A multi-unmanned aerial vehicle control method based on digital twinning

Abstract

The application provides a multi-unmanned aerial vehicle control method based on digital twinning, considers radar threats, attack object threats and terrain threats when generating a flight path of an unmanned aerial vehicle, reduces the interference of the radar threats, the attack object threats and the terrain threats on the movement of the unmanned aerial vehicle, and improves the success rate of the unmanned aerial vehicle in completing a task. The relevant functions of the radar threats, the attack object threats, the terrain threats and energy consumption costs are determined through multi-attribute utility theory (MAUT), the energy consumption costs and the relevant threats are comprehensively evaluated, and thus a suitable flight path is screened out. When a target function is determined, the utility scores of different flight paths are compared, and the best flight path for executing different tasks is screened out. The flight path can not only avoid threats to the greatest extent, but also ensure that the unmanned aerial vehicle reaches a task area within a specified time and effectively executes a task.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) path planning technology, and in particular to a multi-UAV control method based on digital twins. Background Technology

[0002] The widespread application of drones can be traced back to multiple fields. When drones perform missions, considerations such as path planning, safety, and efficiency are involved. Transmission-based drone systems face problems such as insufficient information and slow response, making it difficult to adjust in rapidly changing environments and achieve efficient path planning. Leveraging the advantages of virtual-physical interconnection, digital twin technology is increasingly being integrated with drones. By constructing virtual drone models to simulate the behavior of physical drones throughout their entire lifecycle, their status can be monitored, their mission processes optimized, and their autonomous mission capabilities improved. However, existing flight paths generated between virtual and physical drones do not consider external threats, resulting in low flight path safety and consequently, a low success rate for drone mission completion. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a multi-UAV control method based on digital twins, which considers radar threats, target threats, and terrain threats when generating the flight path of the UAV, reduces the interference of radar threats, target threats, and terrain threats on the movement of the UAV, and improves the success rate of the UAV in completing its mission.

[0004] To solve the above-mentioned technical problems, the technical solution used in this invention is as follows:

[0005] The present invention discloses a multi-UAV control method based on digital twins, comprising the following steps:

[0006] S1. Preset drone path planning standards and generate one or more flight paths according to different tasks to be performed.

[0007] S2. Compare the utility scores of the flight paths corresponding to the same task to be executed, and set the flight path with the highest utility score as the initial flight path of the current task to be executed.

[0008] S3. Establish a digital twin platform for drones, constructing a twin data layer, a virtual layer, a user layer, and a service layer. The twin data layer collects historical flight data of drones; a virtual model in the virtual layer corresponds to a drone.

[0009] S4. The virtual layer optimizes the flight path and transmits the optimized flight path to the service layer so that the UAV can move along the optimized flight path.

[0010] S5. Synchronize the current flight data of the drone in the digital twin platform and monitor the flight status of the drone in real time through the virtual layer.

[0011] Preferably, S1 specifically includes the following models:

[0012] S1.1, Preset radar threat probability, target threat probability, and terrain threat model;

[0013] S1.2, Preset energy consumption cost function, and determine the radar threat cost, target threat cost and terrain threat cost in the flight path through the energy consumption cost function;

[0014] S1.3 Calculate the UAV flight path using the objective function v(x) = ω R cost R +ω M cost M +ω E cost E +ω L cost L ;where ω R+ ω M+ ω E+ ω L＝1 cost R Cost of radar threat M To combat material threats, cost E Cost due to terrain threat L Energy consumption cost; ω R As a weight for the cost of radar threats, ω M As a weighting of the cost of combating material threats, ω E As a weight for the cost of terrain threats, ω L The weighting of energy consumption costs.

[0015] Preferably, the energy consumption cost of the preset drone is positively correlated with its flight length; the energy consumption cost function is: Where L i Let i be the length of flight segment i.

[0016] Preferably, the radar threat probability

[0017]

[0018] R is the unidirectional propagation distance of electromagnetic waves; R h R is the maximum unidirectional propagation distance of the electromagnetic wave at height h. 4 It is the fourth power of the unidirectional propagation distance of electromagnetic waves; Let f be the fourth power of the maximum unidirectional propagation distance of the electromagnetic wave at height h.

[0019] The formula for calculating the cost of radar threats is as follows:

[0020] m represents a uniformly divided flight segment, Rka For radar threat, L i For the flight segment, ka is the radar threat center, and d is the radar threat center. ka The distance n between each split point and ka R The total number of radar threat points is represented by U, where U represents different drones.

