Unmanned aerial vehicle on-demand image acquisition and wireless transmission method

Abstract

The invention relates to the technical field of unmanned aerial vehicle image acquisition and transmission, in particular to an unmanned aerial vehicle on-demand image acquisition and wireless transmission method, which comprises the steps of establishing an unmanned aerial vehicle image acquisition and transmission optimization model aiming at an unmanned aerial vehicle image acquisition task, acquiring basic data of the task and an environment, and acquiring an image of an unmanned aerial vehicle through an alternate optimization algorithm. A task allocation variable and a transmission scheduling variable of the single unmanned aerial vehicle are obtained, the flight path of the unmanned aerial vehicle is optimized by adopting continuous convex approximation (SCA), and a final execution scheme of the single unmanned aerial vehicle is obtained; and through a distributed optimization method, a multi-unmanned aerial vehicle cooperative execution scheme is obtained. The system is suitable for scenes of power monitoring, construction monitoring, building inspection, search and rescue actions and the like, can be compatible with cooperation of a single unmanned aerial vehicle and multiple unmanned aerial vehicles, realizes on-demand image acquisition and wireless transmission, improves the task completion rate and the overall performance of the system, and meets diversified real-time monitoring requirements.

Description

Technical Field

[0001] This invention relates to the field of image acquisition and transmission technology for unmanned aerial vehicles (UAVs). Background Technology

[0002] With the rapid development of unmanned aerial vehicle (UAV) technology, its application in image acquisition and wireless transmission is becoming increasingly widespread. UAVs offer advantages such as rapid deployment and flexible movement, meeting the image monitoring needs of various scenarios, including power monitoring, construction monitoring, building inspection, and search and rescue operations. These applications require UAVs not only to capture high-quality images of designated areas but also to transmit the image data to backend personnel within a specified timeframe.

[0003] Despite extensive research exploring the application of drones in image acquisition and wireless transmission, these methods still suffer from several significant drawbacks: First, most existing methods assume that all acquired data is ultimately transmitted to a single destination, such as a base station or data center. However, in many practical applications, different users may be located in different places, and each task has its own independent time constraints. Second, existing methods often fail to adequately consider the time urgency of tasks, potentially leading to the failure of some tasks due to untimely completion. In such cases, even if the drone can efficiently acquire data, it cannot meet the user's real-time needs. Third, existing methods typically treat task allocation, transmission scheduling, and trajectory design as independent problems for optimization, ignoring their interdependencies. This segmented approach struggles to effectively handle complex multi-tasking environments, especially when multiple drones are collaborating. Finally, traditional methods often overlook the possibility of improving communication efficiency through relay cooperation between drones, resulting in longer data transmission times over long distances and impacting the overall task completion rate. These shortcomings lead to low task completion rates and poor adaptability in complex multi-tasking environments, failing to meet diverse real-time monitoring needs. Summary of the Invention

[0004] To overcome the problems of insufficient task priority management, difficulty in meeting time constraints, and low efficiency of multi-UAV collaboration in existing technologies, this invention provides a method for on-demand image acquisition and wireless transmission by UAVs.

[0005] The technical solution adopted by the present invention to achieve the above objectives is: a method for on-demand image acquisition and wireless transmission by a drone, comprising the following steps:

[0006] S1. For UAV image acquisition tasks, collect basic data on the task and environment, and establish an optimization model for UAV image acquisition and transmission.

[0007] S2. Based on the UAV image acquisition and transmission optimization model, the task allocation variables and transmission scheduling variables of a single UAV are obtained through the alternating optimization algorithm. Then, the flight trajectory of the UAV is optimized by continuous convex approximation (SCA) to obtain the final execution scheme of a single UAV.

[0008] S3. Based on the final execution scheme of a single UAV, a multi-UAV collaborative execution scheme is obtained through distributed optimization methods;

[0009] S4. Based on the actual number of drones deployed, output an optimized solution for on-demand image acquisition and wireless transmission by the drones.

[0010] Preferably, in step S1, the image acquisition task set is defined as follows: .

