A distributed unmanned aerial vehicle measurement and control service system and method

Abstract

The application discloses a distributed unmanned aerial vehicle (UAV) measurement and control service system and method, and belongs to the technical field of artificial intelligence. The system comprises N measurement and control sites which are deployed according to a deployment strategy, each measurement and control site provides a navigation function to a UAV entering an airspace of the measurement and control site according to a state of an obstacle position in the airspace, and the UAV flies according to the navigation function; the navigation function provided by the measurement and control site along a route from a starting position to a destination position of the UAV is continuous. The application can reasonably arrange the measurement and control sites, so that a total deployment state value function of a set number of measurement and control sites is the highest.

Description

Technical Field

[0001] This invention relates to a distributed unmanned aerial vehicle (UAV) telemetry and control service system and method, belonging to the field of artificial intelligence technology. Background Technology

[0002] Low-altitude Internet of Things (IoT) relies heavily on robust communication systems to operate effectively. Recently, there has been increasing interest in low-altitude communication solutions, particularly using low-Earth orbit (LEO) satellites to support connectivity in defined regional environments. LEO satellites have recently gained considerable attention due to their reduced development costs and the ability to expand LEO satellite networks to substantially minimize transmission latency while mitigating the limitations associated with terrestrial networks. However, limitations in the transmission power and processing capabilities of drones, as well as dead zones created by the curvature of the Earth or ground obstructions within satellite coverage, result in weak line-of-sight communication between drones and LEO satellites. Consequently, the reduced coverage of LEO satellite networks poses significant challenges to drone navigation. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a distributed UAV telemetry and control service system and method that enables communication without dead zones; and to optimize the location of a set number of telemetry and control stations, thereby saving costs.

[0004] To achieve the aforementioned objective, this invention provides a distributed unmanned aerial vehicle (UAV) telemetry and control service system, comprising a system based on a deployment strategy. Deployed N monitoring and control stations, deployment strategy Obtain it through the following steps: According to the pre-deployment strategy Select N telemetry and control stations for deployment, among which, , , , and According to the pre-deployment strategy Deployment of telemetry and control sites Communication coverage, throughput, and fairness; To be based on the pre-deployment strategy Deployment of monitoring and control stations Location ; Based on the pre-deployment strategy Get a set of data ; The pre-deployment strategy is calculated according to the following formula. Deployment status value function value: ; Function values ​​for K types of pre-deployment state values Sort rows and get the maximum value. ; judge Is it greater than or equal to the expected value? If it is greater than or equal to the expected value, output... Corresponding pre-deployment strategy As the optimal deployment strategy for telemetry and control sites Otherwise, assign N+1 to N, and then repeat the above steps until the desired value is reached.

[0005] To achieve the aforementioned objective, the present invention also provides a distributed unmanned aerial vehicle (UAV) telemetry and control service method, comprising: according to a deployment strategy N telemetry and control stations deployed; deployment strategy Obtain it through the following steps: According to the pre-deployment strategy Select N telemetry and control stations for deployment, among which, , , , and According to the pre-deployment strategy Deployment of telemetry and control sites Communication coverage, throughput, and fairness; To be based on the pre-deployment strategy Deployment of monitoring and control stations Location ; Based on the pre-deployment strategy Get a set of data ; The pre-deployment strategy is calculated according to the following formula. Deployment status value function value: ; Function values ​​for K types of pre-deployment state values Sort rows and get the maximum value. ; judge Is it greater than or equal to the expected value? If it is greater than or equal to the expected value, output... Corresponding pre-deployment strategy As the optimal deployment strategy for telemetry and control sites Otherwise, assign N+1 to N, and then repeat the above steps until the desired value is reached.

[0006] Compared with existing technologies, the distributed UAV telemetry and control service system and method provided by this invention have the following advantages:

[0007] 1. Utilize telemetry and control stations as relays to ensure seamless communication between low-altitude network equipment and low Earth orbit satellites; 2. Optimize the placement of a set number of telemetry and control stations to maximize the overall deployment status function of the set number of telemetry and control stations. Attached Figure Description

[0008] Figure 1 This is a flowchart of the deployment method of the monitoring and control station provided in the first embodiment of the present invention.

[0009] Figure 2 This is a block diagram of the control system of the unmanned aerial vehicle provided in the first embodiment of the present invention. Detailed Implementation

[0010] It should be noted that, below, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The advantages and features of the present invention, as well as the methods for achieving these advantages and features, will become clear from the accompanying drawings and the detailed embodiments described below.