[0021] Preferably, the threat probability of the strike object

[0022]

[0023] RM h =RM max -kb|h-0.5H|、Kb=(RM max +RM min ) / 0.5H;RM max RM is the effective attack range of the target. min Where RM is the minimum attack radius, H is the attack height, and RM is the minimum attack radius. h Let RM be the attack radius of the target at height h, and eM be the horizontal attack distance. -1 This represents the probability of attacking an object.

[0024] The formula for calculating the cost of combating material threats is as follows.

[0025]

[0026] m represents a uniformly divided flight segment, M kb To combat material threats, L i For the flight segment, KB represents the center of the attack target threat, and d represents the target target threat center. kb The distance n between each split point and kb Rm The total number of threat points; U represents different drones.

[0027] Preferably, a virtual model and a drone authenticate each other and communicate securely using a unique device key.

[0028] Preferably, a virtual model and a drone communicate using MQTT.

[0029] Preferably, S5 is followed by S6; S6: If the physical environment changes, the flight path is regenerated, and then the current flight data of the UAV on the current flight path is obtained, and the current flight path is optimized through the virtual layer; so that the UAV moves along the optimized flight path.

[0030] The advantages of the handle connection structure described in this invention compared to existing technologies are mainly reflected in the following aspects: When driving UAVs to perform tasks, N UAVs take off from different areas and head to different task areas to perform tasks. Different flight paths are planned for different tasks. After determining the target, the flight path generation considers radar threats, target threats, and terrain threats, reducing their interference with UAV movement and improving the success rate of task completion. The correlation function between radar threats, target threats, terrain threats, and energy consumption costs is determined using Multi-Attribute Utility Theory (MAUT). A comprehensive evaluation of energy consumption costs and related threat data is then performed to select a suitable flight path. When determining the objective function, the utility scores of different flight paths are compared to select the optimal flight path for performing different tasks. This flight path not only avoids threats to the greatest extent but also ensures that the UAVs arrive at the task area within the specified time and effectively perform the task.

[0031] By using a digital twin platform to calculate the radar threat probability and the target threat probability for the corresponding flight paths of different tasks performed by the planning department, the threat probability can be determined, enabling the UAV to evade radar and target objects; by using a terrain threat model to determine the threat boundary, the UAV can avoid terrain obstacles. Attached Figure Description

[0032] The above and other objects, features, and advantages of the invention will become clearer through a more detailed description of the preferred embodiments illustrated in the accompanying drawings. The same reference numerals denote the same parts throughout the drawings, and the drawings are not intentionally drawn to scale with actual dimensions; the focus is on illustrating the gist of the invention.

[0033] Figure 1 This is a schematic diagram of the control system in this invention.

[0034] Figure 2 This is a flowchart of the control method in this invention.

[0035] Figure 3 This is a schematic diagram of the flight path in this invention. Detailed Implementation

[0036] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention. In this embodiment, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the present invention.

[0037] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to and integrated with the other element, or there may be an intervening element present. The terms "mounted," "one end," "the other end," and similar expressions used in this invention are for illustrative purposes only.

[0038] like Figure 1-3 As shown, a multi-UAV control system based on digital twins includes a digital twin platform. The digital twin platform includes a virtual layer, a physical layer, a server layer, a user layer, and a twin data layer connected by signals. The physical layer consists of the UAVs and the physical environment, and the virtual layer consists of a virtual model and a virtual environment. The twin data layer is used to collect historical flight data from the physical layer. The virtual layer is used to analyze the historical flight data and optimize the flight path. The physical layer moves along the optimized flight path. During the movement of the physical layer along the optimized flight path, the twin data layer synchronizes the current flight data of the physical layer to the virtual layer. The user layer detects the dynamics of the physical layer through the virtual layer. If it is necessary to actively control the flight of the UAVs, the user layer outputs control signals to actively control the flight of the UAVs.

[0039] A multi-UAV control method based on digital twins includes the following steps:

[0040] S1. Preset UAV path planning standards to generate one or more flight paths for different tasks to be performed; specifically including S1.1-S1.3.

[0041] S1.1, Preset radar threat probability, target threat probability, and terrain threat model.

[0042] S1.2 Preset energy consumption cost function to determine the radar threat cost, target threat cost and terrain threat cost in the flight path.

