Control method and system of unmanned aerial vehicle fleet for surveying and mapping

Abstract

The invention belongs to the technical field of unmanned aerial vehicle surveying and mapping, and provides a control method and system for a surveying and mapping unmanned aerial vehicle fleet, and the method comprises the steps: uniformly setting control points at the periphery of an object to be subjected to geological surveying and mapping, planning a course and an air route, recording data by an operator, and enabling the control points scanned by adjacent unmanned aerial vehicles to have at least three overlapped parts; thirdly, the unmanned aerial vehicle obtains scanning data, and point cloud is generated through calculation and the like; then, establishing an equation set through the coincident control points, and unifying the point cloud data of different unmanned aerial vehicles to the same coordinate system; then, analyzing a blank area of the point cloud model, determining a to-be-executed machine group according to the distance, and then evaluating the electric quantity to determine an executable machine group; and finally, dividing and allocating sub-regions according to the number of surveying and mapping groups and the shape and size of the blank region, and planning a surveying and mapping path, thereby improving surveying and mapping efficiency and avoiding resource waste.

Description

Technical Field

[0001] The present invention belongs to the technical field of UAV mapping, and specifically relates to a control method and system for a UAV fleet used for mapping. Background Art

[0002] With the popularization of UAVs, their applications have become increasingly widespread; in the field of geological mapping, UAVs can reach places that are difficult for humans to access to carry out work, which not only greatly improves the work efficiency of mapping personnel but also effectively avoids potential risks;

[0003] However, in current geological mapping work, due to the complex and changeable terrain and the large scale of mapping, it is often necessary to rely on the collaborative operation of a UAV fleet; but there are many problems in the existing technology: on the one hand, in the process of mapping data conversion, due to the lack of a scientific and systematic control point layout strategy, the data obtained by UAVs is prone to distortion, seriously affecting subsequent data processing, and thus greatly reducing the mapping efficiency; on the other hand, in the face of complex geological mapping scenarios, traditional scheduling methods are difficult to flexibly and accurately assign tasks to the UAV fleet according to the real-time mapping progress and regional characteristics. There is often over-mapping in some areas, consuming a lot of time and energy, while there are mapping omissions in some key areas, greatly restricting the improvement of the overall mapping efficiency.

[0004] Therefore, the present invention provides a control method and system for a UAV fleet used for mapping. Summary of the Invention

[0005] In order to make up for the deficiencies of the existing technology and solve at least one of the technical problems proposed in the background art.

[0006] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0007] In the first aspect, the present invention provides a control method for a UAV fleet used for mapping, including:

[0008] Step 1: Arrange UAV control points and perform pre-mapping on the control points to prevent data distortion;

[0009] Step 2: Obtain the real-time scan data of any UAV, and process the data to obtain point data clouds;

[0010] Step 3: Based on the point cloud data of adjacent UAVs, process and convert them into the same coordinate system, and then use the iterative fusion method to construct a point cloud model;

[0011] Step 4: According to the data of the blank areas in the point cloud model, obtain the adjacent distances, and judge the adjacent distances to obtain the UAV fleet to be mapped;

[0012] Step 5: Obtain the drones in the drone fleet to be surveyed and mapped, obtain the battery capacity and past flight data, calculate the flight support distance, and screen the flight support distance to obtain the surveying and mapping drone fleet;

[0013] Step 6: Process the blank area in the point cloud model to obtain a rectangular surface, divide it into corresponding rectangular sub-areas according to the number of the surveying and mapping drone fleet; assign surveying and mapping tasks to the drones according to the allocation distance.

