Unmanned aerial vehicle dynamic path planning method and device for mobile vehicle team monitoring task

Abstract

The application discloses a kind of unmanned aerial vehicle dynamic path planning method and device for mobile vehicle team monitoring task, it is related to unmanned aerial vehicle technical field, comprising: the position information of each vehicle in the vehicle team to be monitored is obtained;According to position information, the mass center position and motion direction angle of vehicle team are calculated;Based on mass center position and motion direction angle, the flight trajectory of unmanned aerial vehicle is adjusted using predefined unmanned aerial vehicle flight trajectory parameterization model, unmanned aerial vehicle flight trajectory parameterization model is defined based on major axis of elliptical orbit, minor axis of elliptical orbit and angular velocity of unmanned aerial vehicle flight around elliptical orbit, and the direction of elliptical orbit is consistent with motion direction angle;Wherein, every interval an optimization period, with the total area of the monitoring area of unmanned aerial vehicle in optimization period as optimization target, major axis of elliptical orbit, minor axis of elliptical orbit and angular velocity are optimized online.The application can realize real-time, continuous and efficient monitoring to dynamic vehicle team, and significantly improve monitoring coverage quality.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a UAV dynamic path planning method and apparatus for mobile vehicle fleet monitoring tasks. Background Technology

[0002] With the rapid development of drone technology, drones, with their high flexibility and wide coverage capabilities, are being used more and more widely in fields such as convoy escort, environmental monitoring, and aerial reconnaissance. When performing convoy surveillance missions, drones need to continuously cover the area surrounding the convoy and dynamically adjust their flight paths as the convoy moves to ensure the real-time nature and completeness of the surveillance.

[0003] Currently, for mobile vehicle convoy surveillance tasks, commonly used UAV path planning methods mainly include path tracking methods based on dynamic artificial potential fields and elliptical orbit formation surveillance methods.

[0004] Path tracking methods based on dynamic artificial potential fields generate smooth and collision-free flight paths by simulating potential field forces, enabling UAVs to follow ground targets in complex dynamic environments. This method can generate relatively smooth flight trajectories and dynamically adapt to changes in target speed and direction. However, when obstacles are present in the environment and the potential field structure is complex, this method is highly susceptible to causing the UAV to lose track of convoy targets, making continuous and uninterrupted monitoring difficult.

[0005] Elliptical orbit formation monitoring methods employ multiple drones flying uniformly along time-varying elliptical orbits in formation to economically and efficiently monitor the area surrounding a convoy. This method can generate smooth, collision-free paths and dynamically adapt to changes in target speed and direction. However, existing elliptical orbit formation monitoring methods typically rely on pre-set orbit parameters or simple rule adjustments, lacking the ability to optimize online based on real-time convoy movement and monitoring quality feedback. This results in an inability to flexibly adapt to dynamic scenarios such as changes in convoy formation and sudden speed changes, leading to insufficient or redundant effective monitoring coverage and compromised monitoring efficiency.

[0006] In summary, existing technologies cannot adequately address the technical challenge of how to dynamically optimize UAV flight trajectory parameters in real time to maximize surveillance coverage quality while meeting flight constraints in mobile vehicle fleet monitoring missions. Summary of the Invention

[0007] To address some or all of the technical problems existing in the prior art, the present invention provides a method and apparatus for dynamic path planning of unmanned aerial vehicles (UAVs) for mobile vehicle fleet monitoring tasks.

[0008] The technical solution of the present invention is as follows: Firstly, a dynamic path planning method for UAVs for mobile vehicle fleet surveillance tasks is provided, including: Obtain the location information of each vehicle in the convoy to be monitored; Based on the location information, calculate the center of mass position and direction of motion angle of the convoy; Based on the centroid position and motion direction angle of the convoy, the flight trajectory of the UAV is adjusted using a predefined UAV flight trajectory parameterization model. The UAV flight trajectory parameterization model is defined based on the major axis and minor axis of the elliptical orbit and the angular velocity of the UAV flying around the elliptical orbit, and the direction of the elliptical orbit is consistent with the motion direction angle. In this process, every optimization cycle aims to maximize the total area of ​​the UAV's monitoring area within that cycle. The major axis, minor axis, and angular velocity of the UAV around the elliptical orbit are optimized online, with constraints on UAV flight speed and flight trajectory parameters. The total area of ​​the UAV's monitoring area within the optimization cycle is determined by the area of ​​the union of the monitoring areas of all UAVs within that cycle. The UAV's monitoring area is defined as a square area centered on the UAV's current position, and the side length of the square area is determined based on the UAV's flight altitude and field of view.

