Multi-unmanned aerial vehicle area scanning track generation method, device and equipment and storage medium

Abstract

The invention relates to a multi-unmanned aerial vehicle area scanning track generation method and device, equipment and a storage medium. The method comprises the following steps: acquiring environment element information of a task area and unmanned aerial vehicle cluster element information for executing a task; planning an initial coverage trajectory of the task area, wherein the initial coverage trajectory is a reciprocating path of a set path interval matched with the width of the individual detection range of the unmanned aerial vehicle; segmenting the initial coverage trajectory into trajectory segments of which the number is the same as that of the unmanned aerial vehicles, and pairing the trajectory segments with the unmanned aerial vehicles; adjusting the segmentation point positions of the track segments, and searching the segmentation point positions which enable the distance length difference from the takeoff position to the detection of the paired track segments of each unmanned aerial vehicle to be smaller than a set distance threshold value; and outputting the track segment after the position of the segmentation point is adjusted as a region scanning track of the paired unmanned aerial vehicle. According to the invention, track distribution of multi-unmanned aerial vehicle cooperative area scanning is realized, and the scanning efficiency of the target area is improved while the coverage rate of the target area is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV trajectory generation, and particularly to a method, device, equipment and storage medium for generating multi-UAV area scanning trajectories. Background Art

[0002] In the applications of modern industries, unmanned aerial vehicles (UAVs) are widely used in various tasks, including reconnaissance, surveillance, search and rescue, etc., due to their flexibility and high efficiency. Especially in the scanning tasks of large-scale areas, the collaborative work of multiple UAVs can significantly improve the task efficiency. However, there are some deficiencies in the existing multi-UAV collaborative scanning methods, and the optimal task execution efficiency and the shortest total time consumption cannot be achieved.

[0003] Currently, traditional path planning methods usually evenly distribute the task area to each UAV. However, this method does not consider the distance difference between the UAV take-off position and the task area, resulting in different arrival times of each UAV at the task area. Due to the different arrival times of the UAVs at the task area, the method of evenly distributing the task area is difficult to ensure that all UAVs can complete the task simultaneously, thus the goal of the shortest final time consumption cannot be achieved.

[0004] In addition, the existing systems also have deficiencies in dynamic adjustment and real-time optimization. During the actual task execution process, various unforeseen situations may occur, such as weather changes, UAV failures, etc. However, the existing methods lack the flexibility to handle these situations and cannot adjust the task allocation and path planning according to the real-time situation, resulting in low task execution efficiency.

[0005] Therefore, more advanced area scanning algorithms are needed to achieve more efficient and flexible multi-UAV collaborative work, thereby promoting the wide application of UAV technology in complex tasks and improving the overall operation efficiency. Summary of the Invention

[0006] In view of the above analysis, the present invention aims to disclose a method, device, equipment and storage medium for generating multi-UAV area scanning trajectories; to solve the problem of trajectory allocation for multi-UAV collaborative area scanning and achieve the goal of reducing the final scanning time consumption.

[0007] The present invention discloses a method for generating multi-UAV area scanning trajectories, including:

[0008] Step S1, obtaining the environmental element information of the task area and the UAV cluster element information for performing the task;

[0009] Step S2, planning an initial coverage trajectory of the task area according to the obtained element information, where the initial coverage trajectory is a reciprocating path with a set path interval matching the width of the individual detection range of the UAV;

[0010] Step S3: Divide the initial coverage trajectory into trajectory segments equal in number to the number of drones, and pair the trajectory segments with the drones; adjust the positions of the segmentation points of the trajectory segments, and search for the positions of the segmentation points that make the difference in the path lengths of each drone from the takeoff position to the detection of the paired trajectory segments less than the set distance threshold; output the trajectory segments with adjusted segmentation point positions as the area scanning trajectories of the paired drones.

[0011] Further, the obtaining of the environmental element information of the task area includes an anchor point, a size, and a main axis direction;

[0012] The anchor point is used to determine the position of the task area; the size is used to determine the size of the task area; the main axis direction is determined by the shape of the task area and is used to determine the orientation and rotation state of the task area;

[0013] The swarm element information of the drones includes parameters such as the swarm number, the takeoff positions of each drone, the individual detection range, and the flight speed; among them, the individual detection range is the field of view width for the drones to perform scanning detection.