[0014] In a second aspect, the present invention provides a control system for a surveying and mapping drone fleet, including:

[0015] Control point setting module: Arrange drone control points, and perform pre-surveying and mapping on the control points to prevent data distortion;

[0016] Data processing module: Obtain the real-time scanning data of any drone, and process the data to obtain point cloud data;

[0017] Conversion module: Based on the point cloud data of adjacent drones, process and convert them into the same coordinate system, and then use the iterative fusion method to construct a point cloud model;

[0018] Adjacent distance calculation module: Obtain the adjacent distance according to the blank area data in the point cloud model, and judge the adjacent distance to obtain the drone fleet to be surveyed and mapped;

[0019] Surveying and mapping drone fleet screening module: Obtain the drones in the drone fleet to be surveyed and mapped, obtain the battery capacity and past flight data, calculate the flight support distance, and screen the flight support distance to obtain the surveying and mapping drone fleet;

[0020] Task allocation module: Process the blank area in the point cloud model to obtain a rectangular surface, divide it into corresponding rectangular sub-areas according to the number of the surveying and mapping drone fleet; assign surveying and mapping tasks to the drones according to the allocation distance.

[0021] The beneficial effects of the present invention are as follows: Control points are evenly arranged around the object to be measured, and at least 3 overlapping control points exist between adjacent drones, preventing data distortion during the point cloud conversion process from the source and ensuring the consistency and accuracy of the data collected by each drone; Analyze the blank area in the point cloud model in real time, screen the drone fleet to be surveyed and mapped by calculating the distance between the drone and the center of the area and comparing it with the threshold, which can accurately locate the area that needs to be supplemented with surveying and mapping, and reasonably select appropriate drones to participate in the task, improving the pertinence and effectiveness of task scheduling; Estimate the endurance in combination with the battery capacity and flight data of the drones, further screen the surveying and mapping drone fleet, avoid task interruption or drone loss of connection caused by insufficient power, ensure the safety and reliability of task execution, and at the same time optimize the use efficiency of the drones and realize the reasonable allocation of resources. Description of the Drawings

[0022] The present invention will be further described below in conjunction with the accompanying drawings.

[0023] Figure 1 is the flowchart of the steps of Embodiment 1 of the present invention;

[0024] Figure 2 is the system module diagram of Embodiment 2 of the present invention. Specific Embodiments

[0025] In order to make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0026] Embodiment 1

[0027] As Figure 1 shown, a control method for a drone fleet for surveying and mapping according to an embodiment of the present invention includes:

[0028] Step 1: Arrange drone control points, and perform pre-surveying on the control points to prevent data distortion;

[0029] Around the object to be geologically surveyed, evenly distribute around the object to be surveyed according to the number of G control points equipped for each drone; the value of G is adjusted according to the actual situation;

[0030] And plan the drone heading and flight path according to the environmental factors of each take-off point; the drone operator records the data of the control points at this time; before officially carrying out geological surveying, it is necessary to use the drone to pre-survey the control point information in advance; each drone has to survey the control points and record the corresponding information; it should be particularly noted that among the control points surveyed by two adjacent drones, at least M need to overlap; where M is preset to be 3; the purpose of doing this is to effectively avoid possible data distortion problems during the subsequent point cloud conversion process, so as to ensure the accuracy and reliability of the geological survey data;

[0031] In this solution, the drone is equipped with a lidar, and the lidar is used to perform real-time scanning on the geological survey object; the lidar is arranged in the center at the bottom of the drone; the lidar calculates the flight time by emitting laser pulses and receiving reflected signals to generate high-precision three-dimensional coordinates;

[0032] Step 2: The drone set based on any one of the control points obtains the real-time scanning data of the geological survey object by the lidar according to the drone heading and flight path, and uses the real-time scanning data to generate a point cloud through data processing and conversion;

[0033] Specifically, the process of lidar generating point clouds mainly includes steps such as laser emission and reflection, signal reception and processing, and coordinate calculation; the lidar emits laser beams into the surrounding space through the emission system, and these laser beams propagate in the air at the speed of light c; when the laser beams encounter an object, reflection occurs, and part of the reflected light returns to the lidar along the original path; after the receiving system of the lidar receives the reflected light signal, it records the time t experienced by the laser from emission to reception; according to the principle of the constancy of the speed of light, using the formula Calculate the distance d between the drone and the object to be geologically surveyed;

[0034] To convert from polar coordinates to Cartesian coordinates, the lidar usually measures the angular information of the laser beam, including the horizontal angle θ and the vertical angle φ. In the polar coordinate system, given the distance d, horizontal angle θ, and vertical angle φ to the object to be geologically surveyed, convert them to coordinates in the Cartesian coordinate system; the specific conversion process uses the following formulas;