[0043] S1.3 Calculate the UAV flight path using the objective function v(x) = ω Rcost R +ω M cost M +ω E cost E +ω L cost L ;where ω R+ ω M+ ω E+ ω L＝1 cost R Cost of radar threat M To combat material threats, cost E Cost due to terrain threat L Energy consumption cost; ω R As a weight for the cost of radar threats, ω M As a weighting of the cost of combating material threats, ω E As a weight for the cost of terrain threats, ω L The weighting is applied to energy consumption costs. In this embodiment, a threshold is preset for the objective function v(x), and paths where v(x) is less than the threshold are designated as flight paths, thereby reducing the impact of radar threats, attack threats, terrain threats, and energy consumption costs on the UAV. By presetting the threshold for the objective function v(x), multiple flight paths are generated for different tasks to be performed.

[0044] S2. Compare the utility scores of the flight paths corresponding to the same task to be executed, and set the flight path with the highest utility score as the initial flight path for the current task to be executed. In this embodiment, comparing the utility scores of the flight paths corresponding to the same task to be executed specifically means: comparing the objective function v(x) of the flight paths corresponding to the same task to be executed, and setting the flight path with the smallest objective function v(x) as the initial flight path; the smaller v(x) is, the smaller the impact of radar threat, attack threat, and terrain threat on the UAV.

[0045] S3. Establish a digital twin platform for the UAV, collect historical flight data of the UAV, and construct a virtual layer, in which a virtual model corresponds to a UAV. In a preferred embodiment, the flight data includes flight trajectory, flight speed, flight altitude, flight attitude, UAV position, and environmental parameters; collecting flight data through the twin data layer is an existing technology.

[0046] S4. The virtual layer optimizes the initial flight path and transmits the optimized flight path to the UAV. In a preferred embodiment, the virtual layer optimizes the flight path using a model predictive control algorithm.

[0047] S5. A virtual model and a drone authenticate and communicate securely through a unique device key; the drone's current flight data is synchronized in the digital twin platform, and the drone's flight status is monitored in real time through the virtual layer.

[0048] S6. If the physical environment changes, the flight path is regenerated. Then, the current flight data of the UAV on the current flight path is obtained, and the current flight path is optimized through the virtual layer, so that the UAV moves along the optimized flight path.

[0049] The above method involves N drones taking off from different areas and heading to different mission areas to perform tasks. Different flight paths are planned for different mission requirements. After the target is determined, the flight path generation considers radar threats, target threats, and terrain threats to reduce their interference with drone movement and improve the success rate of mission completion. The Multi-Attribute Utility Theory (MAUT) is used to determine the correlation functions of radar threats, target threats, terrain threats, and energy costs. A comprehensive evaluation of energy costs and related threat data is then performed to select suitable flight paths.

[0050] When determining the objective function, the optimal flight path for performing different tasks is selected by comparing the utility scores of different flight paths. This flight path not only avoids threats to the greatest extent possible, but also ensures that the UAV reaches the mission area within the specified time and effectively performs the mission.

[0051] By using a digital twin platform to calculate the radar threat probability and the target threat probability for the corresponding flight paths of different tasks performed by the planning department, the threat probability can be determined, enabling the UAV to evade radar and target objects; by using a terrain threat model to determine the threat boundary, the UAV can avoid terrain obstacles.

[0052] Energy consumption is related to the power consumption from the starting point to the destination. In a preferred embodiment, the drone's altitude and speed are constant, and the drone's energy cost is positively correlated with the flight length; the energy cost function is... Where L i Let i be the length of flight segment i.

[0053] In a preferred embodiment, the radar threat probability

[0054]

[0055] R is the unidirectional propagation distance of electromagnetic waves; R h R is the maximum unidirectional propagation distance of the electromagnetic wave at height h. 4 It is the fourth power of the unidirectional propagation distance of electromagnetic waves; R is the fourth power of the maximum one-way propagation distance of the electromagnetic wave at height h. Since the electromagnetic waves used by radar propagate in a straight line, and the energy loss of the transmitted electromagnetic waves after reflection from the target is equal to the fourth power of the one-way propagation distance R... 4 The probability of radar detecting a target is inversely proportional to the probability of a target, which is related to the Poisson distribution. The radar threat probability can then be calculated using this formula.

[0056] The formula for calculating the cost of radar threats is as follows:

[0057]

[0058] m represents a uniformly divided flight segment, R ka For radar threat, L i For the flight segment, ka is the radar threat center, and d is the radar threat center. ka The distance n between each split point and ka R Let U be the total number of radar threat points and U be the different UAVs. When a UAV moves along its flight path, it is necessary to consider whether the UAV is in a threat area. The flight segment is divided into m parts to determine the impact of the threat center on the UAV, thereby determining the radar threat cost.