[0011] Each task Includes the following elements:

[0012] Target area: The area is rectangular, with the center located at... , length is Width is ;

[0013] Requesting user: Location is ;

[0014] Deadline: Data collected by drones must be submitted by the deadline. Pre-delivery to the user;

[0015] The basic data includes: target area parameters, user location, deadline, UAV flight performance parameters, communication channel quality parameters, and obstacle-related parameters for all missions;

[0016] Set the following constraints:

[0017] Field of view coverage constraint: When the UAV flies over the target area, the field of view must completely cover the target area;

[0018] Resolution constraint: The pixel density of the acquired image shall not be lower than a preset threshold;

[0019] Transmission rate constraint: The image transmission rate must match the channel quality and distance between the drone and the user;

[0020] Time constraint: The total time for image acquisition and transmission shall not exceed the task deadline. .

[0021] Preferably, in step S2, the task allocation variables and transmission scheduling variables for a single UAV are obtained through an alternating optimization algorithm as follows:

[0022] Set the initial trajectory of the drone as Auxiliary variables are The objective function for constructing the alternating optimization algorithm is:

[0023] ;

[0024] in, Assign variables to the task. To transmit scheduling variables, and It was obtained by solving linear programming. Let be the penalty function. As a penalty parameter, the auxiliary variables are updated based on the task allocation variables and transmission scheduling variables obtained from the solution.

[0025] Preferably, in step S2, the continuous convex approximation (SCA) is used to optimize the UAV flight trajectory as follows:

[0026] Non-convex problem transformation:

[0027] The number of iterations is At that time, in the current iteration trajectory For nonconvex functions Perform a first-order Taylor expansion:

[0028] ;

[0029] Solving the convex subproblem:

[0030] Replacing the non-convex terms, we construct the convex optimization problem as follows:

[0031] ;

[0032] in, For the task The monitored area This represents the task completion coefficient. The penalty term is used to solve for the new trajectory. ,

[0033] Repeat the steps of transforming the non-convex problem and solving the convex subproblem. Compare the objective function values ​​corresponding to the trajectories of two adjacent iterations. If the difference is less than the preset threshold or the maximum number of iterations is reached, the trajectory optimization converges and the optimized trajectory is output.

[0034] Preferably, step S3 includes:

[0035] S3-1, Initialization of multiple UAV parameters:

[0036] For each UAV, an initial trajectory and auxiliary variables are randomly generated, and initial penalty parameters and iteration parameters are set; an auxiliary deadline is introduced. :

[0037] ;

[0038] in, To update the step size coefficient;

[0039] S3-2. Perform local optimization: Each drone independently optimizes its own execution plan based on information from neighboring drones;

[0040] S3-3, Calculation Task In drones Completion rate ;

[0041] S3-4. Perform global coordination and optimization: Adjust the auxiliary deadline based on the actual completion status of the task: If the actual completion time is earlier than expected, shorten the auxiliary deadline for the next round; if it is later than expected, extend the auxiliary deadline.

[0042] Preferably, step S3-3 is

[0043] Calculate task completion rate, define task In drones The completion rate is Calculated in three categories:

[0044] Pure image acquisition task:

[0045] ;

[0046] in, For drones For the task The effective coverage area For the task Total monitored area;

[0047] Pure image transmission task:

[0048] ;

[0049] in, For drones Transmitted tasks The amount of data, The total amount of data that task m needs to transmit;

[0050] Mixed tasks of data acquisition and transmission:

[0051] ;

[0052] in, These are the normalized weighting coefficients.

[0053] The beneficial effects of this invention are as follows:

[0054] This invention solves the mixed-integer nonlinear programming problem (MINLP) by jointly optimizing task allocation, transmission scheduling, and trajectory design. It introduces an alternating optimization algorithm combined with Exact Penalty Method (EPM) and Continuous Convex Approximation (SCA) techniques, effectively handling non-convex constraints and maximizing the total monitoring area while satisfying multiple constraints. Compared to traditional independent optimization methods, this improves task completion rate. This invention is compatible with both single-UAV and multi-UAV scenarios. For single-UAV scenarios, the alternating optimization algorithm ensures efficiency, while multi-UAV scenarios achieve coordination through distributed optimization and assisted deadlines, enabling effective coordination of cross-UAV tasks. It is adaptable to various scenarios such as urban patrols and search and rescue operations. The multi-UAV scenario of this invention supports relay cooperation and cross-UAV tasks. This invention optimizes the collaborative process of image capture and delivery by reducing the flight distance of a single UAV through task allocation and utilizing inter-UAV relay transmission technology. Through neighbor information exchange and global coordination, the system coverage is increased by 2-3 times. The invention introduces an auxiliary deadline and task completion rate mechanism to dynamically adjust task priorities, ensuring that tasks meet time constraints and adapting to the high timeliness requirements of real-time monitoring. Furthermore, the invention fully considers the heterogeneity of different tasks, ensuring the efficiency and timeliness of image capture and delivery through flexible task allocation and transmission scheduling strategies. By dynamically adjusting the flight path and altitude of UAVs to adapt to changes in line-of-sight link probability, it completes as many high-priority tasks as possible within a limited time, meeting the diverse needs of practical applications. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the overall process of an embodiment of the present invention;