[0011] However, the present invention is not limited to the embodiments disclosed below, and can be implemented in many different forms. This embodiment is only used to make the disclosure of the present invention more complete and to fully inform those skilled in the art of the present invention of the scope of the invention. The present invention is defined only by the scope of the claims.

[0012] While terms such as "first," "second," etc., are used to describe various elements, components, and / or parts, these elements, components, and / or parts are not limited by these terms. These terms are used only to distinguish one element, component, or part from other elements, components, or parts. Therefore, it is apparent that, within the technical spirit of this disclosure, the first element, first component, or first part mentioned below may also be a second element, second component, or second part, and the terminology used in this specification is for describing embodiments only and is not intended to limit this disclosure.

[0013] In this specification, unless otherwise specified in the text, the singular includes the plural. The use of "comprising" and / or "consisting of" in this specification does not exclude the presence or addition of one or more other structural elements, steps, actions, and / or components mentioned.

[0014] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Furthermore, terms defined in commonly used dictionaries shall not be interpreted ideally or excessively unless explicitly and specifically defined.

[0015] The low-altitude Internet of Things (IoT) provided by this invention includes a low Earth orbit (LEO) satellite and N telemetry and control (TT&C) stations. The LEO satellite operates at a constant speed and fixed altitude in a predefined orbital plane. The N TT&C stations are deployed at N locations within the airspace to facilitate communication between UAVs within the airspace. Simultaneously, the TT&C stations can provide meteorological information and obstacle information to UAVs entering their service area. The obstacles include flying objects entering the same area and objects fixed to the ground. The flying objects include other UAVs, small aircraft, and birds.

[0016] For simplicity, this invention assumes that low-Earth orbit satellites remain within their designated coverage areas without switching during the observation period, thereby ensuring continuous service within the low-altitude Internet of Things (IoT). This invention defines communication processes on discrete time slots t and t={1,2,…,T}, with the system dynamically operating to optimize network performance at each moment. In this invention, the total duration is represented by T, during which the low-altitude IoT consistently manages multiple communication and resource allocation tasks in each time slot within the originally allocated time frame T. Without loss of generality, this invention considers telemetry and control stations... Position in 3D space is defined as:

[0017] , In the formula, Indicates the telemetry and control station The latitude, longitude, and altitude coordinates. Similarly, drones... The flight position is represented as: .

[0018] First Embodiment

[0019] Figure 1 This is a flowchart of the deployment method of the monitoring and control station provided in the first embodiment of the present invention, as follows: Figure 1 As shown, the deployment method for distributed UAV telemetry and control stations provided in the first embodiment of the present invention includes: Step 1: Initialize the number of monitoring and control stations N; Step 2: Based on the pre-deployment strategy Select N telemetry and control stations for deployment, among which, , , , and According to the pre-deployment strategy Deployment of telemetry and control stations Communication coverage, throughput, and fairness; To be based on the pre-deployment strategy Deployment of telemetry and control stations Measurement and control stations Location ; ; Step 3: Based on the pre-deployment strategy Get a set of data ; Step 4: Calculate the kth pre-deployment strategy according to the following formula. Deployment status value function: ; Step 5: Function for K pre-deployed state values Sort by row to obtain deployment status. ; Step 6: Determine Is it greater than or equal to the expected value? If so, output the value. Corresponding pre-deployment strategy As the optimal deployment strategy for telemetry and control sites Otherwise, assign N+1 to N, and then repeat steps 2-6 until the desired value is reached.

[0020] In this invention, the monitoring and control station throughput for: , In the formula, To enter the telemetry and control station Drones in the airspace The bandwidth of the connection; , , For low Earth orbit satellites to tracking and control stations The transmission power; This refers to the altitude of the low Earth orbit satellite. For telemetry and control stations The altitude at which it is located; Noise power; M represents the average channel gain at a reference distance of 1m; M is the input to the telemetry and control station. The number of drones in the airspace; , , For the nth telemetry and control station The power; For measurement and control stations Location, To enter the telemetry and control station drones in the airspace The location.

[0021] In the first embodiment, the total throughput of the low-orbit satellites is: .

[0022] In this invention, the nth monitoring and control station Fairness of communication for: , In the formula, M is the entry point to the telemetry and control station. The number of drones in the airspace; For measurement and control stations and drones At time t, connectivity is 1 if connected, otherwise 0. , For coefficients; To enter the telemetry and control station relay drones The bandwidth of the connection;

[0023] In this invention, the overall communication fairness of low-Earth orbit satellites is: .