[0009] Furthermore, in some embodiments, the center of gravity position of the convoy is calculated based on the location information using the following formula: ; ; in, This indicates a vehicle index. Indicates the number of vehicles. Represents a time variable. Indicates the first One vehicle Time Axis coordinates Indicates the first One vehicle Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates.

[0010] Furthermore, in some embodiments, the motion direction angle of the convoy is calculated based on the location information using the following formula: ; ; ; in, Indicates the team is The direction and angle of time's movement This indicates that the team's center of gravity is at Time Axis coordinates and in Time The displacement between the axes. This indicates that the team's center of gravity is at Time Axis coordinates and in Time The displacement between the axes. This indicates that the team's center of gravity is at Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates This indicates the preset location information collection time interval. This indicates the arctangent function operation.

[0011] Furthermore, in some embodiments, the parameterized model of the UAV flight trajectory is represented as: ; in, Indicates the drone index. Represents a time variable. Indicates the first A drone Time Axis coordinates Indicates the first A drone Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates Indicates the team is The direction and angle of time's movement Indicates the major axis of the elliptical orbit. Indicates the minor axis of the elliptical orbit. This represents the angular velocity of the drone as it flies around an elliptical orbit. Indicates the number of drones. This represents the operation of the cosine function. This represents the operation of the sine function.

[0012] Furthermore, in some implementations, the total area of ​​the UAV's surveillance area during the optimization period is determined in the following way: ; in, This represents the total area of ​​the drone's surveillance zone during the optimization period. Indicates the drone index. Indicates a point-in-time index. Indicates the number of drones. This represents the number of time points into which the optimization cycle is discretely divided. Indicates the first iteration within the optimization cycle. At a certain point in time, Indicates the first A drone Time monitoring area This represents the area function.

[0013] Furthermore, in some embodiments, the side length of the square region is determined by the following formula: ; in, This represents the side length of the square region. Indicates the drone's flight altitude. Indicates the field of view of the drone. This represents the tangent function operation.

[0014] Furthermore, in some embodiments, the UAV flight speed constraint is expressed as: ; in, Indicates the flight speed of the drone. Represents a time variable. Indicates that drones are in Time Axis coordinates Indicates that drones are in Time Axis coordinates This represents the differentiation operator. This indicates the preset maximum flight speed.

[0015] Furthermore, in some embodiments, the flight trajectory parameter range constraint is expressed as: ; in, Indicates the major axis of the elliptical orbit. Indicates the minor axis of the elliptical orbit. This represents the angular velocity of the drone as it flies around an elliptical orbit. This represents the minimum value of the major axis of the preset elliptical orbit. This represents the maximum value of the major axis of the preset elliptical orbit. This represents the minimum value of the minor axis of the preset elliptical orbit. This represents the maximum value of the minor axis of the preset elliptical orbit. This represents the minimum angular velocity of the drone as it is intended to fly around an elliptical orbit. This indicates the maximum angular velocity of the drone as it is expected to fly around an elliptical orbit.

[0016] Furthermore, in some implementations, a sequential least-squares quadratic programming algorithm is used to optimize the major axis, minor axis, and angular velocity of the UAV flying around the elliptical orbit online.

