Multi-unmanned aerial vehicle target allocation and path planning method for executing multiple regional tasks

Abstract

The invention relates to a multi-unmanned-aerial-vehicle target distribution and path planning method for executing multiple regional tasks, and the method comprises the steps: obtaining the element information of multiple unmanned aerial vehicles, and the environment element information of multiple regional target tasks needing to be executed; determining a corresponding unmanned aerial vehicle number demand according to the size of the target task of each region in the acquired element information; an MVRP problem with the minimization of the total path distance and the minimization of the total cost as targets is constructed, and the unmanned aerial vehicle is allocated to each region target task; path planning is carried out, and a complete path from the takeoff position to a scanning path starting point for scanning survey in the distributed area target task and then to a scanning path ending point is planned for each unmanned aerial vehicle; the unmanned aerial vehicles executing the scanning tasks in the target tasks in each area can complete the respective tasks at the same time, and the total time for completing the tasks is shortened. According to the method, the execution efficiency of the multi-region task targets is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-target allocation and path planning, and in particular to a multi-UAV target allocation and path planning method for performing multi-area tasks. Background Art

[0002] In practical applications, multiple drones need to perform tasks of multiple regional target types at the same time. In order to improve task completion and execution efficiency, it is necessary to comprehensively consider task requirements, realize reasonable task allocation and adjustment, and effectively perform task allocation in multi-drone and multi-target regional collaborative work. This brings challenges to the traditional task allocation method in terms of task complexity.

[0003] For multi-UAV regional collaborative scanning tasks, it is necessary to conduct collaborative scanning of multiple UAVs in the area to achieve complete scanning coverage of the area. However, the final time consumption of the multi-UAV regional scanning task is limited by the UAV that completes the task last. Ideally, when each UAV adopts a greedy approach and the task arrangement is reasonable, all UAVs can complete the task at the same time, thus achieving the shortest total time consumption. However, since the distances between the UAVs' take-off positions and the scanning task area are different, which results in different times for them to arrive at the scanning starting point, the conventional method of equally dividing the task scanning trajectories in the task area cannot meet this goal. Summary of the invention

[0004] In view of the above analysis, the present invention aims to disclose a multi-UAV target allocation and path planning method for performing multi-area tasks; to solve the problem of multi-UAV multi-area target allocation and UAV scanning path planning.

[0005] The present invention discloses a method for allocating and planning paths of multiple unmanned aerial vehicles (UAVs) for performing multiple regional tasks, comprising:

[0006] Step S1, obtaining multiple drone element information and environmental element information of multiple regional target tasks to be performed;

[0007] Step S2: Determine the corresponding number of drones required according to the size of each regional target task in the acquired element information; and construct an MVRP problem with the goal of minimizing the total path distance and minimizing the total cost, and allocate drones to each regional target task;

[0008] Step S3: perform path planning, and plan a complete path for each UAV from the take-off position to the starting point of the scanning path for scanning and surveying in the assigned regional target task, and then to the end point of the scanning path; so that the UAVs performing scanning tasks in each regional target task can complete their respective tasks at the same time, shortening the total time to complete the task.

[0009] Further, in step S1, the obtaining of the environmental element information of the task area includes an anchor point, an area, and a main axis direction;

[0010] The anchor point is used to determine the position of the task area; the area 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;

[0011] The multi-UAV element information includes parameters such as the number of UAVs, 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.

[0012] Further, in step S2, it includes:

[0013] Step S201: According to the take-off positions of each UAV and the positions of each task area, calculate the distances between each UAV and each task, the distances between tasks, and the task equivalent distance, and establish a distance matrix;

[0014] Step S202: Determine the number of UAVs allocated to each task according to the area ratio of the target tasks in each area; and construct an MVRP problem with the goal of minimizing the total path distance and minimizing the total cost;

[0015] Step S203: Solve the established MVRP problem; with the distance matrix as the input, solve the MVRP problem and allocate the corresponding UAVs to each task.

[0016] Further, in the distance matrix established in step S201,

[0017] The distance between the UAV and the task is the Euclidean distance between the take-off position of the UAV and the anchor point of the task area;

[0018] The distance between tasks is the Euclidean distance between the anchor points of two task areas;

[0019] The task equivalent distance is the area of the region divided by the field-of-view width of the UAV.

