UAV deployment and transmission method, electronic device and readable storage medium

Abstract

The present application provides a method for deploying and transmitting drones, an electronic device, and a readable storage medium. It is aimed at the scenario of using multiple drones to provide transmission services for multiple robots that move randomly in a certain area. According to the above scenario, a drone deployment and task transmission optimization model is disclosed, which aims to maximize the total number of successful transmissions and is constrained by the drone flight speed, safety spacing, etc., and simulated annealing and the Hungarian algorithm are used to solve the optimal drone deployment and task transmission scheme. In addition, a drone quantity optimization model is disclosed, which aims to maximize the weighted value of the expected total number of successful transmissions and the deployment cost and is constrained by the total deployment budget, and a three-point search algorithm is used to solve the optimal drone quantity scheme.

Description

Technical Field

[0001] The present application relates to the field of drone technology, and more specifically, to a drone deployment and transmission method, an electronic device, and a readable storage medium. Background Art

[0002] The sixth generation (6G) cellular networks are designed to provide ubiquitous high-speed communications. Therefore, communications can be conducted between ground robots, drones, and between them. Drones have advantages such as low probability of communication blocking and high mobility, but are often far away from ground sensors / targets, resulting in low observation accuracy. On the other hand, ground robots have the advantage of being close to ground sensors / targets, but suffer from ground blockage and low mobility. To exploit their complementarity (e.g., field of view, communication links, computing power), a promising paradigm called integrated robot networks has emerged to provide communications for drone-ground robot cooperative applications.

[0003] However, due to the randomness of the movement of ground robots, the positional relationship between drones and ground robots is the key to determining the transmission connection. How to efficiently deploy drones according to different ground robot tasks and arrange the transmission connection between drones and ground robots has become a challenge that needs to be addressed urgently. In addition, the position between drones and ground robots changes dynamically and continuously, and corresponding data is generated at every moment, which requires a lot of computing power. How to reduce the amount of calculation is also a problem that the patent needs to consider. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a drone deployment and transmission method, an electronic device and a readable storage medium to solve the above-mentioned problems existing in the prior art, achieve a higher data transmission success rate between the drone and the ground robot in the mission scenario, and reduce the amount of data processing.

[0005] In a first aspect, a drone transmission method is provided, which is applied to a task scenario where multiple drones are used to provide data transmission services for multiple robots that move randomly in a given area. The method may include the following contents:

[0006] 1) Characterize the task scenario, including:

[0007] The working time of the drone is divided into T periods, and the duration of each period is , t represents the tth period; m ground robots and n The drones are numbered to form a set; let the transmission power of the ground robot numbered i be , the bandwidth is , duration The amount of data transfer required is ; The channel gain parameter of the UAV is , the power of white noise ;

[0008] 2) Place drones and ground robots in each time period The position of the moment is taken as its position in each period, and a is greater than 1;

[0009] 3) According to the data represented and the time period t, the number is i The ground robot and the number j The position of the UAV, calculate the data transmission rate between the ground robot numbered i and the UAV numbered j during time period t , and further determine whether the data transmission between the two is successful, the judgment value is ;when , it is considered that the data transmission is successful, The value is 1, otherwise it is considered a failure and the value is 0;

[0010] 4) Set the maximum number of successful transmissions in time period t to The objective function is set according to the flight requirements of the UAV in the mission scenario. The total number of successful transmissions refers to the number of groups of UAVs and ground robots with successful data transmission.

[0011] 5) According to the constraints, the objective function is solved to obtain the position deployment plan of the UAV at time period t and the data transmission pairing relationship between the UAV and the ground robot under the deployment plan.

[0012] In an optional implementation manner: the constraints include:

[0013] Use collection Represents the two-dimensional projection of the entire mission area. The mission area refers to the flight area of ​​the UAV. t , numbered i The position of the ground robot is recorded as: , numbered j The position of the drone is recorded as: , Indicates the number j The fixed flight altitude of the drone, ;

[0014] The distance between any two drones is greater than the safe distance ;

[0015] The distance between the positions of any drone in any two adjacent time periods is less than and The product of For the number jThe upper limit of the flight speed of the drone.