[0017] Secondly, a UAV dynamic path planning device for mobile convoy surveillance tasks is also provided, comprising: The location acquisition module is used to acquire the location information of each vehicle in the convoy to be monitored; The state calculation module is used to calculate the center of mass position and direction of motion angle of the convoy based on the position information. The trajectory optimization module is used to adjust the flight trajectory of the UAVs based on the centroid position and motion direction angle of the convoy using a predefined UAV flight trajectory parameterization model. The UAV flight trajectory parameterization model is defined based on the major axis and minor axis of the elliptical orbit and the angular velocity of the UAV flying around the elliptical orbit, and the direction of the elliptical orbit is consistent with the motion direction angle. The trajectory optimization module optimizes the total area of ​​the UAV's monitoring area within each optimization cycle. The optimization objective is to maximize the total area of ​​the UAV's monitoring area within the optimization cycle. The module uses UAV flight speed constraints and flight trajectory parameter range constraints as constraints to optimize the major axis, minor axis, and angular velocity of the UAV around the elliptical orbit online. The total area of ​​the UAV's monitoring area within the optimization cycle is determined by the area of ​​the union of the monitoring areas of all UAVs within the optimization cycle. The UAV's monitoring area is defined as a square area centered on the UAV's current position. The side length of the square area is determined based on the UAV's flight altitude and field of view.

[0018] The main advantages of the technical solution of this invention are as follows: The present invention provides a UAV dynamic path planning method and apparatus for mobile convoy monitoring tasks. By acquiring the position of each vehicle in the convoy in real time and dynamically calculating the convoy's centroid and direction of motion, the method adjusts the UAV flight trajectory in real time using a parameterized model of UAV flight trajectory defined based on elliptical orbit formation parameters. Simultaneously, at each optimization cycle, the method aims to maximize the total area of ​​the UAV's monitoring area within the optimization cycle. Using UAV flight speed constraints and flight trajectory parameter range constraints as constraints, the method optimizes the major axis, minor axis, and angular velocity of the UAV around the elliptical orbit online to update the UAV flight trajectory parameterized model. This enables real-time, continuous, and efficient monitoring of dynamic convoys and significantly improves the monitoring coverage quality and resource utilization efficiency in complex dynamic environments. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and constitute a part of this invention, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 A flowchart illustrating a UAV dynamic path planning method for mobile vehicle fleet surveillance tasks provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a UAV dynamic path planning device for mobile fleet monitoring tasks, provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] The technical solutions provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] refer to Figure 1 This invention provides a method for dynamic path planning of unmanned aerial vehicles (UAVs) for mobile vehicle fleet surveillance tasks. The method includes the following steps: Step 1: Obtain the location information of each vehicle in the convoy to be monitored; Step 2: Calculate the center of mass position and direction of motion angle of the convoy based on the location information; Step 3: Based on the center of mass position and motion direction angle of the vehicle fleet, adjust the flight trajectory of the UAV using a predefined UAV flight trajectory parameterization model. The UAV flight trajectory parameterization model is defined based on the major axis and minor axis of the elliptical orbit and the angular velocity of the UAV flying around the elliptical orbit, and the direction of the elliptical orbit is consistent with the motion direction angle. In this process, every optimization cycle aims to maximize the total area of ​​the UAV's monitoring area within that cycle. The major axis, minor axis, and angular velocity of the UAV around the elliptical orbit are optimized online, with constraints on UAV flight speed and flight trajectory parameters. The total area of ​​the UAV's monitoring area within the optimization cycle is determined by the area of ​​the union of the monitoring areas of all UAVs within that cycle. The UAV's monitoring area is defined as a square area centered on the UAV's current position, and the side length of the square area is determined based on the UAV's flight altitude and field of view.

[0023] In this embodiment of the invention, the drone collects the location information of each vehicle in the convoy to be monitored in real time based on a preset location information collection time interval.

[0024] In this embodiment of the invention, based on the position information of each vehicle in the convoy to be monitored at the current moment, the centroid position and motion direction angle of the convoy at the current moment are calculated. Based on the centroid position and motion direction angle of the convoy at the current moment, the position of the drone is calculated using a predefined drone flight trajectory parameterization model, and the drone is controlled to move to the corresponding position, thereby adjusting the drone's flight trajectory in real time.

