Unmanned vehicle cluster-oriented low-altitude relay communication unmanned aerial vehicle trajectory planning method

Abstract

The invention relates to a low-altitude relay communication unmanned aerial vehicle track planning method for an unmanned vehicle cluster, and belongs to the technical field of unmanned aerial vehicle control. For an air-ground cooperative control system of a small-scale unmanned vehicle scene, relay communication is performed through an unmanned aerial vehicle, and an average path loss model is established; analyzing the path loss difference between the line-of-sight link and the non-line-of-sight link in the system, establishing a probability model taking the elevation angle and the environmental parameter as variables, and determining a path loss range for guaranteeing the basic communication quality based on the maximum path loss; constructing an air-ground communication path total loss objective function, and searching key track points based on the objective function; on the basis of a BRST algorithm, the discrete track is continuous; discrete trajectories are optimized by adopting a Chebyshev node method, and the smoothness of joints of different trajectories is improved. According to the invention, by optimizing the deployment of the relay unmanned aerial vehicle, the path loss of air-ground communication is reduced, the communication quality is guaranteed, and the problem of sudden change of the planning trajectory state of the unmanned aerial vehicle is avoided.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) control technology and relates to a low-altitude relay communication UAV trajectory planning method for unmanned vehicle swarms. Background Technology

[0002] In diverse fields such as military reconnaissance, disaster relief, and smart cities, the applications of UAVs (Unmanned Aerial Vehicles) and ICVs (Intelligent Connected Vehicles) are becoming increasingly widespread, and swarm collaborative control has become a common method to improve efficiency. In air-ground collaborative control systems, UAVs provide global information by virtue of their air superiority, while ICVs rely on their ground mobility to carry out operations. The two complement each other's functions, which can significantly enhance adaptability to complex environments and mission execution efficiency.

[0003] However, the current collaborative efficiency improvement is facing bottlenecks. Existing relay UAV trajectory planning schemes mostly anchor to fixed flight altitudes or rely on preset paths, failing to fully consider environmental differences. In complex and ever-changing real-world scenarios, environmental parameters can significantly alter communication link characteristics, and ICV formation adjustments can also lead to changes in communication links. Existing preset UAV relay deployments are difficult to adapt to these dynamic changes, easily leading to increased communication interruption risks and delayed collaborative responses, severely restricting the overall operational efficiency of the air-ground collaborative control system. Therefore, breaking through the traditional relay deployment model and constructing a collaborative control method that adapts to environmental changes and dynamic ICV formation adjustments has become an urgent need for the development of air-ground collaborative control systems. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a trajectory planning method for low-altitude relay communication UAVs for unmanned vehicle swarms. By optimizing the smooth trajectory, the method solves the dynamic optimal spatial position of the relay UAV, avoids sudden changes in the UAV's planned trajectory state, and improves the problem of high signal transmission loss in air-to-ground communication.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for low-altitude relay communication UAV trajectory planning for unmanned vehicle swarms, the method comprising: For the air-ground cooperative control system in a low-altitude relay scenario with a limited number of unmanned vehicles, a relay communication is carried out by UAVs, and the average path loss model caused by obstacles in the low-altitude environment is analyzed. The path loss difference between line-of-sight and non-line-of-sight links in the air-ground cooperative control system is analyzed. A probabilistic model with elevation angle and environmental parameters as variables is established. Based on the maximum path loss, and considering Gaussian white noise and signal-to-noise ratio threshold, the path loss range that guarantees basic communication quality is obtained. The low-altitude grid partitioning method is used to optimize the UAV relay location search, construct the objective function of total loss of air-to-ground communication path, and search for key trajectory points based on the objective function; Based on the BRST algorithm, the discrete trajectory is made continuous, and finally a smooth trajectory that meets the mechanical performance of the UAV is generated. To address the Runge phenomenon caused by high-order polynomial interpolation, the Chebyshev node method is used to optimize the node distribution and improve the smoothness at the connection between two trajectory segments.

