Task allocation method for enabling multiple unmanned aerial vehicles to correspond to multiple tasks

Abstract

The invention relates to a task allocation method for multiple unmanned aerial vehicles corresponding to multiple tasks, and the method comprises the steps: obtaining the information of multiple unmanned aerial vehicles, and the information of multiple tasks which need to be executed by the unmanned aerial vehicles and comprise different target types; the target type of the task comprises a point target, a line target and a region target; according to the initial position information of the unmanned aerial vehicles and the position information of the tasks, the distance between each unmanned aerial vehicle and each task, the distance between the tasks and the task equivalent distance are calculated, and a distance matrix is established; according to the quantitative relationship between the unmanned aerial vehicle and the task and the target type of the task, constructing an MVRP problem which is matched with the target type and takes the minimization of the total path distance and the minimization of the total cost as distribution targets; and solving an MVRP problem by taking the distance matrix as input, and allocating a corresponding unmanned aerial vehicle to each task. According to the invention, tasks of different target types can be effectively planned and allocated to multiple unmanned aerial vehicles, so that the operation efficiency of the whole unmanned aerial vehicle system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle target allocation, and in particular to a method for allocating tasks corresponding to multiple unmanned aerial vehicles. Background Art

[0002] With the rapid development of drone technology, rotary-wing drones have been widely used due to their flexibility, easy control, wide field of view and diversified payload. Specific application areas include power inspection, agricultural and forestry plant protection, anti-terrorism rescue, environmental monitoring and logistics transportation. However, as the complexity of tasks increases, the capabilities and efficiency of a single drone often cannot meet actual needs. At this time, the advantages of multi-drone collaboration become increasingly apparent. Multi-drone collaboration can significantly improve the completion and efficiency of tasks.

[0003] In practical applications, how to reasonably assign and allocate mission objectives becomes a key issue. Traditional task allocation methods often face challenges of mission diversity and complexity, and different tasks have different requirements for drones. Therefore, in order to improve task completion and execution efficiency, it is necessary to comprehensively consider task requirements, implement task allocation and adjustment through intelligent algorithms, and effectively solve the problem of task allocation in multi-machine and multi-objective collaborative work, so as to give full play to the advantages of multi-machine collaboration. Summary of the invention

[0004] In view of the above analysis, the present invention aims to disclose a method for allocating tasks to multiple UAVs corresponding to multiple tasks, and solve the problem of reasonable target allocation under the condition that multiple UAVs correspond to multiple tasks.

[0005] The present invention discloses a method for allocating multiple tasks to multiple unmanned aerial vehicles, comprising:

[0006] Step S1, acquiring data; acquiring information of multiple drones and information of multiple tasks including different target types to be performed by the drones; the target types of the tasks include point targets, line targets and area targets;

[0007] Step S2, preprocessing; according to the initial position information of the UAV and the position information of the task, the distance between each UAV and each task, the distance between tasks and the task equivalent distance are calculated to establish a distance matrix;

[0008] Step S3, establishing a task allocation problem; according to the quantitative relationship between UAVs and tasks, and the target type of the task, constructing an MVRP problem that matches the target type and takes minimizing the total path distance and minimizing the total cost as the allocation target;

[0009] Step S4, solving the established MVRP problem; using the distance matrix as input, solving the MVRP problem, and assigning a corresponding UAV to each task.

[0010] Furthermore, the target types that the drone needs to perform tasks include: point targets, line targets and area targets; among them,

[0011] Point and line target types cover a variety of tasks including fixed-point hovering monitoring, panoramic scanning stitching, fixed-point search, and relay communication. When allocating point and line targets, only one drone is assigned to perform the task for each target.

[0012] Regional target types cover a variety of tasks including regional image scanning, regional inspection, and regional plant protection. When allocating regional targets, based on the relationship between the number of drones and mission targets, one regional target is assigned one drone to perform the task, or multiple drones are assigned to perform the task collaboratively.