[0056] Figure 2 This is a schematic diagram of a single UAV image acquisition scenario according to an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of a multi-UAV image acquisition scenario according to an embodiment of the present invention. Detailed Implementation

[0058] Embodiments of the present invention provide a method for on-demand image acquisition and wireless transmission by a drone, such as... Figure 1 As shown, it includes the following steps:

[0059] S1. For UAV image acquisition tasks, collect basic data on the task and environment, and establish an optimization model for UAV image acquisition and transmission, specifically:

[0060] An optimization model for UAV image acquisition and transmission is established, compatible with both single-UAV and multi-UAV application scenarios, and the set of image acquisition tasks is defined as follows. .

[0061] Each task Includes the following elements:

[0062] Target area: The area is rectangular, with the center located at... , length is Width is ;

[0063] Requesting user: Location is ;

[0064] Deadline: Data collected by drones must be submitted by the deadline. Delivered to the user before delivery.

[0065] The basic data collected includes the task and environment.

[0066] Task-related data: Target area parameters (coordinates, dimensions), user location, and deadline for all tasks;

[0067] Environmental data: UAV flight performance parameters, communication channel quality parameters, obstacle-related parameters.

[0068] The goal of UAV image acquisition is to maximize the total monitored area by optimizing trajectory, task allocation, and transmission scheduling. To achieve this goal, the model is subject to the following constraints:

[0069] Field of view coverage constraint: When the UAV flies over the target area, the field of view must completely cover the target area;

[0070] Resolution constraint: The pixel density of the acquired image shall not be lower than a preset threshold;

[0071] Transmission rate constraints: The image transmission rate must match the channel quality and distance between the drone and the user (the closer the distance and the better the channel, the higher the transmission rate).

[0072] Time constraint: The total time for image acquisition and transmission shall not exceed the task deadline. .

[0073] S2. Based on the UAV image acquisition and transmission optimization model, the task allocation variables and transmission scheduling variables of a single UAV are obtained through an alternating optimization algorithm. Then, the flight trajectory of the UAV is optimized using Continuous Convex Approximation (SCA) and Exact Penalty Method (EPM) to obtain the final execution scheme of a single UAV, specifically:

[0074] S2-1, Set the initial trajectory of the drone as follows: Auxiliary variables are In the alternating optimization algorithm, the objective function is constructed to maximize the total monitored area of ​​the task:

[0075] ;

[0076] in, Assign variables to the task. To transmit scheduling variables, and It was obtained by solving a linear programming problem. This is a penalty function used to promote the generation of binary solutions; This is a penalty parameter used to control the intensity of the penalty. The maximum penalty parameter is growth factors Iteration interval .

[0077] S2-2. Based on the task allocation variables and transmission scheduling variables, optimize the UAV trajectory through continuous convex approximation (SCA):

[0078] Non-convex problem transformation:

[0079] For the non-convex characteristics of trajectory optimization (such as path nonlinearity caused by obstacle avoidance), the number of iterations is... At that time, in the current iteration trajectory For nonconvex functions Perform a first-order Taylor expansion:

[0080] ;

[0081] Transform the non-convex function into a linear convex function to satisfy the conditions for convex optimization.

[0082] Solving the convex subproblem:

[0083] After replacing the non-convex terms, the convex optimization problem becomes:

[0084] ;

[0085] in, For the task The monitored area This represents the task completion coefficient. The penalty term is used; the new trajectory is obtained by solving it using standard convex optimization tools (such as MATLAB's CVX toolbox). .

[0086] Repeat the steps of transforming the non-convex problem and solving the convex subproblem. Compare the objective function values ​​corresponding to the trajectories of two adjacent iterations. If the difference is less than the preset threshold or the maximum number of iterations is reached, the trajectory optimization converges and the optimized trajectory is output.

[0087] S2-3. Based on the task allocation variables and transmission scheduling variables obtained from the solution, update the auxiliary variables to optimize the solution quality for the next iteration.