[0020] Further, in step S202, allocate UAVs according to the area ratio of the target tasks in each area; allocate more UAVs to the target with a larger area of the region to improve the completion efficiency of the overall task;

[0021] The MVRP mathematical model constructed with the goal of minimizing the total path distance and minimizing the total cost is described as:

[0022]

[0023] Among them, \(T = \{1, 2, \ldots, m\}\) represents the task set, where \(m\) is the number of tasks; \(X = \{1, 2, \ldots, u\}\) represents the UAV set, where \(u\) is the number of UAVs; \(i\) and \(j\) are the task numbers in the task set, and \(i\neq j\); \(k\) is the UAV number in the UAV set.

[0024] c ij is the distance from task \(i\) to task \(j\).

[0025] γ ij is the equivalent path length of the task from task \(i\) to task \(j\).

[0026] indicates whether the path from task \(i\) to task \(j\) is selected by UAV \(k\), and the value is \(\{0, 1\}\).

[0027] f 1 is the weight factor of the flight distance; \(f\) 2 is the weight factor of the equivalent path.

[0028] r i is the number of times task \(i\) needs to be visited.

[0029] Furthermore, the number of times \(r\) that task \(i\) needs to be visited i is determined as follows:

[0030] 1) According to the number of UAVs \(u\) and the area \(A\) of the regional target i an integer programming problem is constructed, and the number of UAVs required for each regional target task is obtained through the first solution.

[0031] The number of times \(r\) that the regional target \(i\) needs to be visited obtained through the first solution i is:

[0032]

[0033] A total is the sum of the areas of all regional target tasks.

[0034] 2) According to the sum of the number of times each regional target task is visited obtained through the first solution being \(u\) or \(u - 1\), it is judged whether to adjust the number of times of being visited.

[0035] If it is \(u\), the number of times of being visited of the planned regional target task is not adjusted; if it is \(u - 1\), the number of times of being visited of the regional target with the largest planned area is increased by 1 to ensure that all UAVs participate in the target allocation.

[0036] Furthermore, in step S3, the path planning process of the UAVs performing the regional target tasks includes:

[0037] Step S301: According to the element information obtained in step S1, plan an initial coverage trajectory for the mission area. The initial coverage trajectory is a reciprocating path with a set path interval that matches the width of the detection range of the individual UAVs.

[0038] Step S302: Divide the initial coverage trajectory into trajectory segments equal in number to 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 travel lengths of each UAV from the take-off position to the completion of the detection of the paired trajectory segments the same. Output the trajectory segments with adjusted segmentation point positions as the area scanning paths for the paired UAVs.

[0039] Further, in step S301, the process of planning the initial coverage trajectory for the mission area includes:

[0040] 1) Determine the main axis direction of the mission area as the main search direction.

[0041] 2) Determine the starting search direction according to the orientation of the assigned UAV take-off position relative to the mission area.

[0042] 3) Determine the path spacing of the reciprocating path according to the width of the detection range of the individual UAVs.

[0043] 4) Plan a reciprocating path within the mission area according to the determined main search direction, starting search direction, and path spacing to cover the entire mission area as the initial coverage trajectory.

[0044] The reciprocating path is a sequence of path control point coordinates consisting of a series of path control points.

[0045] Further, in step S302, the process of adjusting the position of the segmentation points of the trajectory segments includes:

[0046] Step S302-1: Obtain the initialization parameters.

[0047] Including the initial search path C; the number of UAVs N d ; the take-off position B of each UAV; the distance threshold ε; the maximum number of search iterations T;

[0048] The initial search path C is the initial coverage trajectory of the mission area.

[0049] Step S302-2: Divide the initial search path C into N d segments; perform pairing of the trajectory segments with the UAVs, where the i-th trajectory segment corresponds to the i-th UAV; each trajectory segment includes the corresponding sequence of path control points.

[0050] Step S302-3: 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;

[0051] Step S302-4: Set the initial iteration count of the search to zero;

[0052] Step S302-5: Starting from the first iteration, perform iterative search for the split point position; in each iterative search, traverse from the first UAV, and according to the total flight distance difference between adjacent UAVs, move the split point of the adjacent two 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;

[0053] Step S302-6: 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 S302-7; if no, return to Step S302-5 for the next iteration until the maximum number of iterations;

[0054] Step S302-7: Output the trajectory segments after adjusting the split point positions as the area scanning paths of the paired UAVs.