[0006] Furthermore, the air-to-ground cooperative control system includes one relay communication UAV and L Taiwanese driverless cars; driverless cars exist Time and space location for: , , , for exist t Spatial coordinate information at any given time Drones in t The spatial location at a given time is represented as , , , For UAV in t Spatial coordinate information at any given time , The duration limit for drone relay missions; The path loss of the communication link between the drone and the unmanned factory includes free space propagation loss and additional loss, expressed as:

[0007] in, and These represent the average additional path loss for line-of-sight links and non-line-of-sight links, respectively. This refers to the free space propagation loss.

[0008] Furthermore, in the air-ground cooperative control system, the path loss of non-line-of-sight (NOS) links is higher than that of NOS links; the probability of NOS links occurring is related to the elevation angle and the environment, and the probability of NOS links occurring can be considered as the elevation angle. Environmental parameters and A continuous function is expressed as:

[0009] Then drones and unmanned vehicles Expected path loss Represented as:

[0010] In the formula, and The average path losses for line-of-sight links and non-line-of-sight links are respectively expressed as:

[0011]

[0012] In the formula, The carrier frequency of the radio wave; express t Driverless cars The straight-line distance between the drone and the space , express t Driverless cars The distance from the drone's projection on the ground, , c At the speed of light, drones are relative to driverless cars. The angle of elevation is .

[0013] Furthermore, the basic communication quality is limited by the signal source's transmit power. Gaussian white noise Maximum path loss and signal-to-noise ratio threshold The comprehensive determination is as shown in the following formula:

[0014] therefore, The path loss for drones needs to meet the following requirements:

[0015] This means determining the range of path loss that guarantees basic communication quality.

[0016] Furthermore, based on drones and unmanned vehicles Expected path loss To establish an objective function that minimizes the path loss of the air-ground cooperative control system, the first step is to center the current position of the UAV. Low-altitude airspace is divided into The multi-dimensional cube mesh generates a set of mesh intersection points. Then, establish the objective function:

[0017] In the formula, S represents the minimum flight altitude of the UAV; S is the feasible solution space. ;at last, Take the isochronous distance dispersion time series , At the initial moment of the cluster movement, The termination time; at Between them, an optimization algorithm is used to find the isochronous distance. One relay location: , Indicates in Inside, the UAV (Unmanned Aerial Vehicle) was found. One relay location point, , This represents the time at the corresponding location point.

[0018] Furthermore, for a feasible smooth trajectory consisting of relay points, considering the constraints of the UAV's motion state... Based on this trajectory, the drone follows the time sequence along the point. ,…, ,… ,…, ,…, ,… , …, ,…, ,… Driving; The motion state constraints of the UAV are expressed as follows:

[0019] The above formula represents that in Time, Trajectory satisfy Order constraint ,Right now: At times, there are positional constraints. , , for The relay location point; At that time, there is a speed constraint. ; At that time, there is an acceleration constraint. ; Will The expression is defined as Combination of polynomials: , For polynomial components, respectively corresponding to The first derivative satisfies the 1st derivative. Polynomials with first-order derivative constraints; The expression is constructed by iteratively stacking constraints, starting from those satisfying positional constraints. Begin by adding elements that satisfy the velocity constraints one by one. and satisfying acceleration constraints This eventually forms a complete trajectory; The BRST algorithm is used to analyze the trajectory. Interpolation is performed to make the discrete trajectory continuous. The BRST algorithm uses Lagrange interpolation to construct a polynomial trajectory. x , y , z Decoupled interpolation is performed in three directions; for x Direction, Relay location at time exist x The directional component is Lagrange form interpolation polynomial Defined as:

[0020] In the formula, For the Lagrange basis functions:

[0021] The meaning of the Lagrange basis function is that, for ,have , Therefore, for each trajectory point, the polynomial In the summation, the basis functions only take effect at one time step, causing the interpolation polynomial to... Able to meet Position constraints.