[0035] For the conversion of the horizontal axis coordinate x, the formula used is: x = d·sinφ·cosθ;

[0036] For the conversion of the vertical axis coordinate y, the formula used is: y = d·sinφ·sinθ;

[0037] For the conversion of the vertical axis coordinate z, the formula used is: z = d·cosθ;

[0038] During the actual flight of the drone, due to the flight attitude, the lidar on the drone will have a certain deviation angle in the horizontal direction, and the deviation angles include the pitch angle α, roll angle β, and yaw angle γ; at this time, it is necessary to perform rotation and translation transformations on the calculated coordinates above to obtain the accurate coordinates in the world coordinate system;

[0039] Assume the position of the lidar in the world coordinate system is (x f , y f , z f ) Then the coordinates (x w , y w , z w ) after the attitude transformation are: Among them, r ij is the rotation matrix, and each element r ij (i = 1, 2, 3; j = 1, 2, 3) in the rotation matrix is calculated through trigonometric function combinations of the pitch angle α, roll angle β, and yaw angle γ of the lidar, and they determine the rotation relationship of the lidar coordinate system relative to the world coordinate system. The specific calculation formulas are as follows:

[0040] The rotation matrix r x for rotating by the angle β around the x-axis is:

[0041] The rotation matrix r for rotating by an angle α around the y-axis y is as follows:

[0042] The rotation matrix r for rotating by an angle γ around the z-axis z is as follows:

[0043] By continuously emitting lidar beams with the drone and repeating the above process, the lidar can obtain the coordinate information of a large number of points. Using these points, point cloud data is formed to describe the positions and shapes of objects in the surrounding environment;

[0044] Step 3: Based on the point cloud data of adjacent drones, pairwise matching is achieved through the coordinate transformation matrix and the point cloud data is unified into the same coordinate system. Then, an iterative fusion strategy is adopted to gradually align and unify all the drone group data into the same coordinate system, and finally a complete point cloud model is constructed;

[0045] Based on the known mapping results of control points, since every two adjacent drones will pre-map the control points during geological mapping and at least three of the geological mapping points of every two adjacent drones coincide. Assume that the three coordinate points in the point cloud data coordinate system generated by the first drone are respectively: (x1, y1, z1), (x2, y2, z2), (x3, y3, z3); and in the point cloud data coordinate system generated by the second drone are respectively: (x1′, y1′, z1′), (x′2, y′2, z′2), (x3′, y3′, z′3); The corresponding points are used to construct a system of equations to solve the rotation matrix R and the translation vector T;

[0046] Assume that the conversion from the point cloud data coordinate system generated by the first drone to the point cloud data coordinate system generated by the second drone includes rotation and translation. The rotation matrix is R and the translation vector is T; For the control points in the point cloud data coordinate system generated by the first drone, the corresponding points in the point cloud data coordinate system generated by the second drone are P = (x′, y′, z′) T ; Their corresponding relationship is P′ = R·P + T; The above corresponding points are used to establish equations to solve the rotation matrix R and the translation vector T;

[0047] In this way, 9 rotation matrices R and translation vectors T are obtained. Using the average value method, the final rotation matrix R and translation vector T are obtained;

[0048] Transformation of point cloud data: Apply the calculated transformation matrix to one set of point cloud data to transform its coordinates to the same coordinate system as another set of point cloud data. For each point in the point cloud data of the first UAV, perform coordinate transformation through matrix multiplication and addition operations P′ = R·P + T, where R is the rotation matrix, T is the translation vector, and P′ is the transformed coordinate; P is the corresponding point in the coordinate system of the point cloud data of the second UAV.

[0049] According to the above method, adopt an iterative fusion strategy to gradually align the point cloud data generated by all UAV groups pairwise into the same coordinate system.

[0050] Adopting an iterative fusion strategy to decompose global matching into multiple pairwise alignment subtasks saves computing power requirements and improves matching accuracy.