[0059] In a preferred embodiment, the probability of an object attacking a drone follows a Poisson distribution with respect to the horizontal attack distance R, and the probability of the object attacking the drone is eM. -1 The striking object is launched outward from the striking object launching device as the center.

[0060] The probability of the attacking object

[0061]

[0062] RM h =RM max -kb|h-0.5H|、Kb=(RM max +RM min ) / 0.5H;RM max RM is the effective attack range of the target. min Where RM is the minimum attack radius, H is the attack height, and RM is the minimum attack radius. h Let RM be the attack radius of the target at height h, and eM be the horizontal attack distance. -1 The probability of an attack by the target is given; to avoid the drone being attacked by the target while it is in motion, the attack range of the target at altitude h is determined, thereby determining the threat cost of the target.

[0063] The formula for calculating the cost of combating material threats is as follows.

[0064]

[0065] m represents a uniformly divided flight segment, M k To combat material threats, L i For the flight segment, KB represents the center of the attack target threat, and d represents the target target threat center. kb The distance n between each split point and kb Rm The total number of threat points; U represents different drones.

[0066] In a preferred embodiment, the terrain threat cost is determined by a terrain threat model, which is established based on terrain elevation data and terrain relief data. The methods for setting up both the terrain threat cost and the terrain threat model are existing technologies and will not be described in detail here.

[0067] Reference Figure 3 The following explains the flight path generated in S1.3:

[0068] A1, A2, A3, and A4 represent drones performing different tasks; the circular areas in the diagram represent radar threats, strike threats, or terrain threats; drones reduce radar threats, strike threats, and terrain threats while also reducing energy costs to shorten the distance between the starting point Q and the target point.

[0069] Table 2 shows the flight path planning data for UAVs A1, A2, A3, and A4:

[0070]

[0071] Table 2

[0072] In the above S3, the drone includes existing drones, and the specific structure of the drone will not be described here; the drone is connected to the service layer by signal, and the drone receives instructions from the service layer to adjust its flight actions. The drone is equipped with sensors, which are used to collect flight trajectory data, flight speed data, flight altitude data, flight attitude data, drone position data, and environmental parameter data.

[0073] The virtual model comprises more than one part, and the shape and appearance of each part are adapted to the shape and appearance of the various parts of the drone, so that the shape of the virtual model is similar to the shape of the drone entity.

[0074] In a preferred embodiment, the virtual model is developed using the Unity3D physics engine platform, and simulation components are configured to simulate the functions of a physical drone. The virtual model developed using the Unity3D physics engine platform can simulate the drone's lateral linear motion, longitudinal linear motion, vertical linear motion, roll rotation, pitch rotation, and yaw rotation. A collider component is also included to simulate the effect of the drone colliding with obstacles in the environment. The Unity3D physics engine is used as the representation of the virtual layer, enabling interaction between the virtual layer and the physical layer. Furthermore, based on Euler's equations, the motion characteristics in the roll, pitch, and yaw directions are analyzed, and mathematical equations describe the lateral, longitudinal, and vertical motions. The methods for confirming the lateral, longitudinal, and vertical motions are existing technologies and will not be elaborated upon here.

[0075] In S5, in a preferred embodiment, a virtual model and a drone communicate using MQTT (Message Queuing Telemetry Transport).

[0076] Table 1 shows the delay time comparison for different communication network methods:

[0077] Communication network methods Number of drones Delay time (ms) Data packet size (bytes) WIFI-4G 30 65.7-78.8 124 WIFI-5G 30 38.2-45.3 124 cloud services 30 16.4-26.7 124

[0078] Table 1

[0079] Referring to Table 1, by using message queue telemetry transmission and message serialization, the size of data packets can be reduced and communication latency can be lowered.

[0080] Each drone has a unique ID, and the ID of the virtual model corresponds one-to-one with the ID of the drone. This facilitates the differentiation of different drone entities, and through the ID association, the physical drone corresponding to the virtual entity can be accurately located. A virtual model and a drone use a unique device key for authentication and secure communication, ensuring a tight connection between the physical and virtual layers. This allows the virtual layer to obtain and update the drone's status information in real time. Using device keys for authentication and secure communication is existing technology and will not be elaborated upon here.

[0081] The service layer stores configuration data for each virtual drone, including but not limited to flight parameters, sensor data, and communication protocols. This configuration data can be adjusted according to actual needs, facilitating subsequent upgrades and optimizations.