[0014] Further, in step S2, the process of planning the initial coverage trajectory of the task area includes:

[0015] 1) Determine the main search direction as the main axis direction of the task area;

[0016] 2) Determine the starting search direction according to the orientation of the drone swarm relative to the task area;

[0017] 3) Determine the path spacing of the reciprocating path according to the width of the individual detection range of the drones;

[0018] 4) Plan a reciprocating path within the task area according to the determined main search direction, starting search direction, and path spacing to cover the entire task area as the initial coverage trajectory;

[0019] The reciprocating path is a sequence of path control points composed of a series of path control point coordinates.

[0020] Further, the step S3 includes:

[0021] Step S301: Obtain initialization parameters;

[0022] Including the initial search path C; the number of drones N d ; the takeoff positions B of each drone; the distance threshold ε; the maximum number of search iterations T;

[0023] The initial search path C is the initial coverage trajectory of the task area planned in step S2;

[0024] Step S302: Evenly divide the initial search path C into Nd Segment: Pair the trajectory segments with the UAVs. The i-th trajectory segment corresponds to the i-th UAV. Each trajectory segment includes a corresponding sequence of path control points.

[0025] Step S303: Calculate the initial total flight distance of each UAV. The total flight distance includes the preparatory trajectory distance and the mission trajectory distance. The preparatory trajectory distance is the distance from the takeoff position of the UAV to the starting point of the paired trajectory segment. The mission trajectory distance is the trajectory distance from the starting point to the ending point of the paired trajectory segment.

[0026] Step S304: Set the initial iteration count of the search to zero.

[0027] Step S305: Starting from the first iteration, perform iterative search for the split point position. In each iterative search, traverse from the first UAV. According to the difference in the total flight distances of adjacent UAVs, move the split point of the two adjacent trajectory segments assigned to the adjacent UAVs along the search path in the direction of reducing the difference in the total flight distances to obtain the search path for the next iteration.

[0028] Step S306: After all split points have been moved, determine whether the difference in the total flight distances between the first UAV and the last UAV is less than the distance threshold ε. If yes, stop the iteration and proceed to Step S307. If no, return to Step S305 for the next iteration until the maximum number of iterations.

[0029] Step S307: Output the trajectory segments with the split point positions adjusted as the area scanning trajectories of the paired UAVs.

[0030] Furthermore, in the initialization parameters in Step S301, the acceleration period τ, the acceleration ratio coefficient σ, and the non-acceleration ratio coefficient σ′ are also set, where σ′ < σ < 1.

[0031] In the iterative search in Step S305, when the iteration count satisfies t mod τ == 0, control the moving distance of the split point according to the set acceleration ratio coefficient.

[0032] The moving distance δ′ of the split point i = δ i * σ;

[0033] Conversely, control the moving distance of the split point according to the set non-acceleration ratio coefficient.

[0034] The moving distance δ′ of the split point i = δ i * σ′;

[0035] δ i is the difference in the total flight distances between the adjacent i-th UAV and the (i + 1)-th UAV in the t-th iterative search.

[0036] Further, the non-acceleration ratio coefficient σ′ = 0.5; acceleration ratio coefficient: 0.5 < σ < 1.

[0037] Further, the total flight distance L of each unmanned aerial vehicle i is calculated as follows. The two terms in the formula respectively represent the preliminary trajectory distance and the mission trajectory distance:

[0038]

[0039] In the above formula, B i is the take-off position of the unmanned aerial vehicle, and its complete search path is represented as kd i is the number of control points of the search path;

[0040] Calculate the total flight distance difference between adjacent unmanned aerial vehicles

[0041] The present invention also discloses a multi-unmanned aerial vehicle area scanning trajectory generation device, including: a first module, a second module, and a third module;

[0042] The first module is used to obtain the environmental element information of the mission area and the unmanned aerial vehicle cluster element information for performing the mission;

[0043] The second module is used to plan an initial coverage trajectory of the mission area according to the obtained element information. The initial coverage trajectory is a reciprocating path with a set path interval matching the width of the detection range of the individual unmanned aerial vehicle;

[0044] The third module is used to divide the initial coverage trajectory into trajectory segments with the same number as the unmanned aerial vehicles, and pair the trajectory segments with the unmanned aerial vehicles; adjust the position of the segmentation points of the trajectory segments, and search for the position of the segmentation points that makes the distance difference between the distances traveled by each unmanned aerial vehicle from the take-off position to the completion of the detection of the paired trajectory segments less than the set distance threshold; output the trajectory segments with the adjusted segmentation point positions as the area scanning trajectories of the paired unmanned aerial vehicles.