[0055] Furthermore, in the initialization parameters in Step S301, the following are also set: acceleration period τ; acceleration ratio coefficient σ; non-acceleration ratio coefficient σ′; σ′ < σ < 1;

[0056] In the iterative search in Step S305, when the iteration number t satisfies: t mod τ == 0, control the moving distance of the split point according to the set acceleration ratio coefficient;

[0057] The moving distance δ′ of the split point i = δ i * σ;

[0058] Conversely, control the moving distance of the split point according to the set non-acceleration ratio coefficient;

[0059] The moving distance δ′ of the split point i = δ i * σ′;

[0060] δ i is the total flight distance difference between the adjacent i-th UAV and the (i + 1)-th UAV in the t-th iterative search.

[0061] One of the beneficial effects that the present invention can achieve is as follows:

[0062] The multi - UAV target allocation and path planning method for executing multiple - area tasks disclosed in the present invention adopts an automated task - allocation process during target allocation, adapts to the complex and changeable actual environment, realizes the UAV target allocation by combining the distance from the UAV to the task area and the size of the task area, reduces manual intervention, and lowers the possibility of human errors.

[0063] Moreover, the present invention aims to reduce the final time consumption to generate the multi - UAV scanning trajectories; an iterative optimization algorithm is proposed that simultaneously considers the flight trajectory from the UAV take - off position to the target position (preparatory trajectory) and the flight trajectory assigned for executing the area - scanning task (task trajectory). All UAVs completing the task at approximately the same time can reduce the final time consumption for task completion; while improving the coverage rate of the target area, the scanning efficiency of the target area is also improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The drawings are only for the purpose of showing specific embodiments and are not considered as limitations to the present invention. Throughout the drawings, the same reference signs represent the same components.

[0065] Figure 1 It is a flowchart of the multi - UAV target allocation and path planning method for executing multiple - area tasks in the embodiments of the present invention;

[0066] Figure 2 It is a flowchart of the multi - UAV target allocation in the embodiments of the present invention;

[0067] Figure 3 It is a flowchart of the UAV path planning in the embodiments of the present invention;

[0068] Figure 4 It is a schematic diagram of the initial coverage trajectory planned for a rectangular task area in the embodiments of the present invention;

[0069] Figure 5 It is a flowchart of the coverage - path iterative optimization algorithm in the embodiments of the present invention;

[0070] Figure 6 It is a verification diagram of the multi - UAV planned search movement trajectory in a rectangular area in the embodiments of the present invention;

[0071] Figure 7 It is a verification diagram of the multi - UAV planned search movement trajectory in a fan - shaped area in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] The following will specifically describe the preferred embodiments of the present invention in conjunction with the drawings, where the 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.

[0073] An embodiment of the present invention discloses a multi-UAV target allocation and path planning method for executing multiple regional tasks, as follows Figure 1 shown, including:

[0074] Step S1, obtain the information of multi-UAV elements and the environmental element information of multiple regional target tasks to be executed;

[0075] Step S2, determine the corresponding UAV quantity requirements according to the sizes of the regional target tasks in the obtained element information; and construct an MVRP problem with the goal of minimizing the total path distance and minimizing the total cost, and allocate UAVs to each regional target task;

[0076] Step S3, perform path planning, plan a complete path for each UAV from the take-off position to the starting point of the scanning path for scanning and surveying in the allocated regional target task, and then to the end point of the scanning path; enable the UAVs performing the scanning tasks in each regional target task to complete their respective tasks at the same time, and shorten the total time to complete the tasks.

[0077] Specifically, in the scanning tasks of each regional target task in step S3, first establish an initial coverage trajectory; then divide the initial scanning coverage trajectory into trajectory segments that are one-to-one matched with the allocated UAVs; and search and adjust the positions of the segmentation points of the trajectory segments to find the positions of the segmentation points that make the distances of each UAV from the take-off position to the completion of the scanning of the matched trajectory segments the same; output the trajectory segments with the adjusted segmentation points as the scanning paths of the matched UAVs.