[0016] In an optional implementation, simulated annealing and Hungarian algorithm are used to solve the objective function, the simulated annealing algorithm is used to solve the optimal UAV position coordinates, and the Hungarian algorithm is used to solve the optimal pairing relationship between the UAV and the ground robot for data transmission under the UAV position coordinates given in the iterative process of the simulated annealing algorithm. The specific steps for solving the objective function are:

[0017] 1) Randomly generate a set of drone deployment coordinates as the initial solution;

[0018] 2) Given temperature parameters O Sufficient initial temperature O 0;

[0019] 3) Given the number of iterations K ;

[0020] 4) Given U ;

[0021] 5) Yes k =1, …, K Iterate according to steps 6 to 13 respectively;

[0022] 6) Use random perturbations to generate new solutions;

[0023] 7) Given any set of drone coordinates, the optimal pairing problem for data transmission between drones and ground robots is transformed into a non-standard 0-1 assignment problem;

[0024] 8) When When adding virtual drones, where the data transmission between each virtual drone and all ground robots fails. When adding virtual ground robots, where the data transmission between each virtual ground robot and all drones fails;

[0025] 9) Given any set of UAV coordinates, the optimal pairing problem for data transmission between the UAV and the ground robot is transformed into a standard 0-1 assignment problem and solved using the Hungarian algorithm;

[0026] 10) Calculate the difference between the solution before random perturbation and the solution after random perturbation ;

[0027] 11) If Then accept the randomly perturbed solution as the new current solution, otherwise Accept the randomly perturbed solution as the new current solution;

[0028] 12) If continuous U If none of the new solutions are accepted, the current solution is output as the optimal solution and the process ends;

[0029] 13) O reduce, , and go to 5).

[0030] In an optional implementation, the objective function is: ;

[0031] in ; ,1≤i≤m,1≤t≤T;

[0032] ; Indicates time period t Number i Whether the data generated by the ground robot is transmitted to j of drones; among them, when When , it indicates no transmission.

[0033] In an optional implementation,

[0034] In the period t :

[0035] Number j The drone with the serial number i The distance of the ground robot is :

[0036] ;

[0037] Number The drone with the serial number The distance between the drones is :

[0038] ,

[0039] ;

[0040] Number j The distance between the positions of the UAV in two adjacent time periods is :

[0041] ,

[0042] .

[0043] Number i The ground robot numbered jThe channel gain between the UAVs is :

[0044] ,set up is the channel gain at 1 meter;

[0045] Number i The ground robot numbered j The data transmission rate between drones is :

[0046] ,1≤i'≤m.

[0047] On the other hand, a method for deploying drones is provided, which uses any of the above methods to obtain an objective function for maximizing the total number of successful transmissions in time period t, and also includes the following contents:

[0048] Number i The random moving distance and moving angle of the ground robot from time period t to time period t-1 are the parameters, and the position of the ground robot in time period t is The random variable of ,obey The random distribution on the moving angle is ,obey Random distribution on ; The upper limit of the speed of the ground robot;

[0049] Calculate the objective function, calculate the total number of successful transmissions in time period t, and find the maximum value of the total number of successful transmissions and the time period t The total number of successful transmissions is expressed as a function of ;

[0050] Calculate the expected value of the total number of successful transmissions of the drone in all time periods:

[0051] ; Indicates the expected total number of successful transmissions. It represents the mathematical expectation operation;

[0052] The objective function is set to maximize the weighted value of the expected total number of successful transmissions and the deployment cost: , represents the weighted value of the expected total number of successful transmissions and the deployment cost, and They represent the weights of the expected total number of successful transmissions and the deployment cost respectively; , represents the cost of deploying a drone, represents the total deployment budget, Indicates the number of deployed drones;

[0053] For the objective function Solve and find the value of n that maximizes the value of the objective function.