[0025] In this embodiment of the invention, the parameterized model of the UAV flight trajectory is defined based on the elliptical orbit formation parameters, specifically based on the major axis of the elliptical orbit, the minor axis of the elliptical orbit, and the angular velocity of the UAV flying around the elliptical orbit. The direction of the elliptical orbit is consistent with the angle of the direction of motion, so that the UAV can fly in elliptical orbit formation, achieve extensive monitoring coverage of the area around the convoy, and adapt to changes in the direction of motion of the convoy.

[0026] In this embodiment of the invention, every time an optimization cycle is performed, the optimization objective is to maximize the total area of ​​the UAV's monitoring area within the optimization cycle. The constraints are UAV flight speed and flight trajectory parameter range. The major axis, minor axis, and angular velocity of the UAV flying around the elliptical orbit are optimized online to update the UAV flight trajectory parameterization model. The UAV's flight trajectory is then adjusted based on the updated UAV flight trajectory parameterization model.

[0027] In this embodiment of the invention, at the initial moment, the major axis of the elliptical orbit, the minor axis of the elliptical orbit, and the angular velocity of the UAV flying around the elliptical orbit in the parameterized model of the UAV flight trajectory adopt the preset initial values.

[0028] The UAV dynamic path planning method for mobile convoy monitoring provided in this invention acquires the position of each vehicle in the convoy in real time and dynamically calculates the convoy's centroid and direction of motion. It uses a UAV flight trajectory parameterization model based on elliptical orbit formation parameters to adjust the UAV flight trajectory in real time. At each optimization cycle, the optimization objective is to maximize the total area of ​​the UAV's monitoring area within the optimization cycle. Using UAV flight speed constraints and flight trajectory parameter range constraints as constraints, the major axis, minor axis, and angular velocity of the UAV flying around the elliptical orbit are optimized online to update the UAV flight trajectory parameterization model. This method enables real-time, continuous, and efficient monitoring of dynamic convoys and significantly improves the monitoring coverage quality and resource utilization efficiency in complex dynamic environments.

[0029] Furthermore, in this embodiment of the invention, considering that the convoy contains multiple vehicles, the centroid position of the convoy is calculated based on the location information using the following formula: ; ; in, This indicates a vehicle index. Indicates the number of vehicles. Represents a time variable. Indicates the first One vehicle Time Axis coordinates Indicates the first One vehicle Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates.

[0030] It should be noted that, in this embodiment of the invention, a local tangent plane coordinate system, such as the northeast-sky coordinate system, is used as the reference coordinate system for spatial position calculation. Axis coordinates and The axis coordinates are all coordinate values ​​in the set local tangent plane coordinate system.

[0031] In this embodiment of the invention, the centroid coordinates of the convoy are determined by calculating the arithmetic mean of the position coordinates of all vehicles. This method can concisely and effectively abstract the discretely distributed convoy into a unified reference point, providing a clear basis for the center positioning of the UAV's elliptical orbit formation. This facilitates the UAV's formation monitoring of the entire convoy rather than individual vehicles.

[0032] Furthermore, in this embodiment of the invention, the angle of motion direction of the convoy is determined based on the change in the position of the convoy's center of mass.

[0033] In this embodiment of the invention, the angle of the vehicle's direction of motion at the current moment is determined based on the position of the vehicle's center of mass at the current moment and the position of the vehicle's center of mass at the previous moment.

[0034] Specifically, in this embodiment of the invention, the angle of the vehicle's direction of motion is calculated using the following formula: ; ; ; in, Indicates the team is The direction and angle of time's movement This indicates that the team's center of gravity is at Time Axis coordinates and in Time The displacement between the axes. This indicates that the team's center of gravity is at Time Axis coordinates and in Time The displacement between the axes. This indicates that the team's center of gravity is at Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates This indicates the preset location information collection time interval. This indicates the arctangent function operation.

[0035] Among them, the location information collection time interval Configure according to actual needs.