[0078] Specifically, in step S1, the obtaining of the environmental element information of the task area includes anchor points, area, and main axis direction;

[0079] The anchor points are used to determine the position of the task area; the area is used to determine the size of the task area and can be used for subsequent determination of UAV quantity requirements; 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;

[0080] Taking a rectangular or fan-shaped detection area as an example:

[0081] For a rectangular detection area, select the center point of the rectangular area as the anchor point, describe the size with the length and width parameters of the rectangle, the main axis direction is the long side direction of the rectangle, and describe the specific orientation of the detection area with the counterclockwise rotation angle of the main axis direction around the anchor point;

[0082] 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 size parameters, the main axis direction is the direction of the chord of the fan, and describe the specific orientation of the detection area with the counterclockwise rotation angle of the main axis direction around the anchor point;

[0083] The multi-UAV element information includes parameters such as the number of UAVs, 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.

[0084] Specifically, as Figure 2 shown, step S2 includes:

[0085] Step S201: Calculate the distances between each UAV and each task, the distances between tasks, and the task equivalent distances according to the take-off positions of each UAV and the positions of each task area, and establish a distance matrix;

[0086] Step S202: Determine the number of UAVs allocated to each task according to the area ratio of the target tasks in each area; and construct an MVRP problem with the goal of minimizing the total path distance and minimizing the total cost;

[0087] Step S203: Solve the established MVRP problem; use the distance matrix as the input to solve the MVRP problem and allocate the corresponding UAVs to each task.

[0088] Specifically, in the distance matrix established in step S201,

[0089] The distance between the UAV and the task is the Euclidean distance between the take-off position of the UAV and the anchor point of the task area;

[0090] The distance between tasks is the Euclidean distance between the anchor points of two task areas;

[0091] The task equivalent distance is the area of the area divided by the field-of-view width of the UAV.

[0092] Specifically, in step S202, allocate UAVs according to the area ratio of the target tasks in each area; allocate more UAVs to the target with a larger area to improve the completion efficiency of the overall task;

[0093] The MVRP mathematical model constructed with the goal of minimizing the total path distance and minimizing the total cost is described as:

[0094]

[0095] Objective function (1) represents minimizing the total travel distance and minimizing the total cost;

[0096] Constraint (2) means that each task must be visited and visited only once;

[0097] Constraint (3) means that the path of each UAV must start from the starting point and end at the ending point.

[0098] Among them, T = {1, 2, …, m} represents the task set, where m is the number of tasks; X = {1, 2, …, u} represents the UAV set, where u is the number of UAVs; i and j are the task numbers in the task set, and i ≠ j; k is the UAV number in the UAV set.

[0099] c ij is the distance from task i to task j, and the distance between tasks is obtained according to the distance calculated in the preprocessing of step S201.

[0100] γ ij is the equivalent path length of the task from task i to task j; the equivalent path length of the task is obtained according to the distance calculated in the preprocessing of step S201.

[0101] indicates whether the path from task i to task j is selected by UAV k, and the value is {0, 1}.

[0102] f 1 is the weight factor of the flight distance; f 2 is the weight factor of the equivalent path.

[0103] r i is the number of times task i needs to be visited.

[0104] The number of times r that task i needs to be visited i is determined as follows:

[0105] 1) According to the number of UAVs u and the area A of the regional target i construct an integer programming problem, and initially solve to obtain the UAV quantity requirements for each regional target task.

[0106] The number of times r that the initially solved regional target i needs to be visited i is:

[0107]

[0108] where A total is the sum of the areas of all regional target tasks.

[0109] 2) According to the sum of the number of times each regional target task is visited obtained from the initial solution being u or u - 1, determine whether to adjust the number of times visited.

[0110] If it is u, then do not adjust the number of times the planned regional target task is visited; if it is u - 1, then add 1 to the number of times the planned regional target with the largest area is visited to ensure that all UAVs participate in the target allocation.

[0111] Specifically, the solution process in step S203 includes:

[0112] 1) According to the established distance matrix, convert the objective function and constraints in the established MVRP problem, as well as relevant parameters including the number of times a task is visited and time windows, into problem parameters in the data structure required by the planning solver.

[0113] 2) Select an optimization algorithm for handling the MVRP problem in the planning solver.

[0114] Select a suitable optimization algorithm according to the actual situation. For example, use the integer programming algorithm to handle the MVRP problem.

[0115] 3) Use the API interface to pass the problem parameters into the solving function in the planning solver for solving to obtain the optimal planning result of task assignment to drones, and obtain an optimal task assignment plan with the goal of minimizing the total path distance and minimizing the total cost.