[0013] Further, in step S1,

[0014] The obtained drone information includes the initial position data and elevation data of u drones; a drone set X is established based on the drone information, and the set includes the longitude, latitude and altitude coordinates of the initial position of each drone;

[0015] The acquired task information includes the position data and elevation data of m tasks; a task set T is established according to the task information, and the set includes the task point coordinates of the point target task, the target point sequence consisting of a series of target points from the task start point to the end point of the line target task, and / or multiple groups of contour point sequences of the area target task, wherein the first group of contour point sequences in the multiple groups of contour point sequences of the area target task is used to calibrate the outer contour of the area task, and the remaining groups of contour point sequences are used to calibrate the corresponding obstacle contour or no-fly zone contour within the area task.

[0016] Furthermore, the preprocessing process in step S2 includes:

[0017] 1) Calculate the distance between each drone in the drone set X and each task in the task set T;

[0018] 2) Calculate the distance between tasks in the task set T;

[0019] 3) Calculate the equivalent distance of each task in the task set T;

[0020] In the equivalent distance, for point target tasks, a fixed value is used; for line target tasks, the path length is used; for area target tasks, the area divided by the coverage width is used;

[0021] 4) Establish a distance matrix using the calculated distances between each UAV and each task, the distances between tasks, and the task equivalent distances.

[0022] Furthermore, in step S3, the process of establishing the task allocation problem includes:

[0023] Step S301, determine whether the number u of drones is greater than the number m of mission targets; if not, proceed to step S302; if yes, proceed to step S303;

[0024] Step S302: construct a first MVRP problem for solving the task allocation in step S4;

[0025] The first MVRP problem aims to solve the problem of assigning tasks to all drones, so that one drone performs at least one task, and includes drones that perform multiple tasks;

[0026] Step S303, determine whether there is a regional target task in the task; if not, proceed to step S304; if yes, proceed to step S305;

[0027] Step S304: construct a second MVRP problem for solving the task allocation in step S4;

[0028] The second MVRP problem aims to solve the problem of assigning tasks to the same number of UAVs as the number of tasks, assigning the same number of UAVs as the number of tasks, each UAV performs one task, and the remaining UAVs do not perform tasks;

[0029] Step S305: construct a third MVRP problem for solving the task allocation in step S4;

[0030] The third MVRP problem aims to solve the problem of allocating at least one drone to each task; in the allocation process, one drone is first allocated to each non-regional target task, and the remaining drones are allocated to regional target tasks; when multiple regional target tasks are included, the remaining drones are allocated according to the area ratio of each regional target task; more drones are allocated to targets with large areas to improve the overall task completion efficiency.

[0031] Furthermore, the first, second and third MVRP problems are constructed with the MVRP mathematical model constructed with minimizing the total path distance and minimizing the total cost as the objective function, which is described as:

[0032]

[0033]

[0034]

[0035] 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;

[0036] 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 S2;

[0037] γ ij is the task equivalent path length from task i to task j; the task equivalent path length is obtained according to the distance calculated in the preprocessing of step S2;

[0038] To indicate whether the path from task i to task j is selected by UAV k, the value is {0,1};

[0039] r i is the number of times task i needs to be visited;

[0040] f1 is the weight factor of the flight distance; f2 is the weight factor of the equivalent path.

[0041] Furthermore, in the first MVRP problem, all drones are dispatched and the number of times task i needs to be visited r i Set to 1, that is, each task needs to be accessed once.

[0042] Furthermore, in the second MVRP problem, only UAVs with the same number of mission targets are dispatched, and the number of times task i needs to be visited is r i Set to 1, that is, each task needs to be accessed once.

