An automatic route planning method for unmanned aerial vehicle (UAV)-borne hyperspectral imaging systems

Abstract

The present invention belongs to the field of unmanned aerial vehicle technology, and in particular relates to an automatic route planning method for a hyperspectral imaging system carried by an unmanned aerial vehicle, which is used to solve the problem that the hyperspectral imaging system carried by an unmanned aerial vehicle cannot be adjusted according to the specific conditions of the target area, resulting in many short routes. The method only needs to input the longitude and latitude of the vertices of the polygonal target area, the altitude of the unmanned aerial vehicle above the ground, the field of view angle of the imaging spectrometer, and the image lateral overlap rate and other parameters in sequence, and can automatically and quickly generate a route file without the participation of any other software. The generated route meets the requirements of the longest and shortest route in the polygonal target area, greatly improving the flight efficiency of the unmanned aerial vehicle, thereby ensuring that more hyperspectral image data are obtained within a limited flight time, while ensuring that the image deformation error at the turning point does not affect the image quality within the regional range.

Claims

1. A method for automatic route planning for an unmanned aerial vehicle (UAV)-mounted hyperspectral imaging system, characterized in that: The following steps are involved: Obtaining coordinate data of each vertex of the first polygon containing the area to be measured; Determine the concavity and convexity of the first polygon, and if it is a concave polygon, convert the first polygon into a second polygon, and the second polygon is a convex polygon; The method for converting the first polygon into the second polygon comprises the following steps: connecting any three adjacent vertices on the first polygon in a clockwise direction to form two vectors; calculating the cross product of the two vectors, and if the cross product is positive, deleting the middle vertex among the three vertices; determining whether all vertices on the first polygon need to be deleted according to the above method, and connecting the vertices that do not need to be deleted to obtain the second polygon; The second polygon is proportionally enlarged to obtain a third polygon, the third polygon including the second polygon and a buffer zone, and coordinate data of each vertex of the third polygon is calculated. The method for proportionally enlarging the second polygon comprises the following steps: calculating an azimuth according to coordinate data of two adjacent vertices of the second polygon; calculating the angles of each internal angle of the second polygon according to the azimuth of the vertex of the second polygon; calculating an azimuth Az of a corresponding vertex of the third polygon relative to a vertex of the second polygon using the azimuth of the vertex of the second polygon; determining a distance d1 between a vertex of the second polygon and a corresponding vertex of the third polygon; calculating the coordinates of the corresponding vertex of the third polygon based on the vertex coordinate data of the second polygon, the azimuth Az, and the distance d1; and sequentially calculating the coordinates of all vertices of the third polygon to determine the third polygon. Determine the circumscribed rectangle of the third polygon, rotate the third polygon around the center of the circumscribed rectangle at equally spaced angles for a total of 180 degrees, and redefine the circumscribed rectangle each time the polygon is rotated, so that the sides of the redetermined circumscribed rectangle maintain their original orientations. Determine the minimum length d of a side a during the rotation process, and the total rotation angle A of the circumscribed rectangle at this time. t ; Divide the minimum length d by the flight distance between drones to obtain the minimum number of routes; The minimum number of routes is perpendicular to the side a and intersects with the side of the third polygon. The intersection points on each route are connected in the order of head-tail-tail-head to obtain the drone route, which is then rotated in the direction opposite to the rotation angle of the third polygon by A. t , the UAV route of the area to be measured can be obtained.

2. The automatic route planning method for an unmanned aerial vehicle (UAV)-mounted hyperspectral imaging system according to claim 1, characterized in that: The first polygon is the smallest polygon that includes the area to be measured.

3. The automatic route planning method for an unmanned aerial vehicle (UAV)-mounted hyperspectral imaging system according to claim 1 or 2, characterized in that: The coordinate data of each vertex of the first polygon is the longitude and latitude values ​​of the vertex.

4. The automatic route planning method for an unmanned aerial vehicle (UAV)-mounted hyperspectral imaging system according to claim 1 or 2, characterized in that: The side a is east-west.

5. The automatic route planning method for an unmanned aerial vehicle (UAV)-mounted hyperspectral imaging system according to claim 1, characterized in that: Determining the distance d1 between the second polygon vertex and the corresponding vertex of the third polygon comprises the following steps: Obtain the altitude H of the drone, the field of view FOV of the imaging spectrometer, and the image lateral overlap rate SO data during flight, and calculate the spacing SP of the drone's route: SP=H×tan(FOV / 2)×2×(100.0-SO); The distance between the second polygon and the third polygon is BD, where SP <BD<1.5*SP; According to BD and the second polygon's internal angle, d1 = BD / sin(the internal angle / 2).

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