[0036] In this embodiment of the invention, by using the displacement of the centroid at adjacent moments to calculate the motion direction angle of the convoy, the overall motion trend of the convoy can be reflected in real time and accurately, thereby ensuring that the direction of the elliptical trajectory of the UAV is dynamically consistent with the actual forward direction of the convoy, and enhancing the pertinence and effectiveness of convoy monitoring.

[0037] Furthermore, in this embodiment of the invention, the parameterized model of the UAV flight trajectory is represented as follows: ; in, Indicates the drone index. Represents a time variable. Indicates the first A drone Time Axis coordinates Indicates the first A drone Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates Indicates the team is The direction and angle of time's movement Indicates the major axis of the elliptical orbit. Indicates the minor axis of the elliptical orbit. This represents the angular velocity of the drone as it flies around an elliptical orbit. Indicates the number of drones. This represents the operation of the cosine function. This represents the operation of the sine function.

[0038] In this embodiment of the invention, by modeling the centroid of the convoy, the direction of motion of the convoy, the elliptical orbit formation parameters and the uniform phase distribution of the UAVs in a unified manner, multiple UAVs can work together to form an elliptical orbit formation that dynamically adapts to the translation and turning of the convoy, thereby achieving comprehensive and orderly monitoring coverage of the area surrounding the convoy.

[0039] Furthermore, in this embodiment of the invention, in order to ensure that the UAV can efficiently and accurately monitor the convoy and significantly improve the monitoring coverage quality and resource utilization efficiency in complex dynamic environments, a quantitative definition of monitoring quality is proposed, specifically using the total area of ​​the UAV's monitoring area to evaluate the UAV's monitoring quality.

[0040] In this embodiment of the invention, considering that there may be overlap between the monitoring areas of different drones, and that there may also be overlap between the monitoring areas of the same drone at different times, the total area of ​​the monitoring area of ​​the drones in the optimization period is determined based on the area of ​​the union of the monitoring areas of all drones in the optimization period.

[0041] In this embodiment of the invention, in order to facilitate the calculation and determination of the total area of ​​the UAV's monitoring area within the optimization period, an optimization period is discretized into multiple time points, and the area of ​​the union of the monitoring areas of all UAVs at all time points is calculated as the total area of ​​the UAV's monitoring area within the optimization period.

[0042] Specifically, based on the above definition, the total area of ​​the UAV's surveillance zone during the optimization period is determined in the following way: ; in, This represents the total area of ​​the drone's surveillance zone during the optimization period. Indicates the drone index. Indicates a point-in-time index. Indicates the number of drones. This represents the number of time points into which the optimization cycle is discretely divided. Indicates the first iteration within the optimization cycle. At a certain point in time, Indicates the first A drone Time monitoring area This represents the area function.

[0043] In this embodiment of the invention, the area of ​​the union of the surveillance areas of all UAVs at all points in time is calculated using existing computational geometry algorithms.

[0044] In this embodiment of the invention, the total area of ​​the surveillance area of ​​the UAV is accurately calculated by performing a union operation on the surveillance areas of all UAVs at all discrete time points. This effectively eliminates the overlap and redundancy between multiple UAVs and in the time dimension, thereby providing a quantitative indicator that truly reflects the surveillance quality for UAV trajectory optimization, which can significantly improve coverage efficiency and resource utilization.

[0045] Furthermore, in this embodiment of the invention, in order to facilitate calculation and solution, the monitoring area of ​​the UAV is defined as a square area centered on the current position of the UAV.

[0046] In this embodiment of the invention, the side length of the square region is determined based on the drone's flight altitude and field of view, specifically using the following formula: ; in, This represents the side length of the square region. Indicates the drone's flight altitude. Indicates the field of view of the drone. This represents the tangent function operation.

[0047] In this embodiment of the invention, by simplifying the monitoring area of ​​the UAV into a square area whose side length is uniquely determined by the flight altitude and field of view, the computational complexity of the monitoring area is effectively reduced, while the monitoring range can be adaptively adjusted according to the flight parameters of the UAV.