[0024] In this invention, the monitoring and control station Communication coverage for: , In the formula, For the nth telemetry and control station and entering the telemetry and control station relay drones At time t, connectivity is 1 for connectivity and 0 otherwise.

[0025] In this invention, the total communication coverage of low-Earth orbit satellites is: .

[0026] The first embodiment of the present invention achieves the following beneficial effects through the above technical solution: the telemetry and control stations, through dense or flexible deployment, can achieve seamless coverage of a wide area, ensuring the continuity and real-time nature of data acquisition; thereby maximizing the total deployment status value function of the set number of telemetry and control stations.

[0027] In the first embodiment, one or more of the N telemetry and control stations can be UAV base stations. When a UAV base station is used as a telemetry and control station, the UAV base station is preferably a multi-rotor UAV. The multi-rotor can hover at a set position to provide services to UAVs entering its airspace.

[0028] Each telemetry and control station sends signals to drones entering its airspace based on the location of obstacles within its controlled airspace. Provide navigation functions to enable drones Flying based on navigation functions; Unmanned Aerial Vehicle The navigation functions provided by the telemetry and control stations along the route from the starting position to the destination position are continuous. In this invention, obstacles include fixed objects fixed to the ground and other flying objects entering the same airspace. The flying objects include other manned and unmanned aircraft, birds, etc.; obstacles also include areas with restricted airspace.

[0029] In this invention, the navigation function includes the line integral Lyapunov guided curve function, and the UAV... Fly along this curve: , In the formula, Represents the constraint function; Indicates drone Location; Indicates coefficient; For drones At the position of the guide curve Speed ​​control commands at the location; Indicates drone Maximum permissible speed; For drones Speed ​​commands.

[0030] Navigation functions include pipe boundary repulsion functions: , In the formula, , , , The radius of the virtual tube; and It has two parameters; For drones The distance from the virtual tube centered on the guide curve.

[0031] In this invention, the navigation function includes a collision avoidance function: , In the formula, , , The radius of the virtual tube; and It has two parameters; For drones The distance between the position of and the position of obstacle j; , For coefficients; , Let j be the position of obstacle j. For drones The location.

[0032] The first embodiment achieves the following beneficial effects through the above technical solution: it provides a safe navigation path for UAVs in a three-dimensional environment.

[0033] Figure 2 The drone provided in the first embodiment of the present invention A block diagram of the control system. (For example...) Figure 2 As shown, drone The control system includes a terminal controller and an airborne controller. The airborne controller includes a communication unit and a flight controller. The communication unit receives control data frames from the terminal controller and navigation data frames sent from the telemetry and control station. The flight controller deframes the received control data frames to obtain control commands and derivative functions.

[0034] The airborne controller also includes a speed command generator, an error calculator, a reinforcement learning module, and a motor controller. The speed command generator generates speed control commands based on navigation functions provided by the telemetry and control station. , In the formula, Represents the norm; Indicates the time period The arc; Indicates in Online location Tangential velocity vector; It is the set of all obstacles.

[0035] The error calculator is based on the speed control command. and drones measured speed Generate tracking error signal The reinforcement learning module is based on the error signal Generation state Generate control strategies based on state. In the formula, This is the control parameter vector for the speed controller; the motor controller controls the drive drone based on the control parameter vector. Motor drive signal I iM (t).

[0036] The terminal controller communicates with the airborne controller via a channel provided by the telemetry and control (TT&C) station. The terminal controller also connects to the TT&C station and the central server via the same channel. The terminal controller runs an air navigation program. Users can input the UAV's starting point and destination, as well as its parameters, through the input unit on the air navigation program interface. The central server plans a path for the UAV based on the user-inputted starting point and destination and distributes it to multiple TT&C stations. These stations provide navigation functions to the UAV based on obstacle conditions within their controlled airspace. These navigation functions include Lyapunov curves, collision avoidance functions, and boundary repulsion functions. The terminal controller generates a visible path on the air navigation program interface based on these navigation functions for the user to view. It can also display the UAV's past flight paths, creating a flight trajectory. Past and future flight paths are displayed in different colors to differentiate them for the user.

