A method for controlling a UAV to circle based on quadratic programming

Abstract

The application belongs to the technical field of unmanned aerial vehicles, and relates to a method for surrounding control of an unmanned aerial vehicle based on quadratic programming, which comprises the following steps: acquiring the position and speed of the unmanned aerial vehicle, and the position and speed of a target; determining the distance r(t) between the unmanned aerial vehicle and the target according to the position of the unmanned aerial vehicle and the position of the target; determining a distance error Cost r according to r(t) and a distance expected value r0; determining a speed error Cost v according to the position of the unmanned aerial vehicle and the position of the target; constructing a target function J based on the distance error and the speed error; transforming the target function into a quadratic programming problem according to the speed of the unmanned aerial vehicle and the speed of the target in combination with a speed constraint and an acceleration constraint; obtaining optimal control instructions V u (t) by solving the quadratic programming problem based on a quadratic programming solver; and performing surrounding control on the unmanned aerial vehicle based on real-time control instructions V u (t). The method has the beneficial effect of realizing accurate and stable surrounding control of the unmanned aerial vehicle around a moving target (such as a ship) in a complex dynamic environment.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a UAV orbit control method based on quadratic programming. Background Technology

[0002] Drone orbit control is a technology that enables drones to fly stably and controllably around a specific target (such as a fixed point, moving object, or area). Its core objective is to achieve precise trajectory tracking of the drone around the target while maintaining a safe distance and relative position. Orbit control typically combines multiple technologies, including target localization, trajectory planning, real-time flight adjustments, and environmental perception and anti-interference capabilities. Drone orbit control is significant in various application scenarios. In tasks involving the monitoring, tracking, and data acquisition of moving targets, drone orbit control provides a continuous and stable observation perspective, ensuring comprehensive coverage of the target. For example, in reconnaissance and surveillance, drones can track enemy vehicles, personnel, and other moving targets in real time, acquiring dynamic information about the target through orbital flight to support tactical decision-making. In environmental monitoring, drone orbit control can be used to observe the movement patterns of wildlife or monitor moving objects in the ocean, such as ships, to collect environmental data and behavioral patterns. Furthermore, in emergency rescue scenarios, drone orbit control helps to quickly locate missing persons or monitor dynamic changes in disaster areas, providing crucial information for rescue operations. This technology not only improves the efficiency and accuracy of data acquisition but also reduces the risk of human intervention, demonstrating its unique value in modern technological applications. With technological advancements, drone surround control is moving towards intelligence, collaboration, and autonomy. For example, through multi-sensor fusion, reinforcement learning, and collaborative control technologies, the adaptability and mission execution capabilities of drones in complex environments are further enhanced.

[0003] Existing methods for UAV orbit control mainly include GPS-based positioning and waypoint planning control methods, which are simple and easy to implement, but heavily reliant on GPS signals and struggle to handle dynamic targets in complex environments. Distance- and azimuth-based control methods maintain the distance and azimuth between the UAV and the target to achieve orbiting, suitable for GPS-denied environments, but require high-precision measurement equipment. Event-triggered anti-interference orbit control methods reduce system load by decreasing the frequency of control signal updates, improving anti-interference capability and real-time performance. Rigid azimuth-based hunting control methods are suitable for multi-UAV cooperative orbiting, but require high levels of communication and coordination. Visual servoing-based orbit control methods adjust flight trajectories through visual feedback, exhibiting autonomy and flexibility, but may be unstable under complex lighting conditions. Reinforcement learning-based orbit control methods learn optimal flight strategies to adapt to complex environments, but the training process is time-consuming and policy convergence is difficult to guarantee. Each of these methods has its advantages and disadvantages, and is suitable for different application scenarios and environmental conditions.

[0004] Existing UAV control methods mainly focus on path planning, obstacle avoidance, and formation control, but research on precise orbital control of UAVs around moving targets (such as ships) is relatively limited. In current technologies, UAV orbital control typically relies on preset paths or simple geometric models. These methods perform well in static environments, but often fail to meet the requirements for precise control in dynamic environments (such as scenarios where the target's position and velocity are constantly changing). Existing methods often do not adequately consider the physical limitations of UAVs (such as maximum speed and acceleration limits), potentially leading to control failures in practical applications. Furthermore, existing methods typically employ discrete pathpoint tracking, where the UAV moves according to preset pathpoints. This results in discrete and discontinuous control inputs, affecting the smoothness and accuracy of the UAV's movement.

[0005] Existing drone orbit control technology has the following main drawbacks:

[0006] 1. Insufficient adaptability: Existing methods are poorly adaptable in dynamic environments, making it difficult to adjust the drone's movement in real time to maintain a fixed distance and circular trajectory from the target.

[0007] 2. Insufficient multi-objective optimization: Existing methods usually only focus on a single control objective (such as distance maintenance or trajectory tracking), and lack comprehensive optimization for multiple control objectives (such as simultaneously maintaining distance and maintaining circular motion).

