UAV intelligent seeding path planning method, storage medium and UAV

Abstract

The present invention discloses a method for planning a UAV intelligent flying seeding path, including: obtaining the position data of the target area and the boundary of the target area to obtain the target area graphic; using drawing software to draw a flying seeding pattern in the target area graphic, and outputting the flying seeding pattern as a bitmap graphic file composed of multiple pixel points; calculating the longitude and latitude data of each pixel point in the bitmap graphic file according to the position data of the boundary of the target area graphic; using the pixel points as waypoints on the UAV flight path to plan the UAV flying seeding path. The present invention adopts the method of using the pixel points in the flying seeding pattern as the UAV flight waypoints to realize the planning of the UAV flying seeding path. The control accuracy of the UAV flying seeding path is high and the control is simple, so that it can be applied to the flying seeding operation and flying seeding control of complex patterns, especially multi-connected domain graphics.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and in particular to a method for planning an intelligent flying seeding path for unmanned aerial vehicles, a storage medium and a unmanned aerial vehicle. Background Art

[0002] With the development of drone technology, it has been widely used in various fields. In the field of agriculture and forestry, drones are not only used to monitor forests and desertified land, but also used in agricultural planting and planting of landscape plants.

[0003] Patent document CN107179776A discloses a method for sowing landscape plants based on drones. For sowing areas with irregular edge shapes, drones can operate accurately in areas near the borders. The algorithm optimizes the row scheduling order of drone operations, reduces the flight distance and time during non-effective operation periods, improves the operation quality, and makes the shape of the sowing area more accurate and beautiful. However, the optimal path algorithm for sowing adopted by it cannot be effectively applied to complex geometric combination figures, especially the processing of complex geometric figures with multiple non-connected domains in the same background; and for some figures with complex boundaries and fine composition, the image segmentation method adopted by it has certain errors in control. In the control process, it needs to be combined with the control of the drone's operating direction, making the entire control process more complicated and cumbersome, and cannot meet the requirements of sowing control of complex geometric combination figures. Summary of the invention

[0004] In view of the problems of low control accuracy and complex control process in existing aerial seeding control methods, the present invention provides a UAV intelligent aerial seeding path planning method, a storage medium and a UAV, which can realize accurate aerial seeding control of complex geometric combination figures.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0006] The UAV intelligent seeding path planning method includes:

[0007] Obtaining the location data of the target area and the boundary of the target area to obtain a target area graphic;

[0008] Drawing the aerial seeding pattern in the target area graph using drawing software, and outputting the aerial seeding pattern as a bitmap graphic file composed of pixel points;

[0009] Calculate the longitude and latitude data of each pixel point in the bitmap graphic file according to the position data of the graphic boundary of the target area;

[0010] The pixel points are used as waypoints on the UAV's flight path to plan the UAV's flight path.

[0011] In the above technical solution, further, the target area boundary includes an outer contour boundary of the target area and an outer contour boundary of an obstacle in the target area.

[0012] In the above technical solution, further, there is no intersection or overlap between the contour boundary of the aerial seeding pattern and the outer contour boundary of the obstacle in the target area.

[0013] In the above technical solution, further, the location data of the boundary of the target area includes longitude and latitude data of the boundary of the target area.

[0014] In the above technical solution, further, the step of calculating the longitude and latitude data of each pixel in the bitmap graphic file includes:

[0015] Obtain the longitude and latitude coordinate information of any two non-adjacent points in the target area, and calculate the area of ​​the target area based on the position data of the boundary of the target area;

[0016] The flying seeding pattern is enlarged in proportion to match the target area according to the area of ​​the target area, and the distance between adjacent pixel points in the bitmap graphic file of the flying seeding pattern is calculated;

[0017] The longitude and latitude data of each pixel in the bitmap graphic file are calculated based on the combination of calibration points.

[0018] In the above technical solution, further, according to the magnification ratio of the aerial seeding pattern in the target area, the number of pixel points between two adjacent waypoints on the aerial seeding path is set to determine the waypoints on the flight path of the UAV.

[0019] In the above technical solution, further, the step of planning the flying path of the drone includes:

[0020] Obtain the drone parameters, and use the optimal path algorithm to generate the optimal flight path of the drone based on the drone parameters and the pixel data of the seeding pattern.

[0021] In the above technical solution, further, the drone parameters include the minimum turning angle of the drone, the power consumption of the drone, the number of seeds carried by the drone, the sowing speed of the drone, and the flight time of the drone.

[0022] The present invention also provides a storage medium, which stores one or more execution programs, and the execution programs can be executed by a processor to implement the steps of the above-mentioned UAV intelligent seeding path planning method.