[0043] Furthermore, the third MVRP problem includes:

[0044] 1) The number of times the point and line target tasks need to be visited r i Set to 1, that is, each point and line target task needs to be visited once, and each is assigned 1 drone;

[0045] 2) After allocating drones to point and line targets, the remaining drones are allocated to regional target tasks; when there are multiple regional target tasks, proceed to the next step;

[0046] 3) According to the number of remaining 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;

[0047] The number of times r the region target i obtained by the initial solution needs to be visitedi for:

[0048]

[0049] A total is the sum of the areas of all regional targets;

[0050] 4) 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 of the planned regional target tasks will not be adjusted; if it is U-1, the number of visits of the planned largest regional target will be increased by 1 to ensure that all drones participate in target allocation.

[0051] Furthermore, the solution process in step S4 includes:

[0052] 1) According to the established distance matrix, the objective function and constraints in the established MVRP problem, as well as related parameters including the number of times the task is visited and the time window, are converted into problem parameters of the data structure required by the planning solver;

[0053] 2) Select the optimization algorithm for the MVRP problem in the planning solver;

[0054] 3) Using the API interface, the problem parameters are passed into the solution function in the planning solver for solving the problem and obtaining the optimal planning result to allocate the UAVs to the tasks, thereby obtaining the optimal task allocation plan with the allocation objectives of minimizing the total path distance and minimizing the total cost.

[0055] The present invention can achieve one of the following beneficial effects:

[0056] The present invention discloses a method for allocating multiple tasks to multiple drones, which can provide a more efficient and reasonable task allocation scheme for the multi-drone system within a reasonable time, and help drone operators to plan and allocate tasks more effectively, thereby improving the operating efficiency of the entire drone system; it is particularly suitable for drone task planning problems in the fields of military, search and rescue, logistics and distribution, etc.

[0057] The present invention takes into account the initial position of the drone, the mission location information, and the requirements of different types of missions. By constructing the MVRP model and using the planning solver to solve it, the cost-effective mission allocation and path planning scheme can be obtained under the premise of satisfying the constraints. The automated task allocation process is adopted to adapt to the complex and changeable actual environment, and to handle different types of tasks (point tasks, line tasks, and regional tasks), which enhances the applicability and flexibility, reduces manual intervention, and reduces the possibility of human error. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0059] Figure 1 This is a flow chart of a target allocation method for multiple UAVs corresponding to multiple mission targets in an embodiment of the present invention;

[0060] Figure 2 It is a schematic diagram of the execution process of a static point target in an embodiment of the present invention;

[0061] Figure 3 Schematic diagram of the execution process of the line target task in the embodiment of the present invention;

[0062] Figure 4 Schematic diagram of the execution process of the regional target task in an embodiment of the present invention;

[0063] Figure 5 It is a schematic diagram of the process of establishing a task allocation problem in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used to illustrate the principles of the present invention together with the embodiments of the present invention.

[0065] An embodiment of the present invention discloses a method for allocating multiple UAVs to multiple mission targets, such as Figure 1 As shown, the following steps are included:

[0066] Step S1, acquiring data; acquiring information of multiple drones and information of multiple tasks including different target types to be performed by the drones; the target types of the tasks include point targets, line targets and area targets;

[0067] Step S2, preprocessing; according to the initial position information of the UAV and the position information of the task, the distance between each UAV and each task, the distance between tasks and the task equivalent distance are calculated to establish a distance matrix;

[0068] Step S3, establishing a task allocation problem; according to the quantitative relationship between UAVs and tasks, and the target type of the task, constructing an MVRP problem that matches the target type and takes minimizing the total path distance and minimizing the total cost as the allocation target;

[0069] Step S4, solving the established MVRP problem; using the distance matrix as input, solving the MVRP problem, and assigning a corresponding UAV to each task.

[0070] Specifically, the target types that the drone needs to perform tasks include: point targets, line targets and area targets; among them,

[0071] Point and line target types cover a variety of tasks including fixed-point hovering monitoring, panoramic scanning stitching, fixed-point search, and relay communication. When allocating point and line targets, only one drone is assigned to perform the task for one target; one drone can perform multiple point and line target type tasks.