[0088] S2-4. After each iteration, use the exact penalty method to gradually increase the penalty parameter according to the growth factor to ensure that the final solution is close to the binary solution.

[0089] S2-5. Repeat steps S2-1 to S2-4 until the objective function converges, obtaining the final task allocation, transmission scheduling scheme, and optimized trajectory. Output the final execution scheme for a single UAV. A single UAV inspection scenario is as follows: Figure 2 As shown.

[0090] S3. Based on the single UAV final execution scheme, a multi-UAV cooperative execution scheme is obtained through a distributed optimization method, specifically as follows:

[0091] In multi-UAV scenarios, a task may require multiple UAVs to work together to complete it. For example, one UAV may capture an image while another UAV is responsible for delivery. The following factors need to be considered: cross-UAV tasks, i.e., image capture and delivery for some tasks are completed by different UAVs; relay transmission, i.e., data exchange is required between UAVs to complete cross-UAV tasks.

[0092] In multi-UAV scenarios, distributed optimization is achieved by using auxiliary deadlines and completion rates. Auxiliary variables and deadlines are iteratively updated to gradually improve overall performance.

[0093] S3-1, Initialization of multiple UAV parameters:

[0094] For each UAV, an initial trajectory and auxiliary variables are randomly generated, and initial penalty parameters and iteration parameters are set.

[0095] In order to control the task An auxiliary deadline is introduced to determine the time that must be completed at each stage of the relay path. :

[0096] ;

[0097] in, To update the step size coefficient, the initial value of the auxiliary deadline is set to the average node of the total task deadline. In each iteration, these auxiliary deadlines are dynamically adjusted based on the actual task completion status. By adjusting these auxiliary deadlines, the collaboration between different UAVs can be gradually optimized.

[0098] In multi-UAV scenarios, the problem is decomposed into multiple single-UAV sub-problems through local optimization and global coordination, with each UAV independently completing trajectory optimization, task allocation, and transmission scheduling:

[0099] S3-2. Local Optimization: Each UAV independently optimizes its execution plan based on information from neighboring UAVs (UAVs within communication range or with task dependencies). The communication topology is a local adjacency graph, and each UAV only interacts with UAVs within its communication range. The information exchanged between UAVs includes: trajectory information. Transmission scheduling plan, auxiliary deadline and task completion rate This information is used to construct local optimization problems and ensure smooth task transfer between multiple UAVs.

[0100] S3-3. Calculate the task completion rate and define the task. In drones The completion rate is Calculated in three categories:

[0101] Pure image acquisition task:

[0102] ;

[0103] in, For drones For the task The effective coverage area For the task Total monitored area;

[0104] Pure image transmission task:

[0105] ;

[0106] in, For drones Transmitted tasks The amount of data, The total amount of data that task m needs to transmit;

[0107] Mixed tasks of data acquisition and transmission:

[0108] ;

[0109] in, These are the normalized weighting coefficients.

[0110] S3-4. Perform global coordination and optimization: By iteratively updating the auxiliary deadline and neighboring UAV information, gradually improve overall performance. Adjust the auxiliary deadline based on the actual task completion status: If the actual completion time is earlier than expected, shorten the auxiliary deadline for the next round; if it is later than expected, extend the auxiliary deadline to ensure smooth task coordination among multiple UAVs.

[0111] S3-5. Repeat steps SS3-2 to S3-4 until the objective functions of all drones converge. Output a multi-drone collaborative execution scheme. This scheme supports relay cooperation between drones (e.g., drone 1 collects images, drones 2 and 3 relay the data to the user), reducing the flight distance of a single drone. Multi-drone inspection scenarios include... Figure 3 As shown.

[0112] S4. Based on the actual number of drones deployed, output the final optimized solution for on-demand drone image acquisition and wireless transmission:

[0113] If one drone is deployed, the single-drone final execution scheme obtained in step S3 is adopted; if two or more drones are deployed, the multi-drone collaborative execution scheme obtained in step S4 is adopted.

[0114] This invention has been described through embodiments. Those skilled in the art will understand that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of this invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, this invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of this invention.

Claims

1. A method for on-demand image acquisition and wireless transmission by a drone, characterized in that, Includes the following steps: S1. For UAV image acquisition tasks, collect basic data on the task and environment, and establish an optimization model for UAV image acquisition and transmission. S2. Based on the UAV image acquisition and transmission optimization model, the task allocation variables and transmission scheduling variables of a single UAV are obtained through the alternating optimization algorithm. Then, the flight trajectory of the UAV is optimized by continuous convex approximation (SCA) to obtain the final execution scheme of a single UAV.