[0022] in, y direction and z Directional interpolation method and x The interpolation method for the direction is the same, and will not be repeated in this invention.

[0023] Furthermore, the Chebyshev node method is used to optimize the trajectory interpolated by the BRST algorithm. Within the time period For each time point, the corresponding time point is re-selected as a planned trajectory node using the following formula:

[0024] In the formula, ; Indicates the midpoint of the time interval. This is the start time of the time period. This is the end point of the time period; Indicates the length of half the interval; For standard Chebyshev node core items, When, the cosine value is The nodes are not uniformly distributed internally, and the spacing between nodes is smaller at the edges.

[0025] The beneficial effects of this invention are as follows: (1) This invention improves the reliability and coverage efficiency of low-altitude relay communication links through path loss analysis and dynamic relay location optimization. In complex and varied terrains and diverse scenarios, such as suburbs, urban areas and high-density building areas, this invention enables the system to adapt to environmental parameter fluctuations and ICV formation adjustments in real time, overcome signal obstruction and scattering problems caused by obstacles, reduce average path loss, break through the coverage blind zone limitations of traditional static relay deployment, thereby reducing the risk of communication interruption, enhancing the continuity and stability of collaborative control, and ensuring efficient and smooth information transmission during task execution.

[0026] (2) This invention utilizes the BRST algorithm and the Chebyshev node method to achieve continuous and smooth UAV trajectory processing, thereby improving the rationality of UAV trajectory planning and flight stability. By strictly satisfying the mechanical performance constraints of the UAV, including position, speed, and acceleration limits, and combining the Chebyshev node method, the generated trajectory avoids Runge phenomenon and state change problems caused by high-order polynomial interpolation, ensuring the stability and controllability of the flight process, improving the safety and operability of low-altitude relay communication in actual deployment, and providing reliable support for continuous collaborative operations in complex environments.

[0027] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0028] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic flowchart of a low-altitude relay communication UAV trajectory planning method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram showing the relationship between altitude difference and signal coverage. Figure 3 This is a schematic diagram of low-altitude grid division. Figure 4 This is a schematic diagram of the smooth trajectory of a relay drone. Detailed Implementation

[0029] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0030] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0031] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, 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, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0032] One embodiment of the present invention provides a low-altitude relay communication UAV trajectory planning method for unmanned vehicle swarms, in order to solve the dynamic optimal spatial position of the relay UAV, solve the problems of large signal transmission loss and sudden changes in UAV trajectory state in air-to-ground communication, and improve the communication quality of air-to-ground cooperative control system.

[0033] like Figure 1 As shown, the method includes the following steps: Step 1: For the air-ground cooperative control system in the scenario of single-hop low-altitude relay for a small-scale ICV group, under the condition that direct ground communication is limited in complex terrain conditions, a relay communication via UAV is proposed, and the average path loss model caused by obstacles in the low-altitude environment is analyzed.

[0034] Consider an air-to-ground cooperative control system for a small-scale ICV group in a single-hop low-altitude relay scenario, consisting of one UAV and This is a series of ICVs. Due to ground signal obstruction, direct communication links between ICVs are severely blocked, making it difficult to establish direct communication. Information transmission between ICVs needs to be relayed through UAVs. Let a certain ICV be... , Without loss of generality, dynamically moving exist Time and space location Recorded as: (1) in, , , for exist t Spatial coordinate information at any given time.

[0035] Due to airborne energy limitations, the duration of the UAV relay mission is assumed to be finite. Inside. Furthermore, definition. To determine the minimum altitude for UAV flight, ensuring the aircraft can avoid obstacles in the terrain without frequent takeoffs and landings, let: (2) in, Indicates UAV in t Spatial location at a given time , , For UAV in t Spatial coordinate information at any given time Since the movement of the ICV cluster is time-varying, the spatial location of the UAV used for relay communication is also time-varying to ensure the best relay communication quality.

