over a large area, which is useful in monitoring applications.
  • the design of dildos for use in unmanned aerial vehicles for swarms of objects or flying unmanned aerial vehicles remains a challenge for manufacturers of unmanned aerial vehicles and in some cases, collisions have proved very difficult to remove completely.
  • each UAV is controlled from a central controller that is normally placed on the ground.
  • a predetermined flight path is designed or selected for each UAV such that none intersects, and a tolerance or space envelope is offered to account for flight variations due to conditions such as the wind that can cause a UAV to deviate from its predefined course.
  • unmanned aerial vehicles operate independently without collisions.
  • Figure 1 shows the mission planning flowchart.
  • Figure 2 is the visualization of the delimitation of the region of interest using the google maps tool.
  • Figure 4 is the image of a field in the visual spectrum.
  • Figure 5 is the image of the same field in the red spectrum.
  • Figure 6 is the image of the same field in the NDVI spectrum.
  • the system has the ability to detect deficiencies of specific nutrients, irrigation, or presence of weeds or pests; by detecting the presence, in fas images, of the respective spectrate signatures, as well as the calculation of different indices used in Precision Agriculture. In addition to making suggestions to the user, about planning of application of agraqu ⁇ micos to correct the detected problems; making use of more information, coming from climatological analysis and predictions in the crop area (made outside the system).
  • the proposed method uses aerial photography taken on the crops to be analyzed, using one or more UAV aerial vehicles (for its acronym in English) with multispectral and hyperspectral sensors, in addition to GPS systems.
  • the first stage of the process is the planning of the mission (Fig. 1), which begins with the delimitation of the region of interest (RDl), drawing a polygon on a map on the system platform. (Fig. 2) These maps are taken from Google Maps, so they are free to use and have georeferencing; saving time and increasing the practicality of the procedure.
  • the result is a KML file with a series of geodetic coordinates that describe the polygon.
  • the next step in the mission planning procedure is the specification to the system of the necessary parameters, such as: UAV model to be used (for consideration of flight capabilities and autonomy), UAV flight speed and height, wind speed and direction, percentages of horizontal and vertical overlap of photographs.
  • UAV model to be used for consideration of flight capabilities and autonomy
  • UAV flight speed and height for consideration of flight capabilities and autonomy
  • wind speed and direction percentages of horizontal and vertical overlap of photographs.
  • the system will automatically determine the number of UAVs needed to cover the RDL completely, in the most efficient way possible. If the system considers the use of more than one UAV, the RDI will be partitioned and a part of it will be assigned to each of the UAVs involved. Otherwise, the system will assign the complete RDI to a single UAV.
  • the partitioning proposes an algorithm that receives as input a region specified as a polygon, which may be non-convex and not simple (may contain empty spaces within the polygon) and also receives the number of UAVs and their initial position on the periphery of the polygon.
  • the next step in the mission planning process is the multispectral or hyperspectral scan or aerial scan; which consists in determining the routes that the aircraft will follow, so that the RDI is photographed completely.
  • the routes determined by the system are usually zigzag.
  • Each scan line is called the flight line (Fig. 4 and 5).
  • the process used begins by converting the geodetic coordinates, which specify the polygon, to Cartesian coordinates in a local navigation reference frame (NED). This conversion is necessary to be able to use planning algorithms in Euclidean spaces. From that moment it is assumed that the surface to be explored has no curvature. This assumption is reasonable if we compare the size of the land with respect to the land area.
  • the conclusion of the mission planning process is the generation of GPX files by the system, which contain the waypoints and flight routes georeferenced for one or multiple UAVs.
  • the next stage of the process is the execution of the mission (fig. 6).
  • This stage begins with the introduction to the system of the product obtained in the planning of the mission; this being the GPX files generated.
  • This introduction is made through the software included with UAVs, or through free tools available, such as Mission Planner software.
  • the next step is to perform the aerial scan or scan, through the selected sensors and cameras. Captured images are saved in the camera memory. Which are georeferenced and ready for post processing. Completing the stage of mission execution.