[0037] In the first embodiment, the communication unit of the telemetry and control station, the UAV terminal controller, and the frame controller includes at least multiple power modules. These functional modules include a modulation module, a power amplification module, and a transceiver antenna module. A matching control network is provided between adjacent modules. , To match the parameter vectors of multiple controllable elements in the current k control network, The current measurement status, , The impedance of the current k is The current operating frequency of k is... Given the current bandwidth of k, The Q-value profile of the current k; Current adjustment instructions; For the reward function, Here are the parameters of the reward function, where, according to The process of selecting controllable components and forming a connection network, and matching adjacent modules within the connection network, includes: S1: Get the current state of network connection k. ; S2: Obtain control commands according to the following formula : ; S3: According to control commands Get the next k+1 pairs of states and get rewards ; S4: Calculate the cumulative return according to the following formula. , It is the learning rate; S5: Update according to the following formula ; .

[0038] In the first embodiment of the present invention, when the Q value is less than or equal to the threshold, It is positive if it is positive, otherwise it is zero.

[0039] In this invention, any one of the modulated module, power amplifier module, and transceiver antenna module can be independently replaced and upgraded, and adaptive impedance matching can be performed with the modules connected to it.

[0040] Second Embodiment

[0041] The second embodiment of the present invention only describes the contents that are different from those of the first embodiment; the contents that are the same will not be described again.

[0042] The telemetry and control station provided in the second embodiment of the present invention is also for swarms of drones. Provide a cluster cohesion attraction function: , In the formula, , , , The radius of the virtual tube; and It has two parameters; For drones Location and swarm of drones The distance of the location; , is a coefficient.

[0043] In the second embodiment, the drone The control system terminal includes a controller and an airborne controller. The airborne controller includes a communication unit and a flight controller. The communication unit receives control data frames from the terminal controller and navigation data frames sent from the telemetry and control station. The flight controller deframes the received control data frames to obtain control commands and derivative functions.

[0044] The airborne controller also includes a speed command generator, an error calculator, a reinforcement learning module, and a speed controller. The speed command generator generates speed control commands based on the curve integral Lyapunov guidance curve, collision avoidance function, and tube boundary repulsion function provided by the telemetry and control station. , In the formula, Represents the norm; Indicates the time period The arc; Indicates in Online location Tangential velocity vector; A collection of obstacles; It is a group set.

[0045] In the second embodiment, the error calculator operates according to the speed control command. and drones measured speed Generate tracking error signal The reinforcement learning module is based on the error signal Generation state Generate control strategies based on state. In the formula, This is the control parameter vector for the motor controller; the motor controller controls the drive drone based on the control parameter vector. Motor drive signal.

[0046] The terminal controller communicates with the airborne controller via channels provided by the telemetry and control (TT&C) stations. The terminal controller also connects to the TT&C stations and the central server via the same channels. The terminal controller runs an air navigation program. Users can input the UAV's starting point, destination, and swarm size through input units on the air navigation program interface. The central server plans paths for the UAVs based on these inputs and distributes them to multiple TT&C stations. These stations provide navigation functions to the UAV swarm based on obstacle conditions within their controlled airspace. These navigation functions include Lyapunov curves (curve integrals), collision avoidance functions, boundary repulsion functions, and swarm cohesion attraction functions. The terminal controller displays multiple visible paths on the air navigation program interface based on these navigation functions for the user operating the UAVs. The system can also display the UAV swarm's past flight paths, creating multiple tracks. These past and future tracks are displayed in different colors for user differentiation. At low resolutions, multiple tracks are aggregated into a single, thicker track; at high resolutions, multiple tracks are displayed.

[0047] Compared with the prior art, the second embodiment of the present invention can achieve the following beneficial effects: it provides a safe navigation path for swarm drones in a three-dimensional environment.

[0048] The preferred embodiments of the present invention disclosed herein are merely for the purpose of illustrating the present invention. The preferred embodiments do not describe all the details exhaustively, nor do they limit the invention to specific implementation methods. Obviously, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A distributed unmanned aerial vehicle (UAV) telemetry and control service system, characterized in that, Including according to deployment strategy Deployed N monitoring and control stations, deployment strategy Obtain it through the following steps: According to the pre-deployment strategy Select N telemetry and control stations for deployment, among which, ， , and According to the pre-deployment strategy Deployment of telemetry and control sites Communication coverage, throughput, and communication fairness; To be based on the pre-deployment strategy Deployment of monitoring and control stations Location, ; Based on the pre-deployment strategy Get a set of data ; Calculate the pre-deployment strategy according to the following formula. Deployment status value function value: ， Among them, the nth telemetry and control station throughput for: ， In the formula, M represents the number of times the signal enters the telemetry and control station. The number of drones in the airspace; To enter the telemetry and control station Drones in the airspace ; connection bandwidth; For low Earth orbit satellites to tracking and control stations The transmission power; This refers to the altitude of the low Earth orbit satellite. For telemetry and control stations The altitude at which it is located; Let be the noise power; A is the average channel gain at a reference distance of 1m. For the nth telemetry and control station The power; For telemetry and control stations Location; To enter the telemetry and control station drones in the airspace Location; The nth telemetry and control station Fairness of communication for: ， In the formula, For telemetry and control stations and drones At time t, connectivity is 1 for connectivity and 0 otherwise. , For coefficients; The nth telemetry and control station Communication coverage for: ； Function values ​​for K types of pre-deployment state values Sort and get the maximum value ; judge If the value is greater than or equal to the expected value, output: Corresponding pre-deployment strategy As the optimal deployment strategy for telemetry and control sites Otherwise, assign N+1 to N, and then repeat the above steps until the desired value is reached.