[0116] Specifically, as Figure 3 shown, in step S3, the path planning process of the drone executing the regional target task includes:

[0117] Step S301: According to the element information obtained in step S1, plan an initial coverage trajectory for the task area. The initial coverage trajectory is a reciprocating path with a set path interval matching the width of the individual detection range of the drone.

[0118] Step S302: Divide the initial coverage trajectory into trajectory segments with the same number as the drones, and pair the trajectory segments with the drones; adjust the position of the segmentation points of the trajectory segments, and search for the position of the segmentation points that makes the travel lengths of each drone from the takeoff position to the completion of the paired trajectory segment detection the same; output the trajectory segments after adjusting the segmentation point positions as the area scanning paths of the paired drones.

[0119] Specifically, in step S301, the process of planning the initial coverage trajectory for the task area includes:

[0120] 1) Determine the main axis direction of the task area as the main search direction.

[0121] 2) Determine the starting search direction according to the orientation of the assigned drone takeoff position relative to the task area.

[0122] 3) Determine the path spacing of the reciprocating path according to the width of the individual detection range of the drone.

[0123] 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.

[0124] The reciprocating path is a sequence of path control point coordinates composed of a series of path control points.

[0125] The reciprocating path determined in this step reduces the number of turns of the UAV. Since the UAV decelerates, executes a turn, and then accelerates when turning, reducing the number of turns means reducing the time for coverage search in the area, which can improve the efficiency of coverage search and reduce energy waste.

[0126] As Figure 4 shown, it is the initial coverage trajectory planned for a rectangular mission area; in the figure, the main axis direction of the rectangle is the main search direction, and according to the orientation of the multi-UAV relative to the mission area, the starting search direction is determined to be from left to right. In the figure, the relative orientation between the UAV takeoff airport and the mission area is used as the orientation of the multi-UAV relative to the mission area.

[0127] The step S302 includes:

[0128] Step S302-1: Obtain initialization parameters;

[0129] Including 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;

[0130] The initial search path C is the initial coverage trajectory of the mission area planned in step S301;

[0131] Step S302-2: Divide the initial search path C into N d segments; perform pairing of the trajectory segments and the UAVs, where the i-th trajectory segment corresponds to the i-th UAV; each trajectory segment includes a corresponding sequence of path control points;

[0132] Step S302-3: 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 end point of the paired trajectory segment.

[0133] Among them, the total flight distance L i of each UAV is calculated as follows, and the two terms in the formula represent the preparatory trajectory distance and the mission trajectory distance respectively:

[0134]

[0135] In the above formula, B i is the takeoff position of the UAV, and its complete search path is expressed as kd i is the number of search path control points;

[0136] Calculate the total flight distance difference between adjacent UAVs

[0137] Step S302-4: Set the initial iteration count of the search to zero, t = 0;

[0138] Step S302-5: Starting from the first iteration, perform an iterative search for the split point position; in each iterative search, traverse 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;

[0139] Total flight distance difference between adjacent UAVs

[0140] Step S302-6: After all split points have been 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 S302-7; if no, return to Step S302-5 for the next iteration until the maximum number of iterations;

[0141] Step S302-7: Output the trajectory segments with adjusted split point positions as the area scanning paths for the paired UAVs.

[0142] In the preferred solution,

[0143] In Step S302-1, the initialization parameters are also set: acceleration period τ; acceleration ratio coefficient σ; non-acceleration ratio coefficient σ′; σ′ < σ < 1;

[0144] In the iterative search in Step S302-5, when the iteration count t satisfies: t mod τ == 0, control the moving distance of the split point according to the set acceleration ratio coefficient;

[0145] Moving distance of the split point δ′ i = δ i * σ;

[0146] Otherwise, control the moving distance of the split point according to the set non-acceleration ratio coefficient;

[0147] Moving distance of the split point δ′ i = δ i * σ′.

[0148] Preferably, the non-acceleration ratio coefficient σ′ = 0.5; acceleration ratio coefficient: 0.5 < σ < 1.

[0149] By performing an accelerated search to expand the distance of split point position adjustment once in each search acceleration period, the search time can be shortened and the search efficiency can be improved.

[0150] In this embodiment, a search path allocation algorithm for the specific implementation step S302 is also given as shown in the following table: The algorithm flow chart is as Figure 5 shown.

