Unmanned aerial vehicle flight mission planning method and device with thermal stealth function

Abstract

The invention discloses an unmanned aerial vehicle flight mission planning method with a thermal stealth function, and the method comprises the steps: collecting the state of an unmanned aerial vehicle, obtaining the task weight, initial resources, speed and other information of the unmanned aerial vehicle in a cluster, and calculating the flight time of each unmanned aerial vehicle, the task load of each unmanned aerial vehicle, and the path cost of each unmanned aerial vehicle. And finally, the path of each unmanned aerial vehicle in the cluster is optimized through a genetic algorithm. The method provided by the invention comprehensively considers the resources of the unmanned aerial vehicle, the stealth performance and the risk that the unmanned aerial vehicle may be detected and found by enemies, is convenient to calculate, facilitates the measurement or estimation of parameters, can quickly optimize the task planning of the unmanned aerial vehicle, saves energy for the unmanned aerial vehicle to execute various complex tasks, improves the service life, and provides a technical guarantee for the working efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) mission planning, and in particular to a method and device for planning a UAV flight mission with a thermal stealth function. Background Art

[0002] UAV technology has been widely used in military, reconnaissance, logistics and other fields. With the development of technology, UAVs with stealth capabilities have received more and more attention, especially the ability to reduce detection by enemy radar or infrared detection systems during mission execution. Traditional UAV mission planning methods usually rely on fixed flight distances, mission scopes and mission restrictions, and cannot fully utilize the concealment advantages of UAVs with thermal stealth capabilities when performing missions.

[0003] In the existing technology, the flight path planning usually does not take into account the dynamic relationship between the mission execution time, mission load and path optimization of each drone in the drone cluster, especially when the drone has thermal stealth function, the correlation of these factors will directly affect the execution effect and concealment of the mission. Therefore, there is an urgent need for a mission planning method that can comprehensively consider the mission execution time, mission load and flight path optimization of each drone in the drone cluster. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a method and device for planning flight missions of a UAV with thermal stealth function, which can comprehensively consider the stealth performance, mission execution time and mission load of the UAV during the mission execution, and thus optimize the flight path of the UAV.

[0005] Technical solution: To achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A flight mission planning method for UAVs with thermal stealth function is proposed. Based on the thermal stealth characteristics of UAVs, the path cost and risk function of UAVs are considered, the fitness function of the genetic algorithm is improved, and the flight path of cluster UAVs is optimized using the improved genetic algorithm. The improved fitness function F is:

[0007]

[0008] Among them, J i is the total path cost of the i-th UAV, D i (t) is the distance between the i-th UAV and the enemy radar detection area at time t, and n is the number of UAVs. The genetic algorithm path optimization steps are as follows:

[0009] (1) The path point coordinates of all individual drones in the drone group and the distances of all individual drones from the enemy detection radar are encoded into a gene sequence, and an initial population is randomly generated, and the initial value of the evolution counter k is set to 1;

[0010] (2) Calculate the fitness function value of each individual according to the improved fitness function F;

[0011] (3) Perform selection, crossover, and mutation operations on the population based on the obtained fitness to obtain a new generation of population;

[0012] (4) Let k = k + 1; determine whether k is less than the maximum evolutionary number g. If so, go to step (3) (4) to continue the evolution; otherwise, go to step (5);

[0013] (5) Find the individual with the largest fitness function in the population, convert the code into the coordinates of the drone path points, and construct the optimal path.

[0014] As a preferred solution of the present invention, considering the mission execution time, mission load and drone stealth performance, the following mathematical model of the drone path cost is established:

[0015]

[0016] Among them, R i is the resource parameter of the i-th UAV, d i is the distance between the i-th UAV and the enemy detection radar, α and β are weight coefficients, L i is the mission load of the i-th UAV, T i is the mission execution time of the i-th UAV.

[0017] As a preferred solution of the present invention, the method for calculating the mission execution time of the drone is:

[0018]

[0019] Among them, P opt is the total length of the cluster path, v i (t) is the speed of the i-th UAV at time t, D i (t) is the relative position of the i-th UAV and the enemy radar detection area at time t, and k is the stealth weight coefficient.

[0020] As a preferred solution of the present invention, the method for calculating the mission load of the UAV is:

[0021]

[0022] In the formula, w i is the mission weight of the i-th UAV, f(Si )=1-S i is the mission execution efficiency correction function of the i-th UAV, S i is the thermal stealth state of the i-th UAV, when S i = 0, it means that the target temperature of the drone is completely consistent with the ambient temperature, and the thermal stealth effect is the strongest; when S i =1, indicating that the difference between the target temperature and the ambient temperature is the largest, and the thermal stealth effect is the worst.