[0051] Step 4: Based on the data of the blank area in the point cloud model, obtain the adjacent distance, and judge the adjacent distance to obtain the UAV group to be surveyed.

[0052] Specifically, based on the point cloud model, analyze in real time the data of the missing blank area in the point cloud data map, dispatch the UAV group according to the operation conditions of the UAVs, and conduct geological survey on the missing blank area data.

[0053] The specific operation is as follows: Based on the data of the missing blank area in the point cloud model, determine its geometric center point; then calculate the distance between the current position of each UAV and the center point of the blank area; assume that the position of the UAV at this time is at point A and the position of the blank area is at point B, and judge whether the UAV is in the adjacent position of the blank area based on the Euclidean distance formula; use the Euclidean distance formula: Calculate the adjacent distance; compare the obtained adjacent distance with the adjacent distance threshold.

[0054] It should be noted that the adjacent distance threshold is a reference value obtained by technicians in this industry based on historical geological survey experience.

[0055] If the adjacent distance is greater than or equal to the adjacent distance threshold, it is determined that the UAV is not within the adjacent distance; when the UAVs outside the adjacent distance area complete the geological survey task, they can return along the predetermined route without any planning processing.

[0056] If the adjacent position is less than the adjacent distance threshold, it is determined that the UAV is within the adjacent distance; mark all UAVs as: the UAV group to be surveyed.

[0057] Step 5: Obtain the UAVs in the UAV group to be surveyed, obtain the battery capacity and previous flight data, calculate the flight support distance, and screen the flight support distance to obtain the surveying UAV group.

[0058] Specifically, based on the drones in the drone fleet to be surveyed, obtain the battery capacity and past flight data of any one drone, establish the relationship between power consumption and flight distance, so as to estimate the maximum flight distance that the remaining power can support; use the formula: Calculate the maximum flight distance D that the remaining power can support; where E is the remaining power of the drone, E0 is the total battery capacity of the drone, k is the proportionality coefficient between power consumption and flight distance; d0 is the distance the drone has flown;

[0059] Then use the formula: S = D - 2H; Calculate the flight support distance; D is the maximum flight distance that the remaining power can support, and H is the adjacent distance;

[0060] Based on all the drones in the drone fleet to be surveyed, if the flight support distance is greater than or equal to 0, it means that the drone can perform geological survey tasks; then mark all the drones as the survey drone fleet; if the flight support distance is less than 0, it means that the drone cannot perform geological survey tasks; it can return along the predetermined route without any planning process; reasonably plan the drone survey tasks according to the drone power to prevent resource waste caused by the drone losing contact due to too low power.

[0061] Compared with the traditional scheduling method, it is difficult to dynamically and accurately assign tasks to drones according to the real-time survey progress and regional characteristics; often there is repeated survey in some areas, wasting a lot of time and energy, while there are survey omissions in some key areas, greatly reducing the overall survey efficiency.

[0062] Step Six: Based on the blank area in the point cloud model, process it to obtain a rectangular surface, and divide it into corresponding rectangular sub-areas according to the number of the survey drone fleet; assign survey tasks to the drones according to the allocation distance.

[0063] Specifically, obtain the shape and size of the blank area rectangular surface by processing the blank area in the point cloud model, then divide it into different sub-areas, and then assign corresponding sub-areas to each drone in the drone fleet to be surveyed and plan their respective survey paths.

[0064] Specifically, based on the point cloud model, plan the irregular blank area into a regular blank area; find the maximum and minimum values of the blank area in the x-axis and y-axis directions to determine the dimensional information of the blank area in space; and divide the blank area into blank area rectangular surfaces.