[0048] Furthermore, in this embodiment of the invention, based on the actual situation of the UAV, the UAV flight speed constraint can be expressed as: ; in, Indicates the flight speed of the drone. Represents a time variable. Indicates that drones are in Time Axis coordinates Indicates that drones are in Time Axis coordinates This represents the differentiation operator. This indicates the preset maximum flight speed.

[0049] In this embodiment of the invention, based on the actual situation of the UAV, the flight trajectory parameter range constraint can be expressed as: ; in, Indicates the major axis of the elliptical orbit. Indicates the minor axis of the elliptical orbit. This represents the angular velocity of the drone as it flies around an elliptical orbit. This represents the minimum value of the major axis of the preset elliptical orbit. This represents the maximum value of the major axis of the preset elliptical orbit. This represents the minimum value of the minor axis of the preset elliptical orbit. This represents the maximum value of the minor axis of the preset elliptical orbit. This represents the minimum angular velocity of the drone as it is intended to fly around an elliptical orbit. This indicates the maximum angular velocity of the drone as it is expected to fly around an elliptical orbit.

[0050] In an embodiment of the present invention, , , , , , , The specific values ​​should be set according to the actual situation.

[0051] Furthermore, in this embodiment of the invention, the major axis of the elliptical orbit, the minor axis of the elliptical orbit, and the angular velocity of the UAV flying around the elliptical orbit are used as optimization variables. The optimization objective is to maximize the total area of ​​the UAV's monitoring area within the optimization period. The UAV's flight speed constraint and flight trajectory parameter range constraint are used as constraints to construct an optimization problem. By solving the optimization problem, the major axis of the elliptical orbit, the minor axis of the elliptical orbit, and the angular velocity of the UAV flying around the elliptical orbit are optimized online.

[0052] In this embodiment of the invention, based on the optimization variables, optimization objectives, and constraints determined above, the following optimization problem is constructed: ; in, Represents the optimization variable. , This represents the objective function to be optimized. , Indicates that given optimization variables The total area of ​​the drone's surveillance area within the optimization cycle. Describes the optimization variable when the objective function is minimized. The value of .

[0053] In this embodiment of the invention, the Sequential Least Squares Quadratic Programming (SLSQP) algorithm is used to solve the above-mentioned optimization problem, thereby optimizing the major axis, minor axis, and angular velocity of the UAV flying around the elliptical orbit online. The Sequential Least Squares Quadratic Programming algorithm is suitable for handling optimization problems with both linear and nonlinear constraints and can efficiently find the optimal solution.

[0054] In this embodiment of the invention, by formalizing the surveillance task into an elliptical orbit formation parameter optimization problem constrained by flight capability and configuration boundaries, and using a sequential least squares quadratic programming algorithm to solve it online, the optimal UAV flight trajectory parameters that maximize the effective surveillance area can be obtained efficiently in a dynamic environment.

[0055] refer to Figure 2 This invention also provides a UAV dynamic path planning device for mobile fleet monitoring tasks, the device comprising: The location acquisition module 100 is used to acquire the location information of each vehicle in the convoy to be monitored. The state calculation module 200 is used to calculate the center of mass position and direction of motion angle of the convoy based on the position information. The trajectory optimization module 300 is used to adjust the flight trajectory of the UAV based on the centroid position and motion direction angle of the convoy using a predefined UAV flight trajectory parameterization model. The UAV flight trajectory parameterization model is defined based on the major axis and minor axis of the elliptical orbit and the angular velocity of the UAV flying around the elliptical orbit, and the direction of the elliptical orbit is consistent with the motion direction angle. The trajectory optimization module 300 optimizes the total area of ​​the UAV's monitoring area within each optimization cycle. The optimization objective is to maximize the total area of ​​the UAV's monitoring area within the optimization cycle. The module uses the UAV's flight speed constraint and flight trajectory parameter range constraint as constraints to optimize the major axis, minor axis, and angular velocity of the UAV around the elliptical orbit online. The total area of ​​the UAV's monitoring area within the optimization cycle is determined by the area of ​​the union of the monitoring areas of all UAVs within the optimization cycle. The UAV's monitoring area is defined as a square area centered on the UAV's current position. The side length of the square area is determined based on the UAV's flight altitude and field of view.