[0023] A UAV flight mission planning device based on the above method is also provided, the device comprising:

[0024] The model building module is used to build a mathematical model of the path cost of the UAV by considering the mission execution time, mission load and stealth performance of the UAV;

[0025] The fitness function improvement module is used to improve the fitness function of the genetic algorithm by considering the path cost of the UAV and the distance between the UAV and the enemy detection radar;

[0026] The path planning module is used to optimize the flight path of cluster UAVs using a genetic algorithm with an improved fitness function.

[0027] A computer-readable storage medium storing one or more programs is also provided. The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method as described above.

[0028] An electronic device is also provided, comprising one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method as described above.

[0029] Beneficial effects: The flight mission planning method of a UAV with thermal stealth function provided by the present invention can effectively improve the concealment and task execution efficiency of the UAV cluster when performing tasks by calculating the path cost through the task execution time and task load and finally optimizing it. Compared with the prior art, the present invention can better adapt to the dynamic environmental factors in the UAV mission planning, optimize the flight path and maximize the stealth performance of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of the method of an embodiment of the present invention, showing the various steps of UAV flight mission planning. DETAILED DESCRIPTION

[0031] In order to make the content of the present invention clearer, the present invention is described in detail below in conjunction with specific embodiments.

[0032] Engineering example: Assume that there is a drone swarm consisting of three drones with thermal stealth capabilities, performing a reconnaissance mission. The mission requires the drones to get as close to the target area as possible, while maintaining stealth during the reconnaissance process and saving resources (such as power) as much as possible.

[0033] The following is a flight mission planning method for a UAV with thermal stealth function proposed by the present invention:

[0034] Drone parameters:

[0035] Drone 1: mission weight W1=1.0, resource parameter R1=80 units, initial speed v1(t)=10m / s, initial value of stealth state: S1=0.85.

[0036] Drone 2: mission weight W2=1.2, resource parameter R2=100 units, initial speed v2(t)=12m / s, initial value of stealth state: S2=0.90.

[0037] Drone 3: Mission weight W3=0.9, resource parameter R3=70 units, initial speed v3(t)=8m / s, initial value of stealth state: S3=0.80.

[0038] (1) Calculate the task execution time T of each drone in the drone cluster i

[0039]

[0040] where v i (t) is the speed of the drone; h i (t) is the path concealment, which is determined by the relative position of the UAV and the enemy radar detection area; k = 0.5.

[0041] Assuming that the flight distance of the drone cluster is 200 meters, and the path concealment function of each drone is h1(t) = 1.2, h2(t) = 1.0, h3(t) = 1.5, we can calculate the flight time of each drone:

[0042] Drone 1:

[0043]

[0044] Task execution time T1 = 16.00s.

[0045] Drone 2:

[0046]

[0047] Task execution time T1 = 18.87s.

[0048] Drone 3:

[0049]

[0050] Task execution time T3 = 22.86s.

[0051] (2) Calculate the mission load of each drone in the drone cluster

[0052] According to the task execution time T calculated in the path optimization model i , we then calculate the mission load L of each drone in the drone cluster:

[0053]

[0054] where w i is the task weight; T i is the task execution time; R i is the resource parameter of the UAV; f(S i ,C i ) is the task efficiency correction function; γ = 0.8.

[0055] Assume that the task efficiency correction function f(S i ,C i )=1=S i ; Mission load L of each UAV i The calculation is as follows:

[0056] Drone 1:

[0057]

[0058] Drone 2:

[0059]

[0060] Drone 3:

[0061]

[0062] The task loads L1=0.356, L2=0.288, and L3=0.438 reflect the combination of resource consumption and task complexity of each UAV when performing a task.

[0063] (3) Calculate the path cost of each drone in the drone cluster

[0064]

[0065] Where P i(t) is the stealth performance of UAV i; d i (t) is the distance between the UAV and the target; α = 0.6; β = 0.4.

[0066] Calculate the path cost for each drone:

[0067] Drone 1:

[0068]

[0069] Drone 2:

[0070]

[0071] Drone 3:

[0072]

[0073] (4) Optimize the flight path of each drone in the drone swarm.

[0074] The genetic algorithm is used to replan the path so that the fitness function F is maximized. At this time, the calculated value of the fitness function is:

[0075]

[0076] Therefore, it is necessary to use genetic algorithms to replan and finally obtain the path plan with the highest fitness and obtain the optimal path for the UAV.