[0023] The present invention also provides a drone, comprising a processor and a storage medium connected to the processor, wherein the storage medium stores an execution program, and the processor calls the execution program in the storage medium to execute the steps of the drone intelligent seeding path planning method as described above.

[0024] The present invention adopts the method of using pixel points in the aerial seeding pattern as the flight waypoints of the UAV to realize the planning of the UAV aerial seeding path. The control accuracy of the UAV aerial seeding path is high and the implementation process is simple. It well solves the problems of low control accuracy, complex control process, and only applicable to the aerial seeding tasks of simple patterns in the existing UAV aerial seeding operations that rely on boundary information for path planning, so that it can be applied to the aerial seeding operations of complex patterns, especially multi-connected domain graphics.

[0025] The path planning method of the present invention can also solve the problem that the existing UAV aerial seeding operation relies on the control of the ground station, but the ground station often only supports limited path planning and is difficult to implement complex graphic operations. By using existing third-party drawing software to draw the aerial seeding pattern, it is no longer necessary to draw the graphics through the ground station, so that the aerial seeding operation of the UAV will not be restricted and affected by the ground station, and can be applicable to aerial seeding operations of various complex graphics. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 The figure is a flow chart of a method for planning a path for intelligent seeding of unmanned aerial vehicles in one embodiment of the present invention.

[0028] Figure 2 Schematic diagram of the flying seeding pattern in one embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0030] The UAV intelligent seeding path planning method of the present invention uses the pixel points in the seeding pattern as a reference for the UAV seeding path planning, and plans the flight path of the UAV based on the latitude and longitude data of these pixel points as a mapping basis, thereby achieving precise control of the UAV.

[0031] Embodiment 1

[0032] Reference Figure 1 , is a flow chart of a method for planning a path for intelligent seeding of unmanned aerial vehicles in one embodiment of the present invention, the method for planning a path for intelligent seeding of unmanned aerial vehicles comprising:

[0033] S101, acquiring the position data of the target area and the boundary of the target area, and obtaining a target area graph; the boundary of the target area includes the outer contour boundary of the target area and the outer contour boundary of obstacles in the target area; obstacles include ridges, electric poles, etc. in the target area;

[0034] S102, using drawing software to draw a flying seeding pattern in the target area graphic, and output the flying seeding pattern as a bitmap graphic file composed of multiple pixels, such as a graphic file in bmp format; the drawing software here can use existing general drawing software, and can draw and generate any complex and fine graphics as needed, such as Figure 2 As shown;

[0035] S103, calculating the longitude and latitude data of each pixel point in the bitmap graphic file according to the position data of the graphic boundary of the target area;

[0036] S104, using the pixel points in the above bitmap graphic file as waypoints on the UAV flight path to plan the UAV's sowing path; that is, using the pixel points as a reference for UAV flight path planning to control the flight of the UAV.

[0037] As an optional implementation, in this embodiment, the target area and the boundary of the target area can obtain relevant data and information based on the existing technology according to the satellite map and the existing surveying and mapping data. The location data of the boundary of the target area can be collected by using a drone to take aerial photos of the target area, or by using the RTK dotting device circle technology, or by obtaining relevant data based on the existing surveying and mapping data and satellite maps.

[0038] In one embodiment, there is no intersection or overlap between the contour boundary of the flying seeding pattern and the outer contour boundary of the obstacle in the target area. In this way, the flying seeding pattern avoids the area where the obstacle is located, making it applicable to flying seeding operations in various target areas, and providing a freer design space for the setting of the flying seeding pattern in the target area.

[0039] In one embodiment, specifically, the location data of the boundary of the target area includes the longitude and latitude data of the boundary of the target area, that is, the longitude and latitude data of the outer boundary of the target area and the longitude and latitude data of the outer contour boundary of the obstacle in the target area.

[0040] In one embodiment, the step of calculating the longitude and latitude data of each pixel point in the bitmap graphic file in step S103 includes:

[0041] Obtain the longitude and latitude coordinate information of any two non-adjacent points in the target area, and calculate the area of ​​the target area based on the position data of the boundary of the target area;

[0042] The flying seeding pattern is enlarged in proportion to match the target area according to the area of ​​the target area, and the distance between each adjacent pixel point in the bitmap graphic file of the flying seeding pattern is calculated to obtain the effective resolution of the graphic file;

[0043] The longitude and latitude data of each pixel in the bitmap graphic file are calculated based on the combination of calibration points. Specifically, the calculation process of the longitude and latitude data of each pixel is as follows: assuming that the plane where the pixel of the graphic file is located is a Cartesian coordinate system, selecting a pixel in the upper left corner of the graphic file as the calibration origin (or any point can be selected as the calibration origin), selecting a pixel in the lower right corner of the graphic file as the calibration reference point, the calibration reference point and the calibration origin can also be two arbitrary different pixel points with a certain distance interval, and each pixel in the graphic is geometrically converted according to the size of the effective resolution of the graphic file, so that the longitude and latitude data of each pixel in the bitmap graphic file can be obtained.