[0036] In low-altitude environments, UAVs are affected by buildings, mountains, and leaves, etc. exist and Wireless signals can experience signal blockage and scattering, leading to additional losses in the communication link. Therefore, the path loss of wireless signals consists of two parts: free space propagation loss. With additional losses The main source of additional loss is the loss caused by diffraction, reflection, or scattering of the incident wave after encountering obstacles. Since the average path loss is related to both the vertical angle and height between the UAV and ICV, and without considering the small-scale fluctuations caused by rapid changes in the transmission environment, the average path loss of the channel model is as follows: (3) in, , and These are the average additional path losses for LoS and NLoS links, respectively, which are typically strongly correlated with the environment in which the UAV is located.

[0037] Step 2: Analyze the path loss difference between line-of-sight and non-line-of-sight links in the air-ground cooperative control system, establish a probabilistic model with elevation angle and environmental parameters as variables, and derive the path loss range that guarantees basic communication quality based on the maximum path loss and considering Gaussian white noise and signal-to-noise ratio threshold.

[0038] 1. The specific losses of line-of-sight links and non-line-of-sight links are as follows: Due to shadowing effects and signal reflection from obstacles, the path loss of NLoS links is higher than that of LoS links. The probability of LoS communication links occurring is related to the elevation angle and the environment, which can be further divided into suburban areas, low-density urban areas, and high-density urban areas, and characterized by parameters. When the elevation angle is set to... And the environment parameters are set to and At that time, the probability of sight distance can be regarded as , and A continuous function, which can be approximated by the following Sigmoid function: (4) UAV and ground Expected path loss Represented as: (5) in, and The average path loss for LoS and NLoS links, respectively, can be expressed as: (6) (7) in, The carrier frequency of radio waves. express t time The straight-line distance between the UAV and the space. , express t time The distance between the UAV and its projection on the ground. , c At the speed of light, UAV relative to The angle of elevation is .

[0039] 2. Maximum path loss According to equations (4) to (7), the path loss in low-altitude relay communication can be expressed as: (8) in, , , .

[0040] From equation (8), it can be seen that when the UAV is relative to Flight altitude difference ( ) when path loss With distance The shortening reduces the signal strength. The basic communication quality is limited by the signal source's transmit power. Gaussian white noise Maximum path loss and signal-to-noise ratio threshold The comprehensive determination is as follows: (9) therefore, The path loss for UAVs must meet the following conditions: (10) Furthermore, without considering antenna gain, under different conditions The boundary between the height difference of the UAV and the signal coverage area is as follows: Figure 2 As shown.

[0041] Step 3: Optimize UAV relay location search using a low-altitude grid partitioning method, and establish a key trajectory point search method based on the objective function of total loss of air-to-ground communication path.

[0042] This invention focuses on an air-to-ground cooperative control system for a small-scale ICV (Intelligent Communication Vehicle) group with single-hop low-altitude relay. The number of ground-based ICVs is limited, channel congestion is not considered, and the ICVs are geographically concentrated. Therefore, under the condition that all communication links meet the signal-to-noise ratio threshold, a feasible solution space exists, namely: (11) Where S is the feasible solution space. .

[0043] A low-altitude grid partitioning method is adopted to reduce the complexity of the optimization search, with the current position of the UAV as the center. Low-altitude airspace is divided into The multidimensional cubic mesh, and the resulting set of mesh intersection points are recorded as follows: .against The air-to-ground cooperative control system, composed of a single ICV and a single UAV, has the following objective function: (12) Based on the spatial location of each ICV and Equation (12), the dynamic optimal spatial location of the relay UAV can be obtained through an optimization algorithm. Specifically, in The time series of isochronous distances is as follows , At the initial moment of the cluster movement, The termination time. Let... There is an equal time interval between them. A relay location point based on equation (12): ,in, Indicates in Inside, the UAV found ( ) relay location points, This corresponds to the time at that location. Therefore, if only according to... The optimal relay location in a low-dimensional grid is... At that time, UAV in x , y , z velocity in three directions With acceleration It should be: (13) And let , Low-altitude grid generation methods are as follows: Figure 3 As shown.