[0054] In an optional implementation, a three-part search algorithm is used to find the objective function The solution includes the following steps:

[0055] 1) Find the upper limit of the number of drones , This is the floor rounding operation;

[0056] 2) Search for the left endpoint of the interval L The initial value is 0, the right endpoint R The initial value is ;

[0057] 3) Repeat 4) and 5) until exit;

[0058] 4) The left third point of the search interval is , the right third of the search interval is ;

[0059] 5) If , then let ,like , then let ,like And the search interval contains more than three points, then let and ,like And the search interval contains two or fewer points, then the optimal number of output drones is , and exit.

[0060] According to a third aspect, an electronic device is provided, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of any method described in the first aspect are performed.

[0061] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any method described in the first aspect are implemented.

[0062] Beneficial effects: 1) In the scenario where the movement of ground robots is random, the deployment location, transmission connection and number of UAVs are optimized, providing further guidance for the application of UAVs in integrated robot networks; 2) By combining simulated annealing with the Hungarian algorithm, a good initial solution can be quickly found using the Hungarian algorithm in the initial stage, and then further optimized using simulated annealing to improve the quality of the solution. This method can obtain a solution with good enough performance while fully shortening the calculation time; 3) By planning the task time into multiple discrete time periods, the positions selected by the UAV and the ground robot at a specific time in each time period are used as the positions of the entire time period, and the movement trajectories of the ground robot and the flying robot can be simulated with discrete data, which can directly process the simulation and process discrete data, which will greatly reduce the amount of processing and have less impact on the accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A task scenario applicable to the embodiments of the present application;

[0065] Figure 2 A schematic diagram of a flow chart of a method for deploying and transmitting a drone provided in an embodiment of the present application;

[0066] Figure 3 A schematic diagram of the position of a drone and its transmission connection relationship with a ground robot in a mission scenario provided in an embodiment of the present application;

[0067] Figure 4 A schematic diagram of the deployment of drones in a mission scenario provided in an embodiment of the present application.

[0068] Figure 5 A schematic diagram of a device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Unless otherwise defined, the technical terms or scientific terms used in this application should be the usual meanings understood by people with general skills in the field to which the present invention belongs. The "first", "second" and similar words used in this application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0070] The drone deployment and transmission method provided in the embodiment of the present application can be applied to Figure 1 In the scenario shown, Figure 1 As shown. In the mission scenario, both the ground robot and the UAV can observe the ground sensors / targets, and real-time data transmission can be carried out between the UAV and the ground robot. For any given scenario, the number, initial position, bandwidth, and upper limit of the moving speed of the ground robots are known, and their moving paths are random. In addition, the amount of data that the ground robot needs to transmit to the UAV per unit time is also known. The area where the mission scenario is located is called the mission area. The mission area is the flight working area of ​​the UAV. The two-dimensional projection boundary of the mission area is known and can be represented by a plane coordinate system.

[0071] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0072] Figure 2 A schematic diagram of a flow chart of a drone transmission method provided in an embodiment of the present application. The method may include:

[0073] Step 1: Mission area characterization: The mission scenario is to use multiple drones to provide transmission services for multiple robots that move randomly in a certain area. The characterization method includes the following processes:

[0074] 1. Divide the working time of the drone into T periods, and the duration of each period is :

[0075] gather (twenty one),

[0076] In formula (21), the set express T In this example, , ; Long time period, small amount of calculation, but will affect the accuracy; short time period, large amount of calculation, but accurate. Usually a value is determined based on experience according to the characteristics of the scene (such as the speed of the drone and robot, the safety of the environment, the importance of the task). The working time is determined according to the endurance of the drone. Usually the single endurance of a multi-rotor drone is about 30 minutes. Considering that it may need to fly back and need to relax a little margin to ensure safety, it can be set to a value slightly smaller than half of the endurance. Here it is 240*3=12 minutes.

[0077] In each time period, drones and ground robots The position of the moment is taken as its position in each time period, and a is greater than 1. For example, the position in each time period can be selected as one of the positions at the beginning of the time period, in the middle of the time period, or at the end of the time period, as long as it remains consistent.

[0078] By dividing the time period t, the continuous movement trajectory of ground robots and drones can be discretized and represented. By simulating their movement trajectory with discrete data, the amount of data processing can be reduced, thus reducing computing power costs.