[0045] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. The feature is that when the processor executes the computer program, it implements the multi-unmanned aerial vehicle area scanning trajectory generation method as described above.

[0046] The present invention also discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the multi-unmanned aerial vehicle area scanning trajectory generation method as described above.

[0047] The present invention can achieve one of the following beneficial effects:

[0048] Reducing the execution time of the coverage reconnaissance mission: The multi-UAV area scanning trajectory generation method, device, equipment and storage medium disclosed in the present invention aim to generate multi-UAV scanning trajectories with the goal of reducing the final time consumption. By optimizing the preparatory trajectory and mission trajectory of the UAVs, all UAVs can complete the mission at approximately the same time, significantly reducing the total time to complete the mission.

[0049] Improving the coverage rate and scanning efficiency: While increasing the coverage rate of the target area, the scanning efficiency of the target area is also improved. By reasonably allocating trajectory segments and optimizing the position of the segmentation points, the workload of each UAV is balanced, avoiding repeated scanning and missed areas.

[0050] Enhancing the robustness of the system: Through online cooperative task allocation of multiple UAVs, even if individual UAVs fail, the overall mission can be guaranteed to be completed by reallocating the mission trajectory, improving the robustness and reliability of the system, reducing the dependence on manual operations, and increasing the degree of automation and efficiency of mission execution.

[0051] The present invention is applied to the multi-UAV cooperative area scanning mission, which not only improves the efficiency and effectiveness of mission execution, but also enhances the reliability and automation level of the system, and is suitable for high-efficiency reconnaissance missions in various complex environments. Brief Description of the Drawings

[0052] The drawings are only for the purpose of showing specific embodiments and are not considered to be a limitation of the present invention. Throughout the drawings, the same reference signs denote the same components.

[0053] Figure 1 It is a flowchart of the multi-UAV area scanning trajectory generation method in the embodiment of the present invention;

[0054] Figure 2 It is a schematic diagram of the initial coverage trajectory planned for a rectangular mission area in the embodiment of the present invention;

[0055] Figure 3 It is a flowchart of the coverage path iterative optimization algorithm in the embodiment of the present invention;

[0056] Figure 4 It is a verification diagram of the search movement trajectory planned for the UAV cluster in a rectangular area in the embodiment of the present invention;

[0057] Figure 5 It is a verification diagram of the search movement trajectory planned for the UAV cluster in a fan-shaped area in the embodiment of the present invention;

[0058] Figure 6 It is a schematic connection diagram of the composition of the multi-UAV area scanning trajectory generation device in the embodiment of the present invention. Detailed Description of the Specific Embodiment

[0059] The preferred embodiments of the present invention will be specifically described below in conjunction with the accompanying drawings, wherein the accompanying drawings form a part of this application and are used together with the embodiments of the present invention to explain the principles of the present invention.

[0060] An embodiment of the present invention discloses a method for generating a multi - UAV area scanning trajectory, as Figure 1 shown, including the following steps:

[0061] Step S1: Obtain the environmental element information of the mission area and the UAV cluster element information for performing the mission;

[0062] Step S2: According to the obtained element information, plan an initial coverage trajectory for the mission area, and the initial coverage trajectory is a reciprocating path with a set path interval matching the width of the detection range of each UAV;

[0063] Step S3: Divide the initial coverage trajectory into trajectory segments with the same number as the number of UAVs, and pair the trajectory segments with the UAVs; adjust the position of the segmentation points of the trajectory segments, and search for the position of the segmentation points that makes the difference in the travel lengths of each UAV from the take - off position to the completion of the detection of the paired trajectory segments less than the set distance threshold; output the trajectory segments with adjusted segmentation point positions as the area scanning trajectories of the paired UAVs.

[0064] Specifically, in step S1, the obtaining of the environmental element information of the mission area includes anchor points, dimensions, and the main axis direction;

[0065] The anchor points are used to determine the position of the mission area; the dimensions are used to determine the size of the mission area; the main axis direction is determined by the shape of the mission area and is used to determine the orientation and rotation state of the mission area.