[0072] The starting point and end point of the mission of point and line target types are both designated points; during the execution of point and line target type missions, each drone maneuvers to the mission starting point according to the planned path to perform the corresponding mission action, and reaches the mission end point. After the action is completed, the drone completes the current mission; it can go to the next target or end the mission according to the task allocation requirements.

[0073] Among them, the "point target" task includes the "static point target" task; the task starting point and the task ending point of the "static point target" task are the same point; when executing the "static point target" task, the specific task action is carried out after the UAV arrives at the static point position (the starting point of the static point target is also the ending point), and the point task is ended after the action is completed or the relevant conditions are triggered.

[0074] like Figure 2 , 3 As shown, it is a schematic diagram of the execution process of static point target and line target tasks.

[0075] Regional target types include a variety of tasks including regional image scanning, regional inspection, and regional plant protection. When allocating regional targets, one regional target is assigned one drone to perform the task, or multiple drones are assigned to perform the task collaboratively, based on the relationship between the number of drones and the task targets.

[0076] Any point on the area boundary of the area target type task can be selected as the task start point and end point.

[0077] During the execution of the UAV mission, the UAV will carry out specific mission actions after arriving at the mission starting point. During the execution of the action, the UAV will move along the planned path until it reaches the mission end point or ends the mission after the relevant conditions are triggered;

[0078] When multiple UAVs cooperate to perform regional target tasks, the sweep path planned for the target area can be divided into sweep segments equal to the number of UAVs and assigned to corresponding UAVs, so that the UAVs can cooperate to perform sweeps of the target area and jointly perform regional target tasks.

[0079] Preferably, after removing obstacles or avoidance zones in the area, the area of ​​the "area target" task is a polygonal area with holes (PWH, Polygon with Holes) composed of multiple groups of contour points consisting of an outer contour and an inner hole contour, wherein the outer contour and the inner hole contour are both simple polygons (the edges do not intersect, and can be convex polygons or concave polygons).

[0080] like Figure 4 As shown, this is a schematic diagram of the execution process of the regional target task.

[0081] Specifically, in step S1, the obtained drone information includes initial position data and elevation data of u drones; a drone set X is established based on the drone information, and the set includes the longitude, latitude and altitude coordinates of the initial position of each drone;

[0082] Drone set X = {x1,…,x i ,…,x u}; x i =(x lon,i ,x lat,i ,h i ), i={1,2,…,u}; where, x lon,i ,x lat,i ,h i Respectively represent the longitude, latitude and altitude of the i-th UAV coordinates;

[0083] The acquired task information includes the location data and elevation data of m tasks; a task set T is established according to the task information, which includes the task point coordinates of the point target task, the target point sequence consisting of a series of target points from the task start point to the end point of the line target task, and / or multiple groups of contour point sequences of the area target task, wherein the first group of contour point sequences in the multiple groups of contour point sequences of the area target task is used to calibrate the outer contour of the area task, and the remaining groups of contour point sequences are used to calibrate the corresponding obstacle contour or avoidance zone contour within the area task.

[0084] The m task targets include p point target tasks, l line target tasks and r area target tasks; the positions of the m task targets are combined into a task set T; in the task set T,

[0085] Each point target task consists of the longitude, latitude, and altitude coordinates of a single target point;

[0086] Each line target task consists of a set of longitude, latitude and altitude coordinates of the target point, and the target point coordinates are arranged in the execution order of the path;

[0087] Each regional target task consists of multiple groups of longitude, latitude and altitude coordinates of contour target points. The coordinates of the first group of contour target point sequence represent the coordinates of the outer contour of the polygon, and the subsequent groups of contour target point coordinate sequences represent the coordinates of the inner hole contours of each polygon surrounded by the outer contour. The coordinates of the outer contour of the polygon and the coordinates of the inner hole contours of each polygon together constitute the coordinates of the polygonal area with holes (PWH) of the regional target task.