[0044] Step 4: Based on the BRST algorithm, the discrete trajectory is made continuous, and finally a smooth trajectory that meets the mechanical performance of the UAV is generated.

[0045] Based on the determined relay location, the BRST algorithm is used, and the execution logic is as follows: UAV from Starting from the moment, find the value according to formula (12). The relay location points are stored in the data buffer. After storage, the trajectory is optimized based on the collected points to ensure it is smooth enough and meets the UAV motion state constraints. At that moment, it was activated in the low-altitude grid. The optimal relay location for each time period is determined and optimized. Therefore, trajectory smoothing optimization is performed block by block in different time periods, and... For a fixed point - optimize the execution cycle.

[0046] definition For a feasible smooth trajectory in three-dimensional space considering the motion state constraints of the UAV, it is possible to follow the time sequence along the point... ,…, ,… ,…, ,…, ,… , …, ,…, ,… Driving. Due to the mechanical limitations of the UAV, the following constraint functions are defined: (14) Indicates in Time, Trajectory satisfy Order constraint In this embodiment ,Right now: (1) At times, there are positional constraints. , ; (2) At that time, there is a speed constraint. ; (3) At that time, there is an acceleration constraint. .

[0047] The position, velocity, and acceleration of the corresponding UAV meet the mechanical performance limits at critical time points.

[0048] definition The expression is Combination of polynomials: (15) in, For polynomial components, respectively corresponding to The first derivative is responsible for satisfying the second derivative. Polynomials with first-order derivative constraints. The construction method achieves constraint satisfaction through "iterative superposition": starting from the polynomials satisfying positional constraints... Begin by adding elements that satisfy the velocity constraints one by one. Satisfying acceleration constraints This process, in turn, forms a complete trajectory, avoiding the matrix inversion step of directly solving high-dimensional equations in traditional methods.

[0049] Before satisfaction Rank (In this embodiment, we take) ), denoted as: (16) If the current optimization step aims to make the UAV satisfy the acceleration constraint, then we have The trajectory has been recursively recursed once and has satisfied the position and velocity constraints.

[0050] To reveal the recursive properties of equation (15), two lemmas are introduced concerning specific polynomials and their derivatives: Lemma 1: If Its derivative is: (17) and: (18) in, τ Indicates the time interval within the i-th time period, the... τ Each trajectory node. This indicates that within the i-th time period, the corresponding time period is... τ The moment of each trajectory node.

[0051] Lemma 2: If ,make If it is a polynomial, then: (19) and: (20) in, μ It represents the order of the derivative.

[0052] Theorem: In At any time in , UAV satisfies Motion state constraints, If the following conditions are met: (twenty one) This is a feasible smooth optimization trajectory, and its expression is: (twenty two) Among them, the first Rank Trajectory It is based on Partial trajectory of order It is generated recursively and through interpolation, and has the following recursive dependency at the corresponding time point of the relay position: (twenty three) The BRST algorithm uses Lagrange interpolation to construct a polynomial trajectory, in order to x , y , z Interpolation is performed using a three-directional decoupling method. The Lagrange polynomial is a classic technique for interpolation problems; its core is to construct a polynomial trajectory from known discrete data points (i.e., relay points) that passes precisely through all these points. x Taking direction as an example, let Moment The component in this direction is Lagrange form interpolation polynomial Defined as: (twenty four) in, For the Lagrange basis functions: (25) The meaning of the Lagrange basis function is that, for ,have , Therefore, for each trajectory point, only the basis function at one time moment in the summation of equation (24) will have an effect, making the interpolation polynomial... Able to meet Position constraints.