Abstract

L'invention concerne un procédé au moyen duquel il est possible d'utiliser plusieurs aéronefs sans pilotes radiocommandés par ordinateur pour générer une carte photographique multispectrale d'un champ de culture afin de générer des informations qui aident à améliorer le rendement de culture ainsi qu'à déterminer la présence de maladies, plaies et mauvaises herbes.
PCT/MX2015/000165 2015-12-11 2015-12-11 Procédé de planification de survol de polygones irréguliers à l'aide d'au moins deux véhicules aériens sans pilotes pour l'agriculture de précision par analyse multispectrale et hyperspectrale d'images aériennes WO2017099568A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/MX2015/000165 WO2017099568A1 (fr) 2015-12-11 2015-12-11 Procédé de planification de survol de polygones irréguliers à l'aide d'au moins deux véhicules aériens sans pilotes pour l'agriculture de précision par analyse multispectrale et hyperspectrale d'images aériennes

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/MX2015/000165 WO2017099568A1 (fr) 2015-12-11 2015-12-11 Procédé de planification de survol de polygones irréguliers à l'aide d'au moins deux véhicules aériens sans pilotes pour l'agriculture de précision par analyse multispectrale et hyperspectrale d'images aériennes

Publications (1)

Publication Number Publication Date
WO2017099568A1 true WO2017099568A1 (fr) 2017-06-15

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PCT/MX2015/000165 WO2017099568A1 (fr) 2015-12-11 2015-12-11 Procédé de planification de survol de polygones irréguliers à l'aide d'au moins deux véhicules aériens sans pilotes pour l'agriculture de précision par analyse multispectrale et hyperspectrale d'images aériennes

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WO (1) WO2017099568A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108881825A (zh) * 2018-06-14 2018-11-23 华南农业大学 基于Jetson TK1的水稻杂草无人机监控系统及其监控方法
US20210343160A1 (en) * 2020-05-01 2021-11-04 Honeywell International Inc. Systems and methods for flight planning for conducting surveys by autonomous aerial vehicles
US11922620B2 (en) 2019-09-04 2024-03-05 Shake N Bake Llc UAV surveying system and methods

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5467271A (en) * 1993-12-17 1995-11-14 Trw, Inc. Mapping and analysis system for precision farming applications
US20050149235A1 (en) * 2002-08-19 2005-07-07 Seal Michael R. [method and system for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data]
WO2011160159A1 (fr) * 2010-06-25 2011-12-29 Cambium Land & Water Management Pty. Ltd. Système et procédé pour générer un image spectrale d'un lot de terrain
CN104794424A (zh) * 2014-01-20 2015-07-22 北京天合数维科技有限公司 一种新的中低分辨率遥感数据复合的耕地识别方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5467271A (en) * 1993-12-17 1995-11-14 Trw, Inc. Mapping and analysis system for precision farming applications
US20050149235A1 (en) * 2002-08-19 2005-07-07 Seal Michael R. [method and system for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data]
WO2011160159A1 (fr) * 2010-06-25 2011-12-29 Cambium Land & Water Management Pty. Ltd. Système et procédé pour générer un image spectrale d'un lot de terrain
CN104794424A (zh) * 2014-01-20 2015-07-22 北京天合数维科技有限公司 一种新的中低分辨率遥感数据复合的耕地识别方法

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108881825A (zh) * 2018-06-14 2018-11-23 华南农业大学 基于Jetson TK1的水稻杂草无人机监控系统及其监控方法
US11922620B2 (en) 2019-09-04 2024-03-05 Shake N Bake Llc UAV surveying system and methods
US20210343160A1 (en) * 2020-05-01 2021-11-04 Honeywell International Inc. Systems and methods for flight planning for conducting surveys by autonomous aerial vehicles
US11600185B2 (en) * 2020-05-01 2023-03-07 Honeywell International Inc. Systems and methods for flight planning for conducting surveys by autonomous aerial vehicles

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