[0151]

[0152]

[0153] The main process of this algorithm is as follows:

[0154] ① Input data such as the path C obtained by the initial search, the number N of drones, d the distance threshold ε between the take-off positions of the drones, the algorithm acceleration period τ, the algorithm acceleration ratio σ, and the maximum number of iterations T into the algorithm.

[0155] ② Divide the initial search path C into N d segments on average to obtain the initial search paths of the drones (Line 1)

[0156] ③ Calculate the total initial flight distance of each drone according to Equation (1) (Line 2)

[0157] ④ Set the number of iterations t to 0 (Line 3)

[0158] ⑤ When the number of iterations t is less than the maximum number of iterations T, traverse i from 1 to N - 1; if it is greater than or equal to the number of iterations, output the result (Lines 4 - 5)

[0159] ⑥ Calculate the total flight distance difference between adjacent drones (Line 6)

[0160] ⑦ When t mod τ == 0, set the moving distance δ′ i = δ i * σ (Lines 7 - 8)

[0161] ⑧ Otherwise, set the moving distance δ′ i = δ i * 0.5 (Lines 9 - 10)

[0162] ⑨ Move the search starting point along the initial search path C by δ′ i to obtain a new search path and (Line 12)

[0163] ⑩ If holds, output the result (Lines 14 - 15)

[0164] If it does not hold, set t = t + 1 and perform the next round of iteration. (Line 17)

[0165] The algorithm first evenly divides the initial search path as the initial assignment of scanning tasks. Since the distances from the initial point of the UAV to the starting points of the scanning tasks 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 serial number UAVs, and adjust the search path division points according to the magnitude of the distance difference to make the total flight distances of adjacent serial number UAVs closer until convergence to 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 in the acceleration period, the moving distance will be 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 preset maximum number of iteration times, the iteration stops, and thus the final complete search path of each UAV is obtained

[0166] Verify the algorithm within the rectangular and sector task target areas. Specifically set the number of UAVs assigned to the task, the initial positions, and the specific parameters of the target detection area. According to the multi-UAV collaborative detection algorithm, plan the search movement trajectories for the UAVs, and obtain Figure 6 and Figure 7 the search movement trajectories of the UAVs shown

[0167] Through Figure 6 and Figure 7 results, it can be verified that for different combinations of specific parameters of multi-UAVs and task target areas, according to the multi-UAV collaborative detection algorithm, search movement trajectories can be planned for the UAVs, and according to the final detection and search results, the coverage rate of the target area is nearly 1

[0168] In summary, for the multi-UAV target assignment and path planning method for executing multiple regional tasks in this embodiment, when performing target assignment, an automated task assignment process is adopted, which adapts to complex and changeable actual environments, realizes UAV target assignment by combining the distance from the UAV to the task area and the size of the task area, reduces manual intervention, and reduces the possibility of human errors

[0169] Moreover, the present invention aims to generate multi-UAV scanning trajectories to reduce the final time consumption; an iterative optimization algorithm that simultaneously considers the flight trajectory from the takeoff position of the UAV 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.

[0170] As described above, only the preferred specific embodiments of the present invention are provided, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived 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 multi-UAV target allocation and path planning for performing multi-area missions, characterized in that: include: Step S1, obtaining multiple drone element information and environmental element information of multiple regional target tasks to be performed; Step S2: Determine the corresponding number of drones required according to the size of each regional target task in the acquired element information; and construct an MVRP problem with the goal of minimizing the total path distance and minimizing the total cost, and allocate drones to each regional target task; Step S3: perform path planning, and plan a complete path for each UAV from the take-off position to the starting point of the scanning path for scanning and surveying in the assigned regional target task, and then to the end point of the scanning path; so that the UAVs performing scanning tasks in each regional target task can complete their respective tasks at the same time, shortening the total time to complete the task.

2. The method for multi-UAV target allocation and path planning for performing multi-area tasks according to claim 1, characterized in that: In step S1, the environmental element information of the task area is obtained, including anchor points, area and main axis direction; The anchor point is used to determine the position of the task area; the area 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 multi-UAV element information includes parameters including the number of UAVs, the take-off position of each UAV, the individual detection range and the flight speed; among which, the individual detection range is the width of the field of view of the UAV for scanning and detection.