[0044] In one embodiment, the number of pixel intervals between two adjacent waypoints on the flying seeding path is set according to the magnification ratio of the flying seeding pattern in the target area and the performance parameters of the drone, and the waypoints on the drone flight path used for flying seeding path planning are determined.

[0045] In one embodiment, the step of planning the flying path of the drone in step S104 includes:

[0046] Obtain the drone parameters, and use the optimal path algorithm to generate the optimal flight path of the drone based on the drone parameters and the pixel data of the flying seeding pattern. The drone parameters here include the minimum steering angle of the drone, the power consumption of the drone, the number of seeds carried by the drone, the sowing speed of the drone, the endurance of the drone and other performance parameters.

[0047] Embodiment 2

[0048] A storage medium is also provided in an embodiment of the present invention, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the UAV intelligent seeding path planning method described in the above embodiment.

[0049] Embodiment 3

[0050] Based on the above embodiments, the present invention also provides a drone, including a processor and a storage medium connected to the processor, the storage medium stores an execution program, and the processor calls the execution program in the storage medium to execute the steps of the drone intelligent seeding path planning method described in the above embodiments.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for planning a path for intelligent seeding of unmanned aerial vehicles, characterized in that: include: Obtaining the location data of the target area and the boundary of the target area to obtain a target area graphic; Drawing the aerial seeding pattern in the target area graph using drawing software, and outputting the aerial seeding pattern as a bitmap graphic file composed of pixel points; Calculate the longitude and latitude data of each pixel point in the bitmap graphic file according to the position data of the graphic boundary of the target area; The pixel points are used as waypoints on the flight path of the drone to plan the flight path of the drone; The steps to determine the waypoints on the drone's flight path include: According to the area of ​​the target area, the aerial seeding pattern is enlarged in proportion to match the target area. According to the enlargement ratio of the aerial seeding pattern in the target area, the number of pixel points between two adjacent waypoints on the aerial seeding path is set to determine the waypoints on the UAV flight path.

2. The method for planning a path for intelligent seeding of unmanned aerial vehicles according to claim 1, characterized in that: The target area boundary includes the outer contour boundary of the target area and the outer contour boundary of obstacles in the target area.

3. The method for planning a path for intelligent seeding of unmanned aerial vehicles according to claim 2, characterized in that: There is no intersection or overlap between the contour boundary of the aerial seeding pattern and the outer contour boundary of the obstacle in the target area.

4. The method for planning a path for intelligent seeding of unmanned aerial vehicles according to claim 1 or 2, characterized in that: The location data of the boundary of the target area includes the latitude and longitude data of the boundary of the target area.

5. The method for planning a path for intelligent seeding of unmanned aerial vehicles according to claim 1, characterized in that: The step of calculating the longitude and latitude data of each pixel point in the bitmap graphic file comprises: Obtain the longitude and latitude coordinate information of any two non-adjacent points in the target area, and calculate the area of ​​the target area based on the position data of the boundary of the target area; The flying seeding pattern is enlarged in proportion to match the target area according to the area of ​​the target area, and the distance between adjacent pixel points in the bitmap graphic file of the flying seeding pattern is calculated; The longitude and latitude data of each pixel in the bitmap graphic file are calculated based on the combination of calibration points.

6. The method for planning a path for intelligent seeding of unmanned aerial vehicles according to claim 1, characterized in that: The steps for planning the flight path of the drone include: Obtain the drone parameters, and use the optimal path algorithm to generate the optimal flight path of the drone based on the drone parameters and the pixel data of the seeding pattern.

7. The method for planning a path for intelligent seeding of unmanned aerial vehicles according to claim 6, characterized in that: The drone parameters include the minimum turning angle of the drone, the power consumption of the drone, the number of seeds carried by the drone, the sowing speed of the drone, and the flight time of the drone.

8. A storage medium, characterized in that The storage medium stores one or more execution programs, and the execution programs can be executed by a processor to implement the steps of the UAV intelligent seeding path planning method as described in any one of claims 1 to 7.

9. A drone, characterized in that The drone includes a processor and a storage medium connected to the processor, the storage medium stores an execution program, and the processor calls the execution program in the storage medium to execute the steps of the drone intelligent seeding path planning method according to any one of claims 1 to 7.

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