2. The method for on-demand image acquisition and wireless transmission by a UAV according to claim 1, characterized in that, In step S1, the image acquisition task set is defined as follows: ; Each task Includes the following elements: Target area: The area is rectangular, with the center located at... , length is Width is ; Requesting user: Location is ; Deadline: Data collected by drones must be submitted by the deadline. Pre-delivery to the user; The basic data includes: target area parameters for all missions, user location, deadline, UAV flight performance parameters, communication channel quality parameters, and obstacle-related parameters.

3. The method for on-demand image acquisition and wireless transmission by a UAV according to claim 2, characterized in that, In step S1, each task Including the following constraints: Field of view coverage constraint: When the UAV flies over the target area, the field of view must completely cover the target area; Resolution constraint: The pixel density of the acquired image shall not be lower than a preset threshold; Transmission rate constraint: The image transmission rate must match the channel quality and distance between the drone and the user; Time constraint: The total time for image acquisition and transmission shall not exceed the task deadline. .

4. The method for on-demand image acquisition and wireless transmission by a UAV according to claim 1, characterized in that, In step S2, the task allocation variables and transmission scheduling variables for a single UAV are obtained through an alternating optimization algorithm: Set the initial trajectory of the drone as Auxiliary variables are The objective function for constructing the alternating optimization algorithm is: ； in, Assign variables to the task. To transmit scheduling variables, and It was obtained by solving linear programming. Let be the penalty function. As a penalty parameter, the auxiliary variables are updated based on the task allocation variables and transmission scheduling variables obtained from the solution.

5. The method for on-demand image acquisition and wireless transmission by a UAV according to claim 1, characterized in that, In step S2, the UAV flight trajectory is optimized using Continuous Convex Approximation (SCA) as follows: Non-convex problem transformation: The number of iterations is At that time, in the current iteration trajectory For non-convex functions Perform a first-order Taylor expansion: ； Solving the convex subproblem: Replacing the non-convex terms, we construct the convex optimization problem as follows: ； in, For the task The monitored area This represents the task completion coefficient. The penalty term is used to solve for the new trajectory. , Repeat the steps of transforming the non-convex problem and solving the convex subproblem. Compare the objective function values ​​corresponding to the trajectories of two adjacent iterations. If the difference is less than the preset threshold or the maximum number of iterations is reached, the trajectory optimization converges and the optimized trajectory is output.

6. The method for on-demand image acquisition and wireless transmission by a UAV according to claim 1, characterized in that, It also includes the following steps: S3. Based on the final execution scheme of a single UAV, a multi-UAV collaborative execution scheme is obtained through distributed optimization methods; S4. Based on the actual number of drones deployed, output an optimized solution for on-demand image acquisition and wireless transmission of drones.

7. The UAV on-demand image acquisition and wireless transmission method according to claim 6, characterized in that, Step S3 includes: S3-1, Initialization of multiple UAV parameters: For each UAV, an initial trajectory and auxiliary variables are randomly generated, and initial penalty parameters and iteration parameters are set; an auxiliary deadline is introduced. : ； in, To update the step size coefficient; S3-2. Perform local optimization: Each drone independently optimizes its own execution plan based on information from neighboring drones; S3-3, Calculation Task In drones Completion rate ; S3-4. Perform global coordination and optimization: Adjust the auxiliary deadline based on the actual completion status of the task: If the actual completion time is earlier than expected, shorten the auxiliary deadline for the next round; if it is later than expected, extend the auxiliary deadline.

8. The UAV on-demand image acquisition and wireless transmission method according to claim 7, characterized in that, Task in step S3-3 For a pure image acquisition task, the completion rate was: ； in, For drones For the task The effective coverage area For the task Total monitored area.

9. The method for on-demand image acquisition and wireless transmission by a UAV according to claim 7, characterized in that, Task in step S3-3 For a pure image transmission task, the completion rate is: ； in, For drones Transmitted Task The amount of data, This represents the total amount of data that task m needs to transmit.

10. The UAV on-demand image acquisition and wireless transmission method according to claim 7, characterized in that, Task in step S3-3 For the mixed task of data acquisition and transmission, the completion rate was: ； in, These are the normalized weighting coefficients.

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