[0066] Taking a rectangular or fan - shaped detection area as an example:

[0067] For a rectangular detection area, select the center point of the rectangular area as the anchor point, describe the dimensions with the length and width parameters of the rectangle, the main axis direction is the long - side direction of the rectangle, and use the counter - clockwise rotation angle around the anchor point in the main axis direction to describe the specific orientation of the detection area;

[0068] For a fan - shaped detection area, select the vertex of the fan as the anchor point, use the radius and opening angle of the fan as the dimension parameters, the main axis direction is the direction of the chord of the fan, and use the counter - clockwise rotation angle around the anchor point in the main axis direction to describe the specific orientation of the detection area.

[0069] The UAV cluster element information includes parameters such as the cluster number, the take - off positions of each UAV, the individual detection range, and the flight speed; among them, the individual detection range is the field - of - view width for the UAV to perform scanning detection.

[0070] Specifically, in step S2, the process of planning the initial coverage trajectory of the task area includes:

[0071] 1) Determine the main axis direction of the task area as the main search direction;

[0072] 2) Determine the starting search direction according to the orientation of the UAV cluster relative to the task area;

[0073] 3) Determine the path spacing of the reciprocating path according to the detection range width of each UAV;

[0074] 4) Plan a reciprocating path in the task area according to the determined main search direction, starting search direction and path spacing to cover the entire task area as the initial coverage trajectory;

[0075] The reciprocating path is a sequence of path control points composed of a series of path control point coordinates.

[0076] The reciprocating path determined by this step reduces the number of UAV turns. Since the UAV undergoes a process of deceleration, turning, and then acceleration when turning, reducing the number of turns means reducing the coverage search time in the area, which can improve the coverage search efficiency and reduce energy waste.

[0077] As Figure 2 shown, it is the initial coverage trajectory planned for a rectangular task area; in the figure, the main axis direction of the rectangle is used as the main search direction, and according to the relative orientation of the UAV takeoff airport and the task area, the starting search direction is determined to be from left to right.

[0078] Specifically, in step S3, it includes:

[0079] Step S301, obtain initialization parameters;

[0080] It includes the initial search path C; the number of UAVs N d ; the takeoff position B of each UAV; the distance threshold ε; the maximum search iteration number T;

[0081] The initial search path C is the initial coverage trajectory of the task area planned in step S2;

[0082] Step S302, evenly divide the initial search path C into N d segments: perform pairing of the trajectory segments and the UAVs, and the i-th trajectory segment corresponds to the i-th UAV; each trajectory segment includes the corresponding sequence of path control points;

[0083] Step S303: Calculate the initial total flight distance of each UAV; the total flight distance includes the preparatory trajectory distance and the mission trajectory distance; the preparatory trajectory distance is the distance from the take-off position of the UAV to the starting point of the paired trajectory segment; the mission trajectory distance is the trajectory distance from the starting point to the end point of the paired trajectory segment;

[0084] Among them, the total flight distance L of each UAV i is calculated as follows, where the two terms in the formula represent the preparatory trajectory distance and the mission trajectory distance respectively:

[0085]

[0086] In the above formula, B i is the take-off position of the UAV, and its complete search path is expressed as kd i is the number of control points of the search path;

[0087] Calculate the total flight distance difference between adjacent UAVs

[0088] Step S304: Set the initial iteration count of the search to zero, t = 0;

[0089] Step S305: Starting from the first iteration, perform iterative search for the split point position; in each iterative search, start traversing from the first UAV, and according to the total flight distance difference between adjacent UAVs, move the split point of the two adjacent trajectory segments assigned to the adjacent UAVs along the search path in the direction of reducing the total flight distance difference to obtain the search path for the next iteration;

[0090] Total flight distance difference between adjacent UAVs

[0091] Step S306: After all split points are moved, determine whether the total flight distance difference between the first UAV and the last UAV is less than the distance threshold ε; if yes, stop the iteration and enter Step S307; if not, return to Step S305 for the next iteration until the maximum number of iterations;

[0092] Step S307: Output the trajectory segment after adjusting the split point position as the area scanning trajectory of the paired UAVs.