[0088] Task set T = {t1,…,t j ,…,t m};in,

[0089] When the jth task is a point target task,

[0090] t j =(t lon,j ,t lat,j ,h j ), t lon,j ,t lat,j ,h j are the longitude, latitude and altitude of the point target respectively;

[0091] When the jth task is a line target task,

[0092] t j =[(t lon,j,1 ,t lat,j,1 ,h j,1 ),…,(t lon,j,l ,t lat,j,l ,h j,l ),…,(t lon,j,L ,t lat,j,L ,h j,L )];

[0093] t lon,j,1 ,t lat,j,1 ,h j,1 are the longitude, latitude and altitude of the starting point of the line target respectively;

[0094] t lon,j,l ,t lat,j,l ,h j,l are respectively the longitude, latitude and altitude of the lth point in the line target;

[0095] t lon,j,L ,t lat,j,L ,h j,L are the longitude, latitude and altitude of the end point of the line target respectively; L is the number of target points included in the line target;

[0096] When the jth task is a regional target task,

[0097]

[0098] is the sth group of contour target point sequence in the regional target;

[0099]

[0100] They are the Rth s The starting point longitude, latitude and altitude of each contour;

[0101] They are the Rth s The longitude, latitude and altitude of the cth point of a contour;

[0102] They are the Rth s The longitude, latitude and altitude of the end point of the first contour; C is the latitude and longitude of the first contour; s The number of target points contained in each contour.

[0103] Specifically, the preprocessing process in step S2 includes:

[0104] 1) Calculate the distance d between each drone in the drone set X and each task in the task set T xt ; There are 3 distances in total:

[0105] For point target tasks, the Euclidean distance between the UAV and the point target task point is calculated;

[0106] For line target tasks, the Euclidean distance between the UAV and the starting point of the line target task is calculated;

[0107] For regional target tasks, the Euclidean distance between the UAV and the nearest point in the target area is calculated.

[0108] 2) Calculate the distance d between tasks in the task set T tt ; There are 9 distances in total according to different target types:

[0109] For point-to-point target tasks, the Euclidean distance between two points is directly calculated;

[0110] For point target tasks and line target tasks, the Euclidean distance between the point position of the point target task and the starting point of the line target task is calculated;

[0111] For point target tasks and area target tasks, the Euclidean distance between the point position of the point target task and the nearest point of the area target task is calculated;

[0112] For line target tasks and point target tasks, the Euclidean distance between the end point position of the line target task and the point position of the point target task is calculated;

[0113] For a line target task and a line target task, the Euclidean distance between the end point position of the line target task and the start point position of the other line target task is calculated;

[0114] For line target tasks and regional target tasks, the Euclidean distance between the end point of the line target task and the nearest point of the regional target task is calculated;

[0115] For regional target tasks and point target tasks, the Euclidean distance between the nearest point of the regional target task and the point position of the point target task is calculated;

[0116] For regional target tasks and line target tasks, the Euclidean distance between the nearest point of the regional target task and the starting point of the line target task is calculated;

[0117] For region target tasks and region target tasks, the Euclidean distance between the closest points of the two regions is calculated.

[0118] 3) Calculate the equivalent distance of each task in the task set T; there are three types of equivalent distances:

[0119] For point target tasks, a fixed value is used;

[0120] For line target tasks, path length is used;

[0121] For regional target tasks, the area of ​​the region is divided by the coverage width, where the coverage width is the field of view width of the drone.

[0122] 4) Establish a distance matrix using the calculated distances between each UAV and each task, the distances between tasks, and the task equivalent distances.