[0082] S5 further includes, in a preferred embodiment, the user layer uses a VR device to signal-connect with the virtual layer, allowing the user layer operator to directly interact with the virtual model. If active control of the drone's flight is required, the user layer outputs control signals to actively control the drone's flight. By connecting the user layer and the virtual layer through the VR device, the user can directly interact with the virtual model and control the drone through manipulation of the virtual model. When real-time aircraft data from the physical layer is synchronized to the virtual layer, the user layer monitors the drone's flight status through the VR device and can manipulate the virtual model. The virtual model then sends control signals through the service layer to the physical layer, thereby enabling the user layer to actively control the drone. The VR device is prior art and will not be described in detail here.

[0083] In this specification, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0084] In the description of this specification, the references to terms such as "preferred embodiment," "another embodiment," "other embodiment," or "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0085] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A multi-UAV control method based on digital twins, characterized in that: Includes the following steps: S1. Preset drone path planning standards and generate one or more flight paths according to different tasks to be performed; S1 specifically includes the following models: S1.1, Preset radar threat probability, target threat probability, and terrain threat model; S1.2, Preset energy consumption cost function, and determine the radar threat cost, target threat cost and terrain threat cost in the flight path through the energy consumption cost function; S1.3 Calculate the UAV flight path using the objective function v(x) = ω R cost R +ω M cost M +ω E cost E +ω L cost L ;where ω R +ω M +ω E +ω L＝1 cost R Cost of radar threat M To combat material threats, cost E Cost due to terrain threat L Energy consumption cost; ω R As a weight for the cost of radar threats, ω M As a weighting of the cost of combating material threats, ω E As a weight for the cost of terrain threats, ω L The weighting of energy consumption costs; S2. Compare the utility scores of the flight paths corresponding to the same task to be executed, and set the flight path with the highest utility score as the initial flight path of the current task to be executed. S3. Establish a digital twin platform for drones, constructing a twin data layer, a virtual layer, a user layer, and a service layer. The twin data layer collects historical flight data of drones; a virtual model in the virtual layer corresponds to a drone. S4. The virtual layer optimizes the flight path and transmits the optimized flight path to the service layer so that the UAV can move along the optimized flight path. S5. Synchronize the current flight data of the drone in the digital twin platform and monitor the flight status of the drone in real time through the virtual layer.

2. The multi-UAV control method based on digital twin according to claim 1, characterized in that: The energy consumption cost of the presupposed drone is positively correlated with its flight length; the energy consumption cost function is: Where L i Let i be the length of flight segment i.

3. The multi-UAV control method based on digital twins according to claim 2, characterized in that: The radar threat probability R is the unidirectional propagation distance of electromagnetic waves; R h R is the maximum unidirectional propagation distance of the electromagnetic wave at height h. 4 It is the fourth power of the unidirectional propagation distance of electromagnetic waves; Let f be the fourth power of the maximum unidirectional propagation distance of the electromagnetic wave at height h; The formula for calculating the cost of radar threats is as follows: m represents a uniformly divided flight segment, R ka For radar threat, L i For the flight segment, ka is the radar threat center, and d is the radar threat center. ka The distance n between each split point and ka R The total number of radar threat points is represented by U, where U represents different drones.

4. The multi-UAV control method based on digital twin according to claim 2, characterized in that: The probability of the attacking object RM h =RM max -kb|h-0.5H|、Kb=(RM max +RM min ) / 0.5H;RM max RM is the effective attack range of the target. min Where RM is the minimum attack radius, H is the attack height, and RM is the minimum attack radius. h Let RM be the attack radius of the target at height h, and eM be the horizontal attack distance. -1 This represents the probability of an attack. The formula for calculating the cost of attacking material threats is as follows: m represents a uniformly divided flight segment, M kb To combat material threats, L i For the flight segment, KB represents the center of the attack target threat, and d represents the target target threat center. kb The distance n between each split point and kb Rm The total number of threat points; U represents different drones.

5. The multi-UAV control method based on digital twin according to claim 1, characterized in that: A virtual model and a drone authenticate and communicate securely using a unique device key.

6. The multi-UAV control method based on digital twin according to claim 3, characterized in that: A virtual model and a drone communicate using MQTT.

7. The multi-UAV control method based on digital twin according to claim 1, characterized in that: S5 is followed by S6; S6. If the physical environment changes, the flight path is regenerated. Then, after obtaining the current flight data of the UAV on the current flight path, the current flight path is optimized through the virtual layer, so that the UAV moves along the optimized flight path.

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