[0065] Specifically, the calculation method on the x-axis is: L x = Xmax - Xmin; the calculation method on the y-axis is: L y= Ymax - Ymin; Mark the circumscribed rectangle of the blank area as: the rectangular surface of the blank area. Let the length of the rectangular surface of the blank area in the x-axis direction be X, and the length in the y-axis direction be Y, and count the number N of the current mapping aircraft fleet;

[0066] According to the number N of the mapping aircraft fleet, divide the planned blank area into several equal parts, that is, evenly divide the rectangular surface of the blank area into N rectangular sub-areas; Use the formula on the x-axis: for division, where X is the length of the rectangular surface of the blank area on the x-axis, and N is the number of the mapping aircraft fleet; Use the formula on the y-axis: for division, where Y is the length of the rectangular surface of the blank area on the y-axis, and N is the number of the mapping aircraft fleet;

[0067] For each unmanned aircraft a (a = 1, 2,..., N), obtain its coordinates (x a , y a , z a ) in space; For each rectangular sub-area b (b = 1, 2,..., N), determine its coordinate range [x b 1, x b 2] in the x-axis direction and the coordinate range [y b 1, y b 2] in the y-axis direction;

[0068] Based on any one unmanned aircraft, calculate the horizontal distance from the unmanned aircraft a to the center of the small rectangular sub-area j, denoted as the allocation distance, using the formula: where, is the abscissa of the center of the rectangular sub-area j, is the ordinate of the center of the rectangular sub-area j, x a is the abscissa of the unmanned aircraft a, y a is the ordinate of the unmanned aircraft a, z a is the vertical coordinate of the unmanned aircraft a; Compare the distance from C IJ to each rectangular sub-area, select the nearest allocation distance and mark it as jmin, and assign the unmanned aircraft a with the nearest allocation distance to the corresponding sub-area to be responsible for the mapping task of that sub-area;

[0069] The technical solution of this embodiment is as follows: Control points are evenly arranged around the object to be surveyed and mapped. Before the formal surveying and mapping, each UAV first pre-surveys the control points, and at least 3 points of adjacent UAVs overlap to prevent data distortion. Obtain the lidar scan data to generate point cloud data, and use the overlapping control points to unify the point cloud data of adjacent UAVs into the same coordinate system to obtain a point cloud model. Analyze the blank areas in the point cloud model, calculate the distance between the UAV and the center of the area, and compare it with the threshold to screen the task-executing UAV fleet. Combine the battery and flight data of the UAVs in the fleet to estimate the endurance and screen the task-executing UAV fleet. Plan the irregular blank areas into rectangles, divide the sub-areas according to the number of the fleet, and mobilize the UAV fleet according to the distance to allocate the subsequent surveying and mapping tasks.

[0070] Embodiment 2

[0071] As Figure 2 shown, based on Embodiment 1, the present invention provides a control system for a UAV fleet for surveying and mapping, including:

[0072] Control point setting module: Arrange the UAV control points and pre-survey the control points to prevent data distortion.

[0073] Around the object to be geologically surveyed and mapped, the control points for the object to be surveyed and mapped are evenly arranged according to the number of G control points equipped for each UAV; the value of G is adjusted according to the actual situation.

[0074] And plan the UAV heading and flight path according to the environmental factors of each take-off point; the UAV operator records the data of the control points at this time. Before the formal geological surveying and mapping, the UAVs pre-survey the information of the control points. Each UAV pre-scans the control points and records the information of the control points, and at least M control points in the adjacent UAVs are the overlapping parts, where the preset value of M is 3, to prevent data distortion during the subsequent point cloud conversion process.

[0075] Data processing module: Based on the UAV set at any one control point, obtain the real-time scan data of the geological surveying and mapping object by the lidar according to the UAV heading and flight path, and use the real-time scan data to generate a point cloud through data processing and conversion.

[0076] Specifically, the process of generating a point cloud by the lidar mainly includes steps such as laser emission and reflection, signal reception and processing, and coordinate calculation; the lidar emits laser beams into the surrounding space through the emission system, and these laser beams propagate in the air at the speed of light c. When the laser beam encounters an object, reflection will occur, and part of the reflected light will return to the lidar along the original path. After the receiving system of the lidar receives the reflected light signal, it will record the time t experienced by the laser from emission to reception. According to the principle of the constancy of the speed of light, use the formula to calculate the distance d between the UAV and the object to be geologically surveyed and mapped.