[0123] Specifically, Figure 5 As shown, in step S3, the process of establishing the task allocation problem includes:

[0124] Step S301, determine whether the number u of drones is greater than the number m of mission targets; if not, proceed to step S302; if yes, proceed to step S303;

[0125] Step S302: construct a first MVRP problem for solving the task allocation in step S4;

[0126] The first MVRP problem aims to solve the problem of assigning tasks to all drones, so that one drone performs at least one task, and includes drones that perform multiple tasks;

[0127] Step S303, determine whether there is a regional target task in the task; if not, proceed to step S304; if yes, proceed to step S305;

[0128] Step S304: construct a second MVRP problem for solving the task allocation in step S4;

[0129] The second MVRP problem aims to solve the problem of assigning tasks to the same number of UAVs as the number of tasks, assigning the same number of UAVs as the number of tasks, each UAV performs one task, and the remaining UAVs do not perform tasks;

[0130] Step S305: construct a third MVRP problem for solving the task allocation in step S4;

[0131] The third MVRP problem aims to solve the problem of allocating at least one drone to each task; in the allocation process, one drone is first allocated to each non-regional target task, and the remaining drones are allocated to regional target tasks; when multiple regional target tasks are included, the remaining drones are allocated according to the area ratio of each regional target task; more drones are allocated to targets with large areas to improve the overall task completion efficiency.

[0132] Specifically, the first, second and third MVRP problems are all constructed with the MVRP mathematical model constructed with minimizing the total path distance and minimizing the total cost as the objective function, which is described as:

[0133]

[0134]

[0135]

[0136] 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;

[0137] 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 S2;

[0138] γ ij is the task equivalent path length from task i to task j; the task equivalent path length is obtained according to the distance calculated in the preprocessing of step S2;

[0139] To indicate whether the path from task i to task j is selected by UAV k, the value is {0,1};

[0140] r i is the number of times task i needs to be visited;

[0141] f1 is the weight factor of the flight distance; f2 is the weight factor of the equivalent path.

[0142] Objective function (1) represents minimizing the total driving distance and minimizing the total cost;

[0143] Constraint (2) means that each task must be visited once.

[0144] Constraint (3) indicates that the path of each UAV must start from the starting point and end at the end point.

[0145] Specifically, in the first MVRP problem, all drones are dispatched and the number of times task i needs to be visited is r i Set to 1, that is, each task needs to be accessed once.

[0146] Specifically, in the second MVRP problem, only UAVs with the same number of mission targets are dispatched, and the number of times task i needs to be visited is r i Set to 1, that is, each task needs to be accessed once.

[0147] Specifically, the third MVRP problem includes:

[0148] 1) The number of times the point and line target tasks need to be visited r i Set to 1, that is, each point and line target task needs to be visited once, and each is assigned 1 drone;

[0149] 2) After allocating drones to point and line targets, the remaining drones are allocated to regional target tasks; when there are multiple regional target tasks, proceed to the next step;

[0150] 3) According to the number of remaining 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;

[0151] The number of times r the region target i obtained by the initial solution needs to be visited i for:

[0152]

[0153] A total is the sum of the areas of all regional targets;

[0154] 4) 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 of the planned regional target tasks will not be adjusted; if it is U-1, the number of visits of the planned largest regional target will be increased by 1 to ensure that all drones participate in target allocation.

[0155] Example: Assume there are 5 targets (including 3 area targets) and a total of 10 drones. The areas of the three area targets are A1 = 30, A2 = 50, and A3 = 20. total =100.

[0156] After allocating 1 drone to 2 non-regional targets, 8 drones need to be allocated to 3 regional targets. The number of drones allocated to the three regions after integer programming is:

[0157] x1=floor(30÷100×8)=floor(2.4)=2

[0158] x2=floor(50÷100×8)=floor(4)=4

[0159] x3=floor(20÷100×8)=floor(1.6)=1

[0160] Determine whether the results of integer programming need to be adjusted;

[0161]

[0162] Then, the remaining drone is assigned to the area with the largest area, and the final allocation result is:

[0163] x1=2,x2=5,x2=1

[0164] Make sure the total number of assigned drones is the total number of drones.