[0077] Specifically, the genetic algorithm path optimization steps are as follows:

[0078] (1) The path point coordinates of all individual drones in the drone group and the distances of all individual drones from the enemy detection radar are encoded into a gene sequence, and an initial population is randomly generated, and the initial value of the evolution counter k is set to 1;

[0079] (2) Calculate the fitness function value of each individual according to the improved fitness function F;

[0080] (3) Perform selection, crossover, and mutation operations on the population based on the obtained fitness to obtain a new generation of population;

[0081] (4) Let k = k + 1; determine whether k is less than the maximum evolutionary number g. If so, go to step (3) (4) to continue the evolution; otherwise, go to step (5);

[0082] Find the individual with the largest fitness function in the population, convert the code into the coordinates of the drone path points, and construct the optimal path.

[0083] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the use. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the use methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

[0084] The present invention also provides a UAV flight mission planning device based on the above method, the device comprising:

[0085] The model building module is used to build a mathematical model of the path cost of the UAV by considering the mission execution time, mission load and stealth performance of the UAV;

[0086] The fitness function improvement module is used to improve the fitness function of the genetic algorithm by considering the path cost of the UAV and the distance between the UAV and the enemy detection radar;

[0087] The path planning module is used to optimize the flight path of cluster UAVs using a genetic algorithm with an improved fitness function.

[0088] The technical solution of the above-mentioned UAV flight mission planning device is similar to the technical solution of the above-mentioned UAV flight mission planning method, which will not be repeated here.

[0089] Based on the same technical solution, the present invention also provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, characterized in that when the instructions are executed by a computing device, the computing device executes the UAV flight mission planning method with thermal stealth function as described above.

[0090] Based on the same technical solution, the present invention also provides an electronic system, comprising one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the UAV flight mission planning method with thermal stealth function as described above.

[0091] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

Claims

1. A flight mission planning method for an unmanned aerial vehicle with thermal stealth function, characterized in that: Based on the thermal stealth characteristics of UAVs, the UAV path cost and the distance between the UAV and the enemy detection radar are considered, and the fitness function of the genetic algorithm is improved. The flight path of the swarm UAV is optimized using the improved genetic algorithm. The improved fitness function F is: Among them, J i is the total path cost of the i-th UAV, D i (t) is the distance between the i-th UAV and the enemy radar detection area at time t, and n is the number of UAVs.

2. The flight mission planning method according to claim 1, characterized in that: The steps of optimizing the flight path of swarm drones using the improved genetic algorithm include: (1) The path point coordinates of all individual drones in the drone group and the distances of all individual drones from the enemy detection radar are encoded into a gene sequence, and an initial population is randomly generated, and the initial value of the evolution counter k is set to 1; (2) Calculate the fitness function value of each individual according to the improved fitness function F; (3) Perform selection, crossover, and mutation operations on the current population based on the obtained fitness to obtain a new generation of population; (4) Let k = k + 1; determine whether k is less than the maximum evolutionary generation g. If so, return to step (3); otherwise, proceed to step (5); (5) Find the individual with the largest fitness function in the current population, convert the code corresponding to the individual into a drone path point, and thus generate the optimal path.

3. The flight mission planning method according to claim 1, characterized in that: Considering the mission execution time, mission load and UAV stealth performance, the following mathematical model of the UAV path cost is established: Among them, R i is the resource parameter of the i-th UAV, d i is the distance between the i-th UAV and the enemy detection radar, α and β are weight coefficients, L i is the mission load of the i-th UAV, T i is the mission execution time of the i-th UAV.

4. The flight mission planning method according to claim 3, characterized in that: The calculation method of the UAV's mission execution time is: Among them, P opt is the total length of the cluster path, v i (t) is the speed of the i-th UAV at time t, D i (t) is the relative position of the i-th UAV and the enemy radar detection area at time t, and k is the stealth weight coefficient.

5. The flight mission planning method according to claim 3, characterized in that: The calculation method of the UAV's mission load is: In the formula, w i is the mission weight of the i-th UAV, f(S i )=1-S i is the mission execution efficiency correction function of the i-th UAV, S i is the thermal stealth state of the i-th UAV.

6. A UAV flight mission planning device based on the method as claimed in any one of claims 1 to 5, characterized in that: The device comprises: The model building module is used to build a mathematical model of the path cost of the UAV by considering the mission execution time, mission load and stealth performance of the UAV; The fitness function improvement module is used to improve the fitness function of the genetic algorithm by considering the path cost of the UAV and the distance between the UAV and the enemy detection radar; The path planning module is used to optimize the flight path of cluster UAVs using a genetic algorithm with an improved fitness function.

7. A computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, characterized in that: When the instructions are executed by a computing device, the computing device is caused to perform the method according to any one of claims 1 to 5.

8. An electronic device, characterized in that: The method comprises one or more processors, one or more memories and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing the method as claimed in any one of claims 1 to 5.

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