[0053] Step 5: To address the Runge phenomenon caused by high-order polynomial interpolation, the Chebyshev node method is used to optimize the node distribution and improve the smoothness at the connection between the two trajectory segments.

[0054] In practical applications, when using high-order polynomials to interpolate data points with equal time intervals, the BRST optimized trajectory method exhibits severe oscillations near the endpoints of the interpolation interval, causing the interpolation results to deviate from the true trend. This problem is also known as the Runge phenomenon, which seriously affects the smoothness of the trajectory.

[0055] To address this problem, the Chebyshev node method is employed, which optimizes the node distribution. The core of the Chebyshev node method is to adjust the time points without changing the order of the trajectory polynomial. The distribution forms nodes in the interval A denser distribution at the edges and a relatively sparser distribution in the middle can reduce the oscillations of high-order polynomials at the endpoints.

[0056] exist During the time period, there exists If there are multiple time points, then the corresponding time points are re-selected as planned trajectory nodes using the following formula: (26) in, ; Indicates the midpoint of the time interval; This indicates the length of half the time interval and is used to scale the value. For standard Chebyshev node core items, When, the cosine value is The nodes are not uniformly distributed internally, and the spacing between nodes at the edges is even smaller.

[0057] Trajectory smoothing enables UAVs to fly more smoothly, reducing unnecessary turns and accelerations / decelerations, lowering energy consumption, and improving communication stability. Ultimately, a smooth trajectory is achieved as shown in the image. Figure 4 As shown.

[0058] In summary, this invention provides a low-altitude relay communication UAV trajectory planning method for unmanned vehicle (UAV) swarms. This method improves the reliability and coverage efficiency of air-to-ground communication in complex environments. Through dynamic relay location optimization in different environments, it reduces communication link loss in differentiated scenarios such as suburbs, urban areas, and high-density urban areas, and overcomes the coverage blind spot problem of traditional static relay deployment. Furthermore, this invention addresses the issue of abrupt changes in UAV trajectory state during planning, achieving smooth trajectory optimization while satisfying kinematic constraints, thus enhancing the rationality and stability of low-altitude relay communication UAV trajectory planning.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A trajectory planning method for low-altitude relay communication UAVs for unmanned vehicle swarms, characterized in that, For the air-ground cooperative control system in a low-altitude relay scenario with a limited number of unmanned vehicles, a relay communication is carried out by UAVs, and the average path loss model caused by obstacles in the low-altitude environment is analyzed. The path loss difference between line-of-sight and non-line-of-sight links in the air-ground cooperative control system is analyzed. A probabilistic model with elevation angle and environmental parameters as variables is established. Based on the maximum path loss, and considering Gaussian white noise and signal-to-noise ratio threshold, the path loss range that guarantees basic communication quality is obtained. The low-altitude grid partitioning method is used to optimize the UAV relay location search, construct the objective function of total loss of air-to-ground communication path, and search for key trajectory points based on the objective function; Based on the BRST algorithm, the discrete trajectory is made continuous, and finally a smooth trajectory that meets the mechanical performance of the UAV is generated. To address the Runge phenomenon caused by high-order polynomial interpolation, the Chebyshev node method is used to optimize node distribution and improve the smoothness at the connection points of different trajectories.

2. The method according to claim 1, characterized in that, The air-ground cooperative control system includes one relay communication drone and... L Taiwanese driverless cars; driverless cars exist Time and space location for: , , , for exist t Spatial coordinate information at any given time Drones in t The spatial location at a given time is represented as , , , For UAV in t Spatial coordinate information at any given time , The duration limit for drone relay missions; The path loss of the communication link between the drone and the unmanned factory includes free space propagation loss and additional loss, expressed as: in, and These represent the average additional path loss for line-of-sight links and non-line-of-sight links, respectively. For free space propagation loss.