[0077] To convert from polar coordinates to Cartesian coordinates, a lidar typically measures the angular information of the laser beam, including the horizontal angle θ and the vertical angle φ. In the polar coordinate system, given the distance d, the horizontal angle θ, and the vertical angle φ to the object to be geologically surveyed, they are converted into coordinates in the Cartesian coordinate system. The specific conversion process uses the following formulas;

[0078] For the conversion of the horizontal axis coordinate x, the formula used is: x = d·sinφ·cosθ;

[0079] For the conversion of the vertical axis coordinate y, the formula used is: y = d·sinφ·sinθ;

[0080] For the conversion of the vertical axis coordinate z, the formula used is: z = d·cosθ;

[0081] During the actual flight of the unmanned aerial vehicle (UAV), due to the flight attitude, the lidar on the UAV will have a certain deviation angle in the horizontal direction. The deviation angles include the pitch angle α, the roll angle β, and the yaw angle γ. At this time, the coordinates calculated above need to be rotated and translated to obtain the accurate coordinates in the world coordinate system;

[0082] Assume the position of the lidar in the world coordinate system is (x f , y f , z f ). Then the coordinates (x w , y w , z w ) after the attitude transformation are: Among them, r ij is the rotation matrix. Each element r ij (i = 1, 2, 3; j = 1, 2, 3) in the rotation matrix is calculated through trigonometric function combinations of the pitch angle α, the roll angle β, and the yaw angle γ of the lidar. They determine the rotation relationship of the lidar coordinate system relative to the world coordinate system. The specific calculation formulas are as follows:

[0083] The rotation matrix r x for rotating by the angle β around the x-axis is:

[0084] The rotation matrix r y for rotating by the angle α around the y-axis is:

[0085] The rotation matrix r z for rotating by the angle γ around the z-axis is:

[0086] By continuously emitting lidar beams with the drone and repeating the above process, the lidar can obtain the coordinate information of a large number of points. Using these points, point cloud data is formed to describe the positions and shapes of objects in the surrounding environment;

[0087] Transformation module: Based on the point cloud data of adjacent drones, pairwise matching is achieved through a coordinate transformation matrix and the point cloud data is unified into the same coordinate system. Then, an iterative fusion strategy is adopted to gradually align and unify all the drone group data into the same coordinate system, and finally a complete point cloud model is constructed;

[0088] Based on the survey results of known control points, since every two adjacent drones will pre-survey the control points during geological survey and at least three of the control points surveyed by two adjacent drones coincide. Suppose the three coordinate points in the point cloud data coordinate system generated by the first drone are: (x1, y1, z1), (x2, y2, z2), (x3, y3, z3); and in the point cloud data coordinate system generated by the second drone are: (x1′, y1′, z1′), (x′2, y′2, z′2), (x3′, y3′, z′3); equations are constructed for the corresponding points to solve the rotation matrix R and the translation vector T;

[0089] Suppose the process of converting from the point cloud data coordinate system generated by the first drone to the point cloud data coordinate system generated by the second drone includes rotation and translation; among them, the rotation matrix is R and the translation vector is T; for the control points in the point cloud data coordinate system generated by the first drone, the corresponding points in the point cloud data coordinate system generated by the second drone are P = (x′, y′, z′) T ; their corresponding relationship is P′ = R·P + T; equations are established for the above corresponding points to solve the rotation matrix R and the translation vector T;

[0090] In this way, 9 rotation matrices R and translation vectors T are obtained. Using the average value method, the final rotation matrix R and translation vector T are obtained;

[0091] Transformation of point cloud data: Apply the calculated transformation matrix to one set of point cloud data to transform its coordinates to the same coordinate system as the other set of point cloud data; for each point in the point cloud data of the first drone, coordinate transformation is performed through matrix multiplication and addition operations P′ = R·P + T, where R is the rotation matrix, T is the translation vector, P′ is the transformed coordinate; P is the corresponding point in the point cloud data coordinate system of the second drone;

[0092] Adjacent distance calculation module: Obtain the adjacent distance based on the blank area data in the point cloud model, and judge the adjacent distance to obtain the drone group to be surveyed;

[0093] Specifically, based on the point cloud model, the blank area data missing in the point cloud data map is analyzed in real time, and the drone fleet is scheduled according to the operation of the drones to conduct geological mapping on the missing blank area data;