[0093] In the preferred solution,

[0094] In the initialization parameters in Step S301, the acceleration period τ, the acceleration ratio coefficient σ, and the non-acceleration ratio coefficient σ′ are also set; σ′ < σ < 1;

[0095] In the iterative search in step S305, when the number of iterations satisfies: t mod τ == 0, the moving distance of the segmentation point is controlled according to the set acceleration ratio coefficient;

[0096] The moving distance δ' of the segmentation point i = δ i * σ;

[0097] Conversely, the moving distance of the segmentation point is controlled according to the set non-acceleration ratio coefficient;

[0098] The moving distance δ' of the segmentation point i = δ i * σ'.

[0099] Preferably, the non-acceleration ratio coefficient σ' = 0.5; the acceleration ratio coefficient: 0.5 < σ < 1.

[0100] By performing an accelerated search to expand the adjustment distance of the segmentation point position once in each search acceleration cycle, the search time can be shortened and the search efficiency can be improved.

[0101] In this embodiment, a specific search path allocation algorithm for step S3 is also given, as shown in the following table: The algorithm flowchart is as Figure 3 shown.

[0102]

[0103] The main process of this algorithm is as follows:

[0104] ① Input data such as the path C obtained by the initial search, the number N of drones d , the drone takeoff position distance threshold ε, the algorithm acceleration cycle τ, the algorithm acceleration ratio σ, the maximum number of iterations T, etc. into the algorithm.

[0105] ② Divide the initial search path C evenly into N d segments to obtain the initial search path of the drones (line 1)

[0106] ③ Calculate the initial total flight distance of each drone according to equation (1) (line 2)

[0107] ④ Set the number of iterations t to 0 (line 3)

[0108] ⑤ When the number of iterations t is less than the maximum number of iterations T, traverse i from 1 to N - 1; output the result if it is greater than or equal to the number of iterations (lines 4 - 5)

[0109] ⑥ Calculate the total flight distance difference between adjacent drones (line 6)

[0110] ⑦ When t mod τ == 0, set the moving distance δ′ i = δ i *σ (lines 7 - 8)

[0111] ⑧ Otherwise, set the moving distance δ′ i = δ i *0.5 (lines 9 - 10)

[0112] ⑨ Move the search starting point along the initial search path C by δ′ i , to obtain a new search path and (line 12)

[0113] ⑩ If holds, then output the result (lines 14 - 15)

[0114] If it does not hold, then set t = t + 1 and perform the next round of iteration. (line 17)

[0115] This algorithm first evenly divides the initial search path as the initial assignment of the scanning task. Since the distances from the initial point of the UAV to the starting point of the scanning task are not the same, the final time consumption of the tasks under the initial assignment is not optimal. Therefore, it is necessary to adjust the search path division points to make the total path lengths of each UAV as equal as possible, thereby minimizing the total task time consumption. Specifically, it can be optimized through an iterative algorithm. In each round of iteration, calculate the difference in the total flight distances of adjacent - numbered UAVs, and adjust the search path division points according to the magnitude of the distance difference, so that the total flight distances of adjacent - numbered UAVs are closer, until convergence to the equal total flight distances of each UAV. Generally, the moving distance of the search path division point is set to 0.5 times the distance difference. In order to accelerate the convergence speed of the algorithm, after each round of iteration of the acceleration cycle, the moving distance is increased to σ times the distance difference, where 0.5 < σ < 1. When the distance difference is small enough, or the number of iteration rounds reaches the pre - set maximum number of iteration times, the iteration stops, thereby obtaining the final complete search path of each UAV

[0116] Verify the algorithm within rectangular and fan - shaped search areas, respectively set the specific parameters of the UAV cluster and the target detection area, plan the search movement trajectories for the UAV cluster according to the cluster cooperative detection algorithm, and obtain the search movement trajectories of the UAV cluster as shown in Figure 4 and Figure 5 ;

[0117] Through Figure 4 and Figure 5The results can verify that for different combinations of specific parameters for the UAV cluster and the target detection area, according to the cluster cooperative detection algorithm, search movement trajectories can be planned for the UAV cluster, and according to the final detection and search results, the coverage rate of the target area is nearly 1.