2. The distributed UAV telemetry and control service system according to claim 1, characterized in that, Each telemetry and control station sends signals to drones entering its airspace based on the location of obstacles within its controlled airspace. Provide navigation functions to enable drones Flying based on navigation functions; Unmanned Aerial Vehicle The navigation functions provided by the telemetry and control stations along the route from the starting position to the destination position are continuous.

3. The distributed UAV telemetry and control service system according to claim 2, characterized in that, The navigation function includes a guidance curve, and the drone... Fly along the guide curve.

4. The distributed UAV telemetry and control service system according to claim 3, characterized in that, Navigation functions include pipe boundary repulsion functions: ， In the formula, The radius of the virtual tube; and It has two parameters; For drones The distance from the virtual tube centered on the guide curve; , is a coefficient.

5. The distributed UAV telemetry and control service system according to claim 4, characterized in that, Navigation functions include collision avoidance functions: ， In the formula, The radius of the virtual tube; and It has two parameters; For drones The distance to obstacle j, and ; The location of the obstacle; For drones Location; , is a coefficient.

6. A distributed unmanned aerial vehicle (UAV) telemetry and control service method, characterized in that, include: According to the deployment strategy Deploy N monitoring and control stations, deployment strategy Obtain it through the following steps: According to the pre-deployment strategy Select N telemetry and control stations for deployment, among which, ， , and According to the pre-deployment strategy Deployment of telemetry and control sites Communication coverage, throughput, and communication fairness; To be based on the pre-deployment strategy Deployment of monitoring and control stations Location, ; Based on the pre-deployment strategy Get a set of data ; Calculate the pre-deployment strategy according to the following formula. Deployment status value function value: ， Among them, the nth telemetry and control station throughput for: ， In the formula, M represents the number of times the signal enters the telemetry and control station. The number of drones in the airspace; To enter the telemetry and control station Drones in the airspace ; connection bandwidth; For low Earth orbit satellites to tracking and control stations The transmission power; This refers to the altitude of the low Earth orbit satellite. For telemetry and control stations The altitude at which it is located; Let be the noise power; A is the average channel gain at a reference distance of 1m. For the nth telemetry and control station The power; For telemetry and control stations Location; To enter the telemetry and control station drones in the airspace Location; The nth telemetry and control station Fairness of communication for: ， In the formula, For telemetry and control stations and drones At time t, connectivity is 1 for connectivity and 0 otherwise. , For coefficients; The nth telemetry and control station Communication coverage for: ； Function values ​​for K types of pre-deployment state values Sort and get the maximum value ; judge If the value is greater than or equal to the expected value, output: Corresponding pre-deployment strategy As the optimal deployment strategy for telemetry and control sites Otherwise, assign N+1 to N, and then repeat the above steps until the desired value is reached.

7. The distributed UAV telemetry and control service method according to claim 6, characterized in that, Each telemetry and control station sends signals to drones entering its airspace based on the location of obstacles within its controlled airspace. Provide navigation functions to enable drones Flying based on navigation functions; Unmanned Aerial Vehicle The navigation functions provided by the telemetry and control stations along the route from the starting position to the destination position are continuous.

8. The distributed UAV telemetry and control service method according to claim 7, characterized in that, The navigation function includes a guidance curve, and the drone... Fly along the guide curve.

9. The distributed UAV telemetry and control service method according to claim 8, characterized in that, Navigation functions include pipe boundary repulsion functions: ， In the formula, The radius of the virtual tube; and It has two parameters; For drones The distance from the virtual tube centered on the guide curve; , is a coefficient.

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