[0094] The specific operations are as follows: Based on the blank area data missing in the point cloud model, its geometric center point is determined; then the distance between the current position of each drone and the center point of the blank area is calculated; assuming the current position of the drone is at point A and the position of the blank area is at point B, based on the Euclidean distance formula, it is judged whether the drone is in the adjacent position of the blank area; using the Euclidean distance formula: The adjacent distance is calculated; the obtained adjacent distance is compared with the adjacent distance threshold;

[0095] It should be noted that the adjacent distance threshold is a reference value obtained by technicians in this industry based on historical geological mapping experience;

[0096] If the adjacent distance is greater than or equal to the adjacent distance threshold, it is determined that the drone is not within the adjacent distance; when the drone not in the adjacent distance area completes the geological mapping task, it can return along the predetermined route without any planning processing;

[0097] If the adjacent position is less than the adjacent distance threshold, it is determined that the drone is within the adjacent distance; all drones are marked as: the drone fleet to be surveyed;

[0098] Surveying drone fleet screening module: Obtain the drones in the drone fleet to be surveyed, obtain the battery capacity and past flight data, calculate the flight support distance, and screen the flight support distance to obtain the surveying drone fleet;

[0099] Specifically, based on the drones in the drone fleet to be surveyed, obtain the battery capacity and past flight data of any one drone, establish the relationship between power consumption and flight distance, so as to estimate the maximum flight distance that the remaining power can support; using the formula: The maximum flight distance D that the remaining power can support is calculated; where E is the remaining power of the drone, E0 is the total battery capacity of the drone, k is the proportional coefficient of power consumption and flight distance; d0 is the distance that the drone has flown;

[0100] Then use the formula: S = D - 2H; calculate the flight support distance; D is the maximum flight distance that the remaining power can support, and H is the adjacent distance;

[0101] Based on the drones in all the drone fleets to be surveyed, if the flight support distance is greater than or equal to 0, it means that the drone can perform geological survey tasks; then all drones are marked as the survey drone fleet; if the flight support distance is less than 0, it means that the drone cannot perform geological survey tasks; it can then return along the predetermined route without any planning processing; reasonably plan the drone survey tasks according to the drone battery power to prevent waste of resources caused by the drone losing contact due to too low battery power;

[0102] Task allocation module: Process the blank area in the point cloud model to obtain a rectangular surface, and divide it into corresponding rectangular sub-areas according to the number of the survey drone fleet; allocate survey tasks to the drones according to the allocation distance;

[0103] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A control method for a fleet of UAVs for surveying and mapping, characterized in that: include: Step 1: Arrange the drone control points and predict the control points to prevent data distortion; Step 2: Obtain real-time scanning data from any drone and process the data to obtain a point data cloud; Step 3: Based on the point cloud data of adjacent drones, transform them into the same coordinate system through processing, and then use the iterative fusion method to construct the point cloud model; Step 4: Obtain the adjacent distance according to the blank area data in the point cloud model, and determine the adjacent distance to obtain the group of machines to be surveyed; Step 5: Obtain the drones in the fleet to be surveyed, obtain the battery capacity and previous flight data, calculate the flight support distance, and filter the flight support distance to obtain the surveying fleet; Step 6: Obtain a rectangular surface based on the blank area in the point cloud model, and divide it into a corresponding number of rectangular surface sub-areas according to the number of surveying and mapping aircraft groups; assign surveying and mapping tasks to drones according to the assigned distance.

2. The control method of a surveying and mapping drone fleet according to claim 1, characterized in that: The specific process of arranging the drone control points is as follows: Around the object to be geologically surveyed, the number of control points G equipped on each UAV is evenly distributed around the object to be surveyed; the value of G is adjusted according to the actual situation.

3. The control method of a surveying and mapping drone fleet according to claim 1, characterized in that: The specific process of predicting the control points to prevent data distortion is as follows: Each UAV pre-scans the control points and records the information of the control points. The scanned control points have at least M control points in adjacent UAVs, which are overlapping parts. The preset value of M is 3.