[0118] In summary, the multi-UAV area scanning trajectory generation method of this embodiment aims to generate multi-UAV scanning trajectories with the goal of reducing the final time consumption; an iterative optimization algorithm that simultaneously considers the flight trajectory from the UAV takeoff position to the target position (preparatory trajectory) and the flight trajectory assigned for the execution of the area scanning task (task trajectory) is proposed. All UAVs can complete the task at approximately the same time, which can reduce the final time consumption for completing the task; while improving the coverage rate of the target area, the scanning efficiency of the target area is also improved.

[0119] Embodiment 2

[0120] Another embodiment of the present invention discloses a multi-UAV area scanning trajectory generation device, as Figure 6 shown, including: a first module, a second module, and a third module;

[0121] The first module is used to obtain the environmental element information of the task area and the UAV cluster element information for executing the task;

[0122] The second module is used to plan an initial coverage trajectory for the task area according to the obtained element information. The initial coverage trajectory is a reciprocating path with a set path interval matching the width of the individual UAV detection range;

[0123] The third module is used to divide the initial coverage trajectory into trajectory segments with the same number as the UAVs, and pair the trajectory segments with the UAVs; adjust the position of the segmentation points of the trajectory segments, and search for the position of the segmentation points that makes the difference in the travel lengths of each UAV from the takeoff position to the completion of the detection of the paired trajectory segments less than the set distance threshold; output the trajectory segments after adjusting the position of the segmentation points as the area scanning trajectories of the paired UAVs.

[0124] The specific technical details and beneficial effects in this embodiment are the same as those in Embodiment 1. Please refer to them specifically and will not be elaborated here one by one.

[0125] Embodiment 3

[0126] An embodiment of the present invention discloses an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor.

[0127] In one example, the above-mentioned processor may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present application.

[0128] The memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to execute the multi-UAV area scanning trajectory generation method according to Embodiment 1.

[0129] The processor runs a computer program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the multi-UAV area scanning trajectory generation method in Embodiment 1.

[0130] In one example, a communication interface and a bus may also be included.

[0131] Among them, the memory, the processor, and the communication interface are connected through the bus and complete communication with each other.

[0132] The communication interface is mainly used to implement communication between each module, device, unit, and / or device in the embodiments of the present application. The input device and / or output device may also be accessed through the communication interface.

[0133] A bus includes hardware, software, or both, and components of an electronic device are coupled to each other. By way of example and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-E) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable bus or a combination of two or more of these. Where appropriate, a bus may include one or more buses. Although embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0134] Embodiment 4

[0135] An embodiment of the present invention discloses a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the multi-UAV area scanning trajectory generation method in Embodiment 1 can be implemented and the same technical effects can be achieved. To avoid repetition, details are not described herein again. Among them, the above computer-readable storage medium may include a non-transitory computer-readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disc, etc., which is not limited herein.

[0136] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

[0137] As described above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any change or replacement that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for generating multi-UAV area scanning trajectories, characterized in that: include: Step S1, obtaining environmental factor information of the mission area and factor information of the drone cluster performing the mission; Step S2: planning an initial coverage trajectory of the mission area based on the acquired element information, wherein the initial coverage trajectory is a reciprocating path with a set path interval matching the width of the individual detection range of the drone; Step S3, dividing the initial coverage trajectory into trajectory segments equal to the number of UAVs, and pairing the trajectory segments with the UAVs; adjusting the segmentation point positions of the trajectory segments, searching for segmentation point positions that make the difference in the distance length of each UAV from the take-off position to the completion of paired trajectory segment detection less than a set distance threshold; outputting the trajectory segments after the segmentation point positions are adjusted as the regional scanning trajectory of the paired UAVs.

2. The method for generating multi-UAV area scanning trajectories according to claim 1, characterized in that: include: The environmental element information of the task area obtained includes anchor point, size and main axis direction; The anchor point is used to determine the position of the task area; the size is used to determine the size of the task area; the main axis direction is determined by the shape of the task area and is used to determine the orientation and rotation state of the task area; The cluster element information of the drones includes parameters including the number of clusters, the take-off position of each drone, the individual detection range and the flight speed; among which, the individual detection range is the width of the field of view of the drone for scanning and detection.