3. The method for multi-UAV target allocation and path planning for performing multi-area tasks according to claim 1, characterized in that: In step S2, it includes: Step S201: According to the take-off position of each drone and the position of each mission area, the distance between each drone and each mission, the distance between tasks and the task equivalent distance are calculated to establish a distance matrix; Step S202: Determine the number of drones assigned to each task according to the area ratio of each target task in each area; and construct an MVRP problem with the goal of minimizing the total path distance and minimizing the total cost; Step S203, solving the established MVRP problem; using the distance matrix as input, solving the MVRP problem, and assigning a corresponding drone to each task.

4. The method for multi-UAV target allocation and path planning for performing multi-area tasks according to claim 3, characterized in that: In the distance matrix established in step S201, The distance between the drone and the mission is the Euclidean distance between the drone’s takeoff position and the anchor point in the mission area; The distance between tasks is the Euclidean distance between the anchor points of two task areas; The mission equivalent distance is the area of ​​the region divided by the width of the drone's field of view.

5. The method for multi-UAV target allocation and path planning for performing multi-area tasks according to claim 3, characterized in that: In step S202, drones are allocated according to the area ratio of each regional target task; more drones are allocated to targets with larger areas to improve the overall task completion efficiency; The MVRP mathematical model constructed with minimizing the total path distance and minimizing the total cost as the objective function is described as: Where, T = {1, 2…, m} represents the task set, m is the number of tasks; X = {1, 2…, u} represents the drone set, u is the number of drones; i, j are the task numbers in the task set, i≠j; k is the total drone number in the drone set; c ij is the distance from task i to task j; γ ij is the task equivalent path length from task i to task j; To indicate whether the path from task i to task j is selected by UAV k, the value is {0,1}; f1 is the weight factor of the flight distance; f2 is the weight factor of the equivalent path; r i is the number of times task i needs to be visited.

6. The method for multi-UAV target allocation and path planning for performing multi-area tasks according to claim 5, characterized in that: The number of times task i needs to be visited r i The determination process includes: 1) According to the number of drones u and the area of ​​the regional target A i Construct an integer programming problem and solve it for the first time to get the number of drones required for each area's target mission; The number of times r the region target i obtained by the initial solution needs to be visited i for: A total is the sum of the areas of all regional target tasks; 2) According to the sum of the number of visits of each regional target task obtained by the initial solution, which is u or u-1, determine whether to adjust the number of visits; If it is u, the number of visits to the planned regional target tasks will not be adjusted; if it is u-1, the number of visits to the planned largest regional target will be increased by 1 to ensure that all drones participate in target allocation.

7. The method for multi-UAV target allocation and path planning for performing multi-area tasks according to any one of claims 1 to 6, characterized in that: In step S3, the path planning process of the UAV performing the regional target mission includes: Step S301: According to the element information obtained in step S1, an initial coverage trajectory of the mission area is planned, 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 S302, dividing the initial coverage trajectory into the same number of trajectory segments as the number of drones, and pairing the trajectory segments with the drones; adjusting the segmentation point positions of the trajectory segments, searching for segmentation point positions that make the distance lengths of each drone from the take-off position to the completion of paired trajectory segment detection the same; outputting the trajectory segments after the segmentation point positions are adjusted as the area scanning paths of the paired drones.

8. The method for multi-UAV target allocation and path planning for performing multi-area tasks according to claim 7, characterized in that: In step S301, 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 assigned UAV takeoff position 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.

9. The method for multi-UAV target allocation and path planning for performing multi-area tasks according to claim 6, characterized in that: In step S302, the process of adjusting the position of the segmentation point of the trajectory segment includes: Step S302-1, 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 mission area; Step S302-2: 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 a corresponding path control point sequence; Step S302-3, 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 S302-4, setting the initial iteration count of the search to zero; Step S302-5, 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, moving the segmentation points of the two adjacent trajectory segments assigned to the adjacent UAVs along the search path in a direction of reducing the total flight distance difference, to obtain the search path for the next iteration; Step S302-6: 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 S302-7; if no, return to step S302-5 for the next iteration until the maximum number of iterations is reached; Step S302-7: output the trajectory segment after the segmentation point position is adjusted as the area scanning path of the paired drone.

10. The method for multi-UAV target allocation and path planning for performing multi-area tasks according to claim 9, characterized in that: The initialization parameters in step S301 are also set as follows: acceleration period τ; acceleration ratio coefficient σ; non-acceleration ratio coefficient σ′; σ′<σ<1; In the iterative search in step S305, when the number of iterations t 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.

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