[0056] The UAV dynamic path planning device for mobile convoy monitoring tasks provided in this embodiment of the invention consists of modules corresponding to the steps of the above method, and can realize all the processes of the above-mentioned UAV dynamic path planning method for mobile convoy monitoring tasks. The specific working principle, function and technical effect of each module are the same as those of the above-mentioned UAV dynamic path planning method for mobile convoy monitoring tasks, and will not be repeated here.

[0057] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Additionally, the terms "front," "back," "left," "right," "upper," and "lower" in this document refer to the placement shown in the accompanying drawings.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic path planning method for unmanned aerial vehicles (UAVs) for mobile vehicle fleet surveillance tasks, characterized in that, include: Obtain the location information of each vehicle in the convoy to be monitored; Based on the location information, the position of the center of mass and the angle of motion of the convoy are calculated, wherein the angle of motion of the convoy is calculated using the displacement of the center of mass of the convoy at adjacent moments; Based on the centroid position and motion direction angle of the convoy, the flight trajectory of the UAV is adjusted using a predefined UAV flight trajectory parameterization model. The UAV flight trajectory parameterization model is defined based on the major axis and minor axis of the elliptical orbit and the angular velocity of the UAV flying around the elliptical orbit, and the direction of the elliptical orbit is consistent with the motion direction angle. In this process, every optimization cycle aims to maximize the total area of ​​the UAV's monitoring area within that cycle. Constraints include UAV flight speed and flight trajectory parameter range. The major and minor axes of the elliptical orbit, along with the angular velocity of the UAV around the elliptical orbit, are optimized online. The total area of ​​the UAV's monitoring area within the optimization cycle is determined by the area of ​​the union of the monitoring areas of all UAVs within that cycle. The monitoring area of ​​the UAV is defined as a square region centered on the UAV's current position, with the side length of the square region determined based on the UAV's flight altitude and field of view. In this process, an optimization cycle is discretized into multiple time points, and the total area of ​​the monitoring area of ​​all UAVs at all time points is calculated as the area of ​​the monitoring area of ​​the UAVs within the optimization cycle. The parameterized model of the UAV flight trajectory is represented as follows: ； in, Indicates the drone index. Represents a time variable. Indicates the first A drone Time Axis coordinates Indicates the first A drone Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates Indicates the team is The direction and angle of time's movement Indicates the major axis of the elliptical orbit. Indicates the minor axis of the elliptical orbit. This represents the angular velocity of the drone as it flies around an elliptical orbit. Indicates the number of drones. This represents the operation of the cosine function. This represents the operation of the sine function.

2. The UAV dynamic path planning method for mobile vehicle fleet monitoring tasks according to claim 1, characterized in that, Based on the location information, the center of gravity position of the convoy is calculated using the following formula: ； ； in, This indicates a vehicle index. Indicates the number of vehicles. Represents a time variable. Indicates the first One vehicle Time Axis coordinates Indicates the first One vehicle Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates.

3. The UAV dynamic path planning method for mobile vehicle fleet monitoring tasks according to claim 2, characterized in that, Based on the location information, the angle of motion direction of the convoy is calculated using the following formula: ； ； ； in, Indicates the team is The direction and angle of time's movement This indicates that the team's center of gravity is at Time Axis coordinates and in Time The displacement between the axes. This indicates that the team's center of gravity is at Time Axis coordinates and in Time The displacement between the axes. This indicates that the team's center of gravity is at Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates This indicates the preset location information collection time interval. This indicates the arctangent function operation.

4. The UAV dynamic path planning method for mobile vehicle fleet monitoring tasks according to claim 1, characterized in that, The total area of ​​the UAV's surveillance zone during the optimization period is determined in the following way: ； in, This represents the total area of ​​the drone's surveillance zone during the optimization period. Indicates the drone index. Indicates a point-in-time index. Indicates the number of drones. This represents the number of time points into which the optimization cycle is discretely divided. Indicates the first iteration within the optimization cycle. At a certain point in time, Indicates the first A drone Time monitoring area This represents the area function.