3. The method for generating multi-UAV area scanning trajectories according to claim 2, characterized in that: include: In step S2, the process of planning the initial coverage trajectory of the mission area includes: 1) Determine the main axis direction of the task area as the main search direction; 2) Determine the starting search direction based on the orientation of the drone cluster relative to the mission area; 3) Determine the path spacing of the reciprocating path based on the width of the individual detection range of the drone; 4) Planning a reciprocating path in the mission area according to the determined main search direction, starting search direction and path spacing, covering the entire mission area as the initial coverage trajectory; The reciprocating path is a path control point sequence composed of a series of path control point coordinates.

4. The method for generating multi-UAV area scanning trajectories according to claim 3, characterized in that: include: The step S3 comprises: Step S301, obtaining initialization parameters; Including the initial search path C; the number of drones N d ; Take-off position B of each drone; Distance threshold ε; Maximum number of search iterations T; The initial search path C is the initial coverage trajectory of the task area planned in step S2; Step S302: Divide the initial search path C into N d Segment: Pair the trajectory segment with the UAV, the i-th trajectory segment corresponds to the i-th UAV; each trajectory segment includes the corresponding path control point sequence; Step S303, calculating the total initial flight distance of each UAV; the total flight distance includes the preparation track distance and the task track distance; the preparation track distance is the distance from the take-off position of the UAV to the starting point of the paired track segment; the task track distance is the track distance from the starting point to the end point of the paired track segment; Step S304, setting the initial iteration count of the search to zero; Step S305, starting from the first iteration, performing iterative search of the segmentation point position; in each iterative search, starting from the first UAV, according to the total flight distance difference of the adjacent UAVs, the segmentation points of the two adjacent trajectory segments assigned to the adjacent UAVs are moved along the search path in a direction of reducing the total flight distance difference, to obtain the search path for the next iteration; Step S306: After all segmentation points have moved, determine whether the total flight distance difference between the first UAV and the last UAV is less than the distance threshold ε; if yes, stop the iteration and proceed to step S307; if no, return to step S305 for the next iteration until the maximum number of iterations is reached; Step S307: output the trajectory segment after the segmentation point position is adjusted as the area scanning trajectory of the paired UAV.

5. The method for generating multi-UAV area scanning trajectories according to claim 4, characterized in that: include: The initialization parameters in step S301 also set: acceleration period τ; Speedup coefficient σ; non-speedup coefficient σ′; σ′<σ<1; In the iterative search in step S305, when the number of iterations satisfies: t modτ == 0, the moving distance of the segmentation point is controlled according to the set acceleration coefficient; The moving distance of the split point δ′ i =δ i *σ; On the contrary, the moving distance of the split point is controlled according to the set non-acceleration ratio coefficient; The moving distance of the split point δ′ i =δ i *σ′; δ i is the total flight distance difference between the adjacent i-th UAV and the i+1-th UAV in the t-th iteration search.

6. The method for generating multi-UAV area scanning trajectories according to claim 5, characterized in that: include: Non-acceleration coefficient σ′ = 0.5; Speedup ratio coefficient: 0.5<σ<1.

7. The method for generating multi-UAV area scanning trajectories according to claim 5, characterized in that: include: The total flight distance of each drone is L i The calculation is as follows, where the two terms represent the preparation trajectory distance and the task trajectory distance respectively: In the above formula, B i is the take-off position of the UAV, and its complete search path is expressed as kd i is the number of control points in the search path; Calculate the total flight distance difference between adjacent drones 8. A device for generating a multi-UAV area scanning trajectory, characterized in that: include: Module 1, Module 2 and Module 3; The first module is used to obtain environmental element information of the mission area and element information of the drone cluster performing the mission; The second module is used to plan an initial coverage trajectory of the mission area based on the acquired element information, wherein the initial coverage trajectory is a reciprocating path with a set path interval matching the width of the individual detection range of the drone; The third module is used to divide the initial coverage trajectory into trajectory segments with the same number of drones, and pair the trajectory segments with the drones; adjust the segmentation point positions of the trajectory segments, search for the segmentation point positions that make the difference in the distance length of each drone from the take-off position to the completion of the paired trajectory segment detection less than a set distance threshold; and output the trajectory segments after the segmentation point positions are adjusted as the area scanning trajectory of the paired drones.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for generating multi-UAV area scanning trajectories according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for generating multi-UAV area scanning trajectories as described in any one of claims 1 to 7 is implemented.

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