[0079] 2. Number the ground robots to form a ground robot number set:

[0080] (twenty two),

[0081] In formula (22), express m A set of ground robot numbers. In this example, , for ground robots Figure 3 The circle in the figure indicates the number of the user device in the ground robot;

[0082] 3. Number i The initial position of the ground robot is: In this example, Figure 3 The position of the middle circle indicates the position of the corresponding ground robot;

[0083] 4. Number i The transmission power of the ground robot is: In this example, all Both are 1W;

[0084] 5. Number iThe random moving distance of the ground robot from time period t to time period t-1 is the parameter is the position of the ground robot in time period t The random variable of ,obey A random distribution on is the upper limit of the ground robot’s speed. In this example, the moving distance is subject to Uniform distribution on

[0085] 6. Number i The random movement angle of the ground robot from time period t to time period t-1 is the parameter is the position of the ground robot in time period t The random variable is denoted as In this case, the movement angles are subject to The random moving distance and random moving angle are determined by two things: 1) First, the characteristics of the ground robot itself only determine the range of their distribution, that is, the upper speed limit of 1m / s determines that the range of the random moving distance distribution is [0,3], and the structural characteristics of the ground determine the distribution of the random moving angle. For example, in this embodiment, a robot that can turn 360 degrees is used; 2) Then, the position of the robot further determines their specific distribution law within the above range. Taking the random moving distance as an example, if the current position is a plain, the moving distance is more likely to be longer, such as the probability of being on [0,2] is lower and the probability of being on [2,3] is higher. On the contrary, if the current position is a hillside, the moving distance is more likely to be shorter, such as the probability of being on [0,1] is higher and the probability of being on [1,3] is lower.

[0086] 7. Number i The upper limit of the ground robot's speed is: In this example, all Both ;

[0087] 8. Number i The bandwidth of the ground robot is: In this example, all All are 1MHz;

[0088] 9. Number i The ground robot in the duration The amount of data transfer required is : In this example, all Both are 20MB;

[0089] 10. Number the drones to form a drone number set:

[0090] (twenty three),

[0091] In formula (23), express n A collection of drone numbers. In this example, , the drone is Figure 3 The triangle in the figure indicates that the number of the drone is marked in the triangle;

[0092] 11. Number j The upper limit of the flight speed of the drone is: In this example, all Both are 120m / min;

[0093] 12. Number j The initial position of the drone is: .in, Indicates the number j The fixed flight altitude of the drone. In this example, Figure 3 The position of the triangle in the figure represents the two-dimensional projection position of the corresponding drone. , ;

[0094] 13. The safe distance between drones is: In this example, ;

[0095] 14. The power of white noise is In this example, ;

[0096] 15. The channel gain at 1 meter is In this example, ;

[0097] 16. Use represents the 2D projection of the entire task area. In this example, for Figure 3 A square area with a side length of 1400m.

[0098] Step 2: A UAV deployment and task transmission optimization model, including the following processes:

[0099] 1. During the period t , numbered i The position of the ground robot is: ;

[0100] 2. During the period t , numbered j The position of the drone is: ;

[0101] 3. During the period t , numbered j The drone with the serial number i The distance of the ground robot is:

[0102] ;(twenty four),

[0103] In formula (24), Indicates during the period t Number j The drone with the serial number i The distance of the ground robot;

[0104] 4. During the period t , numbered The drone with the serial number The distance between the drones is:

[0105] (25),

[0106] In formula (25), Indicates during the period t Number The drone with the serial number The distance between drones;

[0107] 5. Number j The flight distance of the drone is:

[0108] (26),

[0109] In formula (26), Indicates during the period and time period t The number between j The flight distance of the drone;

[0110] 6. Indicates time period t Number i Whether the data generated by the ground robot is transmitted to j Among them, when When When , it means no transmission;

[0111] 7. During the period t , numbered i The ground robot numbered j The channel gain between drones is:

[0112] (27),

[0113] In formula (27), Indicates during the period t Number i The ground robot numbered j The channel gain between UAVs;

[0114] 8. During the period t , numbered i The ground robot numbered j Data transmission rate between drones:

[0115] ,1≤i'≤m(28),

[0116] In formula (28), Indicates during the period t Number i The ground robot numbered j Data transmission rate between drones;

[0117] 9. During the period t , numbered i The ground robot numbered j Whether the data transmission between the drones is successful:

[0118] (29),

[0119] In formula (29), Indicates during the period t Number i The ground robot numbered j Whether the data transmission between the drones is successful. Indicates the condition satisfaction operator, that is, when the condition in the brackets is met, it is 1, indicating that the transmission is successful, otherwise it is 0, indicating that the transmission fails;

[0120] 10) Constraints on the value of drone deployment decision variables:

[0121] (30);

[0122] 11. UAV safety distance constraints:

[0123] (31);

[0124] 12. UAV flight speed restrictions:

[0125] (32);

[0126] 13. Task transfer decision variable value constraints:

[0127] (33);

[0128] 14. Time period t , each ground robot can only transmit tasks to one drone at most:

[0129] (34);

[0130] 15. Time period t , each UAV can only accept at most one transmission task from the ground robot:

[0131] ,1≤i≤m,1≤t≤T(35);

[0132] 16. By time period t The objective function is to maximize the total number of successful transmissions:

[0133] (36),

[0134] In formula (36), Indicates time period t The total number of successful transmissions;

[0135] 17. Summarize equations (24) to (36) to obtain the optimization model of UAV position deployment and task transmission;

[0136] 18. The present invention uses simulated annealing and Hungarian algorithm to solve the optimization model of drone position deployment and task transmission. Among them, the simulated annealing algorithm is used to solve the optimal drone position deployment plan, and the Hungarian algorithm is used to solve the optimal task transmission plan under a given drone position plan. The specific steps are as follows: 1) randomly generate a set of drone positions as the initial solution; 2) given temperature parameters O Sufficient initial temperature O 0. In this example, ; 3) Given the number of iterations K In this example, ;4) Given U In this example, ; 5) Yes k =1, …,8 do 6) to 12); 6) Use random perturbations to generate new solutions; 7) Given any UAV location deployment plan, solving the problem of optimal task transmission is a non-standard 0-1 assignment problem; 8) When When adding "virtual" drones, where the data transmission between each "virtual" drone and all ground robots is successful. When adding "virtual" ground robots, where the data transmission between each "virtual" ground robot and all drones is failed; 9) Convert equation (36) into an equivalent form At this point, the task transmission optimization problem under the given UAV position deployment scheme is transformed into a standard 0-1 assignment problem, which can be solved using the common Hungarian algorithm; 10) Calculate the difference between the solution before random perturbation and the solution after random perturbation. ; 11) If Then accept the randomly perturbed solution as the new current solution, otherwise Accept the solution after random perturbation as the new current solution; 12) If three consecutive new solutions are not accepted, output the current solution as the optimal solution and end; 13) O reduce, , and go to step 5). In this example, the maximum value of the objective function at the 120th period is 4, and the maximum value of the objective function at the 120th period is Figure 4 The UAV deployment and task transmission scheme shown in the figure. The position of the triangle indicates the two-dimensional projection position of the corresponding UAV, and the number next to the circle indicates the number of the UAV to which the corresponding ground robot is connected during the transmission period.

[0137] The maximum value of the total number of successful transmissions and the time period obtained by the above algorithm t is related to the position of the ground robot, so it can be expressed as .

[0138] The disclosed embodiment also provides a drone deployment method for optimizing the number of drones.

[0139] The process includes:

[0140] 1. Constraints on the value of the decision variable for the number of drones:

[0141] (37),

[0142] In formula (37), represents the set of all natural numbers;

[0143] 2. Total budget constraints for drone deployment:

[0144] (38),

[0145] In formula (38), represents the cost of deploying a drone, represents the total deployment budget. In this example, , ;

[0146] 3. Calculate the expected total number of successful transmissions:

[0147] (39),

[0148] In formula (39), Indicates the expected total number of successful transmissions. It represents the mathematical expectation operation;

[0149] 4. Set the objective function to maximize the weighted value of the expected total number of successful transmissions and the deployment cost:

[0150] (40),

[0151] In formula (40), Represents the weighted value of the expected total number of successful transmissions and the deployment cost. and They represent the weights of the expected total number of successful transmissions and the deployment cost. , ;

[0152] 5. Summarize equations (37) to (40) to obtain the optimization model for the number of drones;

[0153] 6. The above optimization problem belongs to a discrete convex optimization problem. The present invention uses a three-part search algorithm to solve it. The specific steps of the algorithm are: 1) According to formula (38), the upper limit of the number of drones is obtained as .in, is a rounding operation; 2) the left endpoint of the search interval L The initial value is 0, the right endpoint R The initial value is ; 3) Repeat 4) and 5) until exit; 4) The left third point of the search interval is , the right third of the search interval is ; 5) If , then let ,like , then let ,like And the search interval contains more than three points, then let and ,like And the search interval contains two or fewer points, then the optimal number of output drones is , and exit. In this example, the maximum value of the objective function is 2.75, and the optimal number of drones is 4.

[0154] Corresponding to the above method, the embodiment of the present disclosure also provides an electronic device, such as Figure 5As shown, it is a schematic diagram of the structure of an electronic device 400 provided in an embodiment of the present disclosure, including: a processor 41, a memory 42, and a bus 43; the memory 42 is used to store execution instructions, including a memory 421 and an external memory 422; the memory 421 here is also called an internal memory, which is used to temporarily store the operation data in the processor 41, and the data exchanged with the external memory 422 such as a hard disk. The processor 41 exchanges data with the external memory 422 through the memory 421. When the electronic device 400 is running, the processor 41 communicates with the memory 42 through the bus 43, so that the processor 41 executes Figure 2 Steps of the UAV transmission method.

[0155] The embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the drone transmission method described in the above method embodiment are executed. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0156] The disclosed embodiments also provide a computer program product, which includes computer instructions. When the computer instructions are executed by a processor, the steps of the drone transmission method described in the above method embodiment can be executed. For details, please refer to the above method embodiment, which will not be repeated here.

[0157] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0158] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0159] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0161] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0162] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be described in the protection scope of the claims.

Claims

1. A UAV transmission method is applied to a task scenario where multiple UAVs are used to provide data transmission services for multiple robots that move randomly in a given area, and is characterized by: These include: 1) Characterize the task scenario, including: The working time of the drone is divided into T periods, and the duration of each period is , t represents the tth period; m ground robots and n The drones are numbered to form a set; let the transmission power of the ground robot numbered i be , the bandwidth is , duration The amount of data transfer required is ; The channel gain parameter of the UAV is , the power of white noise ; 2) Place drones and ground robots in each time period The position of the moment is taken as its position in each period, and a is greater than 1; 3) According to the data represented and the time period t, the number is i The ground robot and the number j The position of the UAV, calculate the data transmission rate between the ground robot numbered i and the UAV numbered j during time period t , and further determine whether the data transmission between the two is successful, the judgment value is ;when , it is considered that the data transmission is successful, The value is 1, otherwise it is considered a failure and the value is 0; 4) Set the maximum number of successful transmissions in time period t to The objective function is set according to the flight requirements of the UAV in the mission scenario. The total number of successful transmissions refers to the number of groups of UAVs and ground robots with successful data transmission. 5) According to the constraints, the objective function is solved to obtain the position deployment plan of the UAV at time period t and the data transmission pairing relationship between the UAV and the ground robot under the deployment plan.

2. The UAV transmission method according to claim 1, characterized in that: The constraints include: Use collection Represents the two-dimensional projection of the entire mission area. The mission area refers to the flight area of ​​the UAV. t , numbered i The position of the ground robot is recorded as: , numbered j The position of the drone is recorded as: , Indicates the number j The fixed flight altitude of the drone, ; The distance between any two drones is greater than the safe distance ; The distance between the positions of any drone in any two adjacent time periods is less than and The product of For the number j The upper limit of the flight speed of the drone.

3. The UAV transmission method according to claim 1 or 2, characterized in that: The objective function is solved by simulated annealing and Hungarian algorithm. The simulated annealing algorithm is used to solve the optimal UAV position coordinates. The Hungarian algorithm is used to solve the optimal pairing relationship between the UAV and the ground robot for data transmission under the UAV position coordinates given in the iterative process of the simulated annealing algorithm.