3. The method according to claim 2, characterized in that, In air-ground cooperative control systems, the path loss of non-line-of-sight (NOS) links is higher than that of NOS links. The probability of NOS links occurring is related to the elevation angle and the environment; therefore, the probability of NOS links occurring can be considered as the elevation angle... Environmental parameters and A continuous function is expressed as: Then drones and unmanned vehicles Expected path loss Represented as: In the formula, and The average path losses for line-of-sight links and non-line-of-sight links are respectively expressed as: In the formula, The carrier frequency of the radio wave; express t Driverless cars The straight-line distance between the drone and the space , express t Driverless cars The distance from the drone's projection on the ground, , c At the speed of light, drones are relative to driverless cars. The angle of elevation is .

4. The method according to claim 3, characterized in that, The basic communication quality is limited by the signal source's transmit power. Gaussian white noise Maximum path loss and signal-to-noise ratio threshold The comprehensive determination is as shown in the following formula: therefore, The path loss for drones needs to meet the following requirements: This means determining the range of path loss that guarantees basic communication quality.

5. The method according to claim 4, characterized in that, According to drones and unmanned vehicles Expected path loss To establish an objective function that minimizes the path loss of the air-ground cooperative control system, the first step is to center the current position of the UAV. Low-altitude airspace is divided into The multi-dimensional cube mesh generates a set of mesh intersection points. Then, establish the objective function: In the formula, S represents the minimum flight altitude of the UAV; S is the feasible solution space. ;at last, Take the isochronous distance dispersion time series , At the initial moment of the cluster movement, The termination time; at Between them, an optimization algorithm is used to find the isochronous interval. One relay location: , Indicates in Inside, the UAV (Unmanned Aerial Vehicle) was found. One relay location point, , This represents the time at the corresponding location point.

6. The method according to claim 5, characterized in that, For a feasible smooth trajectory consisting of relay points, considering the constraints of the UAV's motion state. Based on this trajectory, the drone follows the time sequence along the point. ,…, ,… ,…, ,…, ,… , …, ,…, ,… Driving; The motion state constraints of the UAV are expressed as follows: The above formula represents that in Time, Trajectory satisfy Order constraints ,Right now: At times, there are positional constraints. , , for The relay location point; At that time, there is a speed constraint. ; At that time, there is an acceleration constraint. ; Will The expression is defined as Combination of polynomials: , For polynomial components, respectively corresponding to The first derivative satisfies the 1st derivative. Polynomials with first-order derivative constraints; The expression is constructed by iteratively stacking constraints, starting from those satisfying positional constraints. Begin by adding elements that satisfy the velocity constraints one by one. and satisfying acceleration constraints This eventually forms a complete trajectory; The BRST algorithm is used to analyze the trajectory. Interpolation is performed to make the discrete trajectory continuous. The BRST algorithm uses Lagrange interpolation to construct a polynomial trajectory. x , y , z Decoupled interpolation is performed in three directions; for x Direction, Relay location at time exist x The directional component is Lagrange form interpolation polynomial Defined as: In the formula, For the Lagrange basis functions: The meaning of the Lagrange basis function is that, for ,have , Therefore, for each trajectory point, the polynomial In the summation, the basis functions only take effect at one time step, causing the interpolation polynomial to... Able to meet Position constraints; y direction and z Directional interpolation method and x The interpolation method for the direction is consistent.

7. The method according to claim 6, characterized in that, The trajectory generated by the BRST algorithm interpolation is optimized using the Chebyshev node method. There are within the time period For each time point, the corresponding time point is re-selected as a planned trajectory node using the following formula: In the formula, ; Indicates the midpoint of the time interval. This is the start time of the time period. This is the end point of the time period; Indicates the length of half the interval; For standard Chebyshev node core items, When, the cosine value is The nodes are not uniformly distributed internally, and the spacing between nodes is smaller at the edges.

Images