[0165] Specifically, the solution process in step S4 includes:

[0166] 1) According to the established distance matrix, the objective function and constraints in the established MVRP problem, as well as related parameters including the number of times the task is visited and the time window, are converted into problem parameters of the data structure required by the planning solver;

[0167] 2) Select the optimization algorithm for the MVRP problem in the planning solver;

[0168] Select an appropriate optimization algorithm based on the actual situation, such as using integer programming algorithm to deal with MVRP problems;

[0169] 3) Using the API interface, the problem parameters are passed into the solution function in the planning solver for solving the problem and obtaining the optimal planning result to allocate the UAVs to the tasks, thereby obtaining the optimal task allocation plan with the allocation objectives of minimizing the total path distance and minimizing the total cost.

[0170] To sum up, the task objective allocation method for multiple UAVs corresponding to multiple tasks can provide a more efficient and reasonable task allocation plan for the multi-UAV system within a reasonable time, helping UAV operators to plan and allocate tasks more effectively, thereby improving the operating efficiency of the entire UAV system; it is especially suitable for UAV mission planning problems in the fields of military, search and rescue, logistics and distribution.

[0171] The present invention takes into account the initial position of the drone, the mission location information, and the requirements of different types of missions. By constructing the MVRP model and using the planning solver to solve it, the cost-effective mission allocation and path planning scheme can be obtained under the premise of satisfying the constraints. The automated task allocation process is adopted to adapt to the complex and changeable actual environment, and to handle different types of tasks (point tasks, line tasks, and regional tasks), which enhances the applicability and flexibility, reduces manual intervention, and reduces the possibility of human error.

[0172] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field 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 allocating multiple tasks to multiple drones, characterized in that: include: Step S1, obtaining data; obtaining information of multiple drones and information of multiple tasks including different target types that the drones need to perform; The target types of the task include point targets, line targets and area targets; Step S2, preprocessing; according to the initial position information of the UAV and the position information of the task, the distance between each UAV and each task, the distance between tasks and the task equivalent distance are calculated to establish a distance matrix; Step S3, establishing a task allocation problem; according to the quantitative relationship between UAVs and tasks, and the target type of the task, constructing an MVRP problem that matches the target type and takes minimizing the total path distance and minimizing the total cost as the allocation target; Step S4, solving the established MVRP problem; using the distance matrix as input, solving the MVRP problem, and assigning a corresponding UAV to each task.

2. The method for allocating multiple tasks to multiple drones according to claim 1, characterized in that: The target types that drones need to perform missions include: point targets, line targets and area targets; among them, Point and line target types cover a variety of tasks including fixed-point hovering monitoring, panoramic scanning stitching, fixed-point search, and relay communication. When allocating point and line targets, only one drone is assigned to perform the task for each target. Regional target types cover a variety of tasks including regional image scanning, regional inspection, and regional plant protection. When allocating regional targets, based on the relationship between the number of drones and mission targets, one regional target is assigned one drone to perform the task, or multiple drones are assigned to perform the task collaboratively.

3. The method for allocating multiple tasks to multiple drones according to claim 1, characterized in that: In step S1, The obtained drone information includes the initial position data and elevation data of u drones; a drone set X is established based on the drone information, and the set includes the longitude, latitude and altitude coordinates of the initial position of each drone; The acquired task information includes the position data and elevation data of m tasks; a task set T is established according to the task information, and the set includes the task point coordinates of the point target task, the target point sequence consisting of a series of target points from the task start point to the end point of the line target task, and / or multiple groups of contour point sequences of the area target task, wherein the first group of contour point sequences in the multiple groups of contour point sequences of the area target task is used to calibrate the outer contour of the area task, and the remaining groups of contour point sequences are used to calibrate the corresponding obstacle contour or no-fly zone contour within the area task.

4. The method for allocating multiple tasks to multiple drones according to claim 3, characterized in that: The preprocessing process in step S2 includes: 1) Calculate the distance between each drone in the drone set X and each task in the task set T; 2) Calculate the distance between tasks in the task set T; 3) Calculate the equivalent distance of each task in the task set T; In the equivalent distance, for point target tasks, a fixed value is used; for line target tasks, the path length is used; for area target tasks, the area divided by the coverage width is used; 4) Establish a distance matrix using the calculated distances between each UAV and each task, the distances between tasks, and the task equivalent distances.