5. The UAV dynamic path planning method for mobile vehicle fleet monitoring tasks according to claim 1, characterized in that, The side length of the square region is determined by the following formula: ； in, This represents the side length of the square region. Indicates the drone's flight altitude. Indicates the field of view of the drone. This represents the tangent function operation.

6. The UAV dynamic path planning method for mobile vehicle fleet monitoring tasks according to claim 1, characterized in that, The drone flight speed constraint is expressed as follows: ； in, Indicates the flight speed of the drone. Represents a time variable. Indicates that drones are in Time Axis coordinates Indicates that drones are in Time Axis coordinates This represents the differentiation operator. This indicates the preset maximum flight speed.

7. The UAV dynamic path planning method for mobile vehicle fleet monitoring tasks according to claim 1, characterized in that, The flight trajectory parameter range constraint is expressed as follows: ； in, Indicates the major axis of the elliptical orbit. Indicates the minor axis of the elliptical orbit. This represents the angular velocity of the drone as it flies around an elliptical orbit. This represents the minimum value of the major axis of the preset elliptical orbit. This represents the maximum value of the major axis of the preset elliptical orbit. This represents the minimum value of the minor axis of the preset elliptical orbit. This represents the maximum value of the minor axis of the preset elliptical orbit. This represents the minimum angular velocity of the drone as it is intended to fly around an elliptical orbit. This indicates the maximum angular velocity of the drone as it is expected to fly around an elliptical orbit.

8. The UAV dynamic path planning method for mobile vehicle fleet monitoring tasks according to claim 1, characterized in that, The major axis, minor axis, and angular velocity of the UAV flying around the elliptical orbit are optimized online using a sequential least squares quadratic programming algorithm.

9. A dynamic path planning device for unmanned aerial vehicles (UAVs) for mobile convoy surveillance tasks, characterized in that, include: The location acquisition module is used to acquire the location information of each vehicle in the convoy to be monitored; The state calculation module is used to calculate the center of mass position and motion direction angle of the convoy based on the position information, wherein the motion direction angle of the convoy is calculated using the center of mass displacement of the convoy at adjacent moments; The trajectory optimization module is used to adjust the flight trajectory of the UAVs based on the centroid position and motion direction angle of the convoy using a predefined UAV flight trajectory parameterization model. The UAV flight trajectory parameterization model is defined based on the major axis and minor axis of the elliptical orbit and the angular velocity of the UAV flying around the elliptical orbit, and the direction of the elliptical orbit is consistent with the motion direction angle. The trajectory optimization module optimizes the total area of ​​the UAV's monitoring area within each optimization cycle. The optimization objective is to maximize the total area of ​​the UAV's monitoring area within that cycle. Constraints include the UAV's flight speed and flight trajectory parameter range. The major and minor axes of the elliptical orbit, along with the angular velocity of the UAV around the elliptical orbit, are used as optimization variables. The total area of ​​the UAV's monitoring area within each optimization cycle is determined by the area of ​​the union of the monitoring areas of all UAVs within that cycle. The monitoring area of ​​the UAV is defined as a square region centered on the UAV's current position, with the side length of the square region determined based on the UAV's flight altitude and field of view. In this process, an optimization cycle is discretized into multiple time points, and the total area of ​​the monitoring area of ​​all UAVs at all time points is calculated as the area of ​​the monitoring area of ​​the UAVs within the optimization cycle. The parameterized model of the UAV flight trajectory is represented as follows: ； in, Indicates the drone index. Represents a time variable. Indicates the first A drone Time Axis coordinates Indicates the first A drone Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates This indicates that the team's center of gravity is at Time Axis coordinates Indicates the team is The direction and angle of time's movement Indicates the major axis of the elliptical orbit. Indicates the minor axis of the elliptical orbit. This represents the angular velocity of the drone as it flies around an elliptical orbit. Indicates the number of drones. This represents the operation of the cosine function. This represents the operation of the sine function.

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