4. The control method of a surveying and mapping drone fleet according to claim 1, characterized in that: The specific process of obtaining the adjacent distance is: based on the missing blank area data in the point cloud model, determine its geometric center point; and calculate the adjacent distance using the Euclidean distance formula.

5. The control method of a surveying and mapping drone fleet according to claim 1, characterized in that: The specific process of determining the adjacent distance to obtain the group of machines to be surveyed is: comparing the obtained adjacent distance with the adjacent distance threshold; It should be noted that the adjacent distance threshold is a reference value obtained by technicians in this industry based on historical geological surveying and mapping experience; If the adjacent distance is greater than or equal to the adjacent distance threshold, it is determined that the drone is not within the adjacent distance; If the adjacent position is less than the adjacent distance threshold, the drone is determined to be within the adjacent distance; all drones are marked as: the group to be surveyed.

6. The control method of a surveying and mapping drone fleet according to claim 1, characterized in that: The specific process of obtaining the battery capacity and previous flight data and calculating the supported flight distance is as follows: Using the formula: The maximum flight distance D that can be supported by the remaining power is calculated; where E is the remaining power of the drone, E0 is the total battery capacity of the drone, k is the proportional coefficient between power consumption and flight distance; d0 is the distance the drone has flown.

7. The control method of a surveying and mapping drone fleet according to claim 1, characterized in that: The specific process of screening the flight support distance to obtain the mapping aircraft group is as follows: The flight support distance is calculated using the formula: S = D-2H; D is the maximum flight distance supported by the remaining power, and H is the adjacent distance; Based on all the drones in the fleet to be surveyed, if the flight support distance is greater than or equal to 0, it means that the drone can perform geological surveying tasks; then all drones are marked as a surveying fleet; if the flight support distance is less than 0, it means that the drone cannot perform geological surveying tasks.

8. The control method of a surveying and mapping UAV fleet according to claim 1, characterized in that: The specific process of obtaining a rectangular surface based on the blank area processing in the point cloud model is as follows: Based on the point cloud model, the irregular blank area is planned into a regular blank area; the maximum and minimum values ​​of the blank area in the x-axis and y-axis directions are found to determine the dimensional information of the blank area in space; and the blank area is divided into blank area rectangular surfaces; Specifically, the calculation method on the x-axis is: L x =Xmax-Xmin; the calculation method on the y-axis is: L y =Ymax-Ymin; mark the circumscribed rectangle of the blank area as: blank area rectangular surface.

9. The control method of a surveying and mapping UAV fleet according to claim 1, characterized in that: The specific process of dividing the surveying and mapping machine group into a corresponding number of rectangular surface areas is as follows: According to the number of surveying and mapping machines N, the planned blank area is divided into several equal parts, that is, the rectangular surface of the blank area is evenly divided into N rectangular surface areas; on the x-axis, the formula is used: Divide, X is the length of the blank area rectangle on the x-axis, N is the number of surveying and mapping machines; on the y-axis, use the formula: Divide, Y is the length of the blank area rectangular surface on the y-axis.

10. A control system for a fleet of surveying and mapping UAVs, the system being used to execute the control method according to any one of claims 1 to 9, characterized in that: include: Control point setting module: arrange the control points of the drone and predict and draw the control points to prevent data distortion; Data processing module: obtains real-time scanning data from any drone and processes the data to obtain a point data cloud; Conversion module: Based on the point cloud data of adjacent drones, it is converted into the same coordinate system through processing, and then the point cloud model is constructed using the iterative fusion method; Adjacent distance calculation module: according to the blank area data in the point cloud model, the adjacent distance is obtained, and the adjacent distance is judged to obtain the group of machines to be surveyed; Surveying and mapping fleet screening module: obtains the drones in the fleet to be surveyed, obtains the battery capacity and previous flight data, calculates the flight support distance, and screens the surveying and mapping fleet based on the flight support distance; Task allocation module: Based on the blank area processing in the point cloud model, a rectangular surface is obtained, which is divided into a corresponding number of rectangular surface sub-areas according to the number of surveying and mapping aircraft groups; surveying and mapping tasks are allocated to drones according to the allocated distance.

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