4. The UAV transmission method according to claim 3, characterized in that: The specific steps for solving the objective function are: 1) Randomly generate a set of drone deployment coordinates as the initial solution; 2) Given temperature parameters O Sufficient initial temperature O 0; 3) Given the number of iterations K ; 4) Given U ; 5) Yes k =1, …, K Iterate according to step 6) to step 13) respectively; 6) Use random perturbations to generate new solutions; 7) Given any set of drone coordinates, the optimal pairing problem for data transmission between drones and ground robots is transformed into a non-standard 0-1 assignment problem; 8) When When adding virtual drones, where the data transmission between each virtual drone and all ground robots fails. When adding virtual ground robots, where the data transmission between each virtual ground robot and all drones fails; 9) Given any set of UAV coordinates, the optimal pairing problem for data transmission between the UAV and the ground robot is transformed into a standard 0-1 assignment problem and solved using the Hungarian algorithm; 10) Calculate the difference between the solution before random perturbation and the solution after random perturbation ; 11) If Then accept the randomly perturbed solution as the new current solution, otherwise Accept the randomly perturbed solution as the new current solution; 12) If continuous U If none of the new solutions are accepted, the current solution is output as the optimal solution and the process ends; 13) O reduce, , and go to 5).

5. The UAV transmission method according to claim 1 or 2, characterized in that: The objective function is: ； in ; ,1≤i≤m,1≤t≤T; ; Indicates time period t Number i Whether the data generated by the ground robot is transmitted to j of drones; among them, when When , it indicates no transmission.

6. The UAV transmission method according to claim 5, characterized in that: In the period t : Number j The drone with the serial number i The distance of the ground robot is : ； Number i The ground robot numbered j The channel gain between the UAVs is : ,set up is the channel gain at 1 meter; Number i The ground robot numbered j The data transmission rate between drones is : ,1≤i'≤m。 7. The UAV transmission method according to claim 5, characterized in that: In the period t : Number The drone with the serial number The distance between the drones is : ， ； Number j The distance between the positions of the UAV in two adjacent time periods is : ， 。 8. A method for deploying a drone, characterized in that: The objective function for maximizing the total number of successful transmissions in time period t is obtained by using the method according to any one of claims 1 to 7, and further includes the following contents: Number i The random moving distance and moving angle of the ground robot from time period t to time period t-1 are the parameters, and the position of the ground robot in time period t is The random variable of ,obey The random distribution on the moving angle is ,obey Random distribution on ; The upper limit of the speed of the ground robot; Calculate the objective function, calculate the total number of successful transmissions in time period t, and find the maximum value of the total number of successful transmissions and the time period t The total number of successful transmissions is expressed as a function of ; Calculate the expected value of the total number of successful transmissions of the drone in all time periods: ; Represents the expected total number of successful transmissions, where It represents the mathematical expectation operation; The objective function is set to maximize the weighted value of the expected total number of successful transmissions and the deployment cost: , represents the weighted value of the expected total number of successful transmissions and the deployment cost, and They represent the weights of the expected total number of successful transmissions and the deployment cost respectively; , represents the cost of deploying a drone, represents the total deployment budget, Indicates the number of deployed drones; For the objective function Solve and find the value of n that maximizes the value of the objective function.

9. The method for deploying a drone according to claim 8, characterized in that: Use the three-part search algorithm to solve the objective function The solution includes the following steps: 1) Find the upper limit of the number of drones , It is a floor rounding operation; 2) Search for the left endpoint of the interval L The initial value is 0, the right endpoint R The initial value is ; 3) Repeat 4) and 5) until exit; 4) The left third point of the search interval is , the right third of the search interval is ; 5) If , then let ,like , then let ,like And the search interval contains more than three points, then let and ,like And the search interval contains two or fewer points, then the optimal number of output drones is , and exit.

10. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method as described in any one of claims 1 to 9 are performed.

11. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of any method described in claims 1 to 9 are implemented.

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