5. The method for allocating multiple tasks to multiple drones according to claim 1, characterized in that: In step S3, the process of establishing the task allocation problem includes: Step S301, determine whether the number u of drones is greater than the number m of mission targets; if not, proceed to step S302; if yes, proceed to step S303; Step S302: construct a first MVRP problem for solving the task allocation in step S4; The first MVRP problem aims to solve the problem of assigning tasks to all drones, so that one drone performs at least one task, and includes drones that perform multiple tasks; Step S303, determine whether there is a regional target task in the task; if not, proceed to step S304; if yes, proceed to step S305; Step S304: construct a second MVRP problem for solving the task allocation in step S4; The second MVRP problem aims to solve the problem of assigning tasks to the same number of UAVs as the number of tasks, assigning the same number of UAVs as the number of tasks, each UAV performs one task, and the remaining UAVs do not perform tasks; Step S305: construct a third MVRP problem for solving the task allocation in step S4; The third MVRP problem aims to solve the problem of allocating at least one drone to each task; in the allocation process, one drone is first allocated to each non-regional target task, and the remaining drones are allocated to regional target tasks; when multiple regional target tasks are included, the remaining drones are allocated according to the area ratio of each regional target task; more drones are allocated to targets with large areas to improve the overall task completion efficiency.

6. The method for allocating multiple tasks to multiple drones according to claim 5, characterized in that: The first, second and third MVRP problems are all constructed with minimizing the total path distance and minimizing the total cost as the objective function. The MVRP mathematical model 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, and the distance between tasks is obtained according to the distance calculated in the preprocessing of step S2; γ ij is the task equivalent path length from task i to task j; the task equivalent path length is obtained according to the distance calculated in the preprocessing of step S2; To indicate whether the path from task i to task j is selected by UAV k, the value is {0,1}; r i is the number of times task i needs to be visited; f1 is the weight factor of the flight distance; f2 is the weight factor of the equivalent path.

7. The method for allocating multiple tasks to multiple drones according to claim 6, characterized in that: In the first MVRP problem, all drones are dispatched and the number of times task i needs to be visited is r i Set to 1, that is, each task needs to be accessed once.

8. The method for allocating multiple tasks to multiple drones according to claim 6, characterized in that: In the second MVRP problem, only UAVs with the same number of mission targets are dispatched, and the number of times task i needs to be visited is r i Set to 1, that is, each task needs to be accessed once.

9. The method for allocating multiple tasks to multiple drones according to claim 6, characterized in that: The third MVRP problem includes: 1) The number of times the point and line target tasks need to be visited r i Set to 1, that is, each point and line target task needs to be visited once, and each is assigned 1 drone; 2) After allocating drones to point and line targets, the remaining drones are allocated to regional target tasks; when there are multiple regional target tasks, proceed to the next step; 3) According to the number of remaining 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 targets; 4) 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 of the planned regional target tasks will not be adjusted; if it is U-1, the number of visits of the planned largest regional target will be increased by 1 to ensure that all drones participate in target allocation.

10. The method for allocating multiple tasks to multiple UAVs according to any one of claims 1 to 9, characterized in that: The solution process in step S4 includes: 1) According to the established distance matrix, the objective function and constraints in the established MVRP problem, as well as related parameters including the number of times the task is visited and the time window, are converted into problem parameters of the data structure required by the planning solver; 2) Select the optimization algorithm for the MVRP problem in the planning solver; 3) Using the API interface, the problem parameters are passed into the solution function in the planning solver for solving the problem and obtaining the optimal planning result to allocate the UAVs to the tasks, thereby obtaining the optimal task allocation plan with the allocation objectives of minimizing the total path distance and minimizing the total cost.

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