[0008] 3. Inadequate consideration of physical limitations: Existing methods often ignore the speed and acceleration limitations of drones, which may lead to situations that exceed physical limitations in practical applications, affecting the feasibility and safety of control.

[0009] 4. Discontinuous control input: Existing methods typically employ discrete pathpoint tracking, resulting in discontinuous control input and affecting the stability and accuracy of the UAV's motion. Summary of the Invention

[0010] Technical problems to be solved

[0011] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a UAV orbit control method based on quadratic programming, which solves the technical problem that the existing UAV orbit control methods have limitations in complex environments and dynamic target tracking.

[0012] Technical solution

[0013] To achieve the above objectives, the main technical solutions adopted by the present invention include:

[0014] In a first aspect, the present invention provides a UAV orbit control method based on quadratic programming, comprising:

[0015] Establish a coordinate system with the UAV takeoff point as the origin, the east direction as the positive x-axis, and the north direction as the positive y-axis;

[0016] Obtain the location P of the drone u (t)=(x u ,y u ) and velocity V u (t)=(v ux ,v uy ), Target location P s (t)=(x s ,y s ) and velocity V s (t)=(v sx ,v sy ), where x u Let x be the position of the drone on the x-axis, and y be the position of the drone on the x-axis. u Let v be the position of the drone on the y-axis. ux Let v be the velocity of the drone in the x-axis direction. uy Let x be the velocity of the drone in the y-axis direction. s Let x be the position of the target on the x-axis, y s v is the position of the target on the y-axis. sx Let v be the velocity of the target in the x-axis direction. st The velocity of the target in the y-axis direction;

[0017] The distance r(t) between the UAV and the target is determined based on the positions of the UAV and the target.

[0018] The distance error Cost is determined based on r(t) and the expected distance value r0. r ;

[0019] Determine the velocity error (Cost) based on the drone's position and the target's position. v ;

[0020] The objective function J is constructed based on the distance error and velocity error;

[0021] Based on the speed of the UAV and the speed of the target, combined with speed and acceleration constraints, the objective function is transformed into a quadratic programming problem.

[0022] The optimal control command V is obtained by solving the quadratic programming problem using a quadratic programming solver. u (t);

[0023] Based on real-time control command V u (t) Perform orbital control on the UAV.

[0024] Optionally, the distance between the UAV and the target is r(t) = (xu -x s ) 2 +(y u -y s ) 2 .

[0025] Optionally, distance error Cost r =(r(t)-r0) 2 .

[0026] Optionally, speed error Cost v =(v r (t)) 2 +(v tan (t)-v desired ) 2 , where v r (t) is the radial velocity, v r (t)=ε(P u (t)-P s (t)-r0) 2 ε is the integral term, v tan (t) is the tangential velocity. v desired It is the set desired tangential velocity.

[0027] Alternatively, the objective function J = λ r Cost r +λ v Cost v , where λ r and λ v It is the weighting coefficient.

[0028] Optionally, the velocity constraint is ||V u (t)||≤V max , where V max This is the maximum speed of the drone.

[0029] Optionally, the acceleration constraint is ||V u (t)-V u (t-Δt)||≤a max , where V u (t) represents the velocity of the drone in the current cycle, V u (t-Δt) represents the speed of the UAV in the previous cycle.

[0030] Optionally, based on the speed of the UAV and the speed of the target, and combining speed constraints and acceleration constraints, the objective function can be transformed into a quadratic programming problem as follows:

[0031]

[0032] stAVu ≤b,CV u =d

[0033] Among them, V u Let H be the speed of the drone, and H be the quadratic term matrix of the objective function. I is the identity matrix; f is the linear term vector. T stands for transpose operation.

[0034] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed, implements the UAV orbit control method based on quadratic programming described in any of the first aspects above.

[0035] Thirdly, the present invention provides a storage device, including a storage medium and a processor, wherein the storage medium stores a computer program, and when the program is executed by the processor, it implements the UAV orbit control method based on quadratic programming described in any of the first aspects above.

[0036] Beneficial effects

[0037] The beneficial effects of this invention are as follows: This invention provides a quadratic programming-based UAV orbit control method, transforming the UAV orbit control problem into a quadratic programming optimization problem. The orbit control objective is achieved by minimizing distance and velocity errors. Employing continuous velocity adjustment instead of discrete pathpoint tracking significantly improves the smoothness and accuracy of the UAV's motion. Introducing weighting coefficients λr and λv flexibly adjusts the priority between distance maintenance and circular motion maintenance, adapting to different application scenarios. Considering the speed and acceleration limitations of the UAV ensures the feasibility and safety of control commands, avoiding control failures due to exceeding physical limitations. A general optimization framework is provided, which can be flexibly adjusted according to different objectives and constraints, exhibiting good scalability and adaptability. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the UAV orbiting trajectory result provided by the quadratic planning orbiting controller in an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of the simulation results of the circular motion trajectory of the quadratic planning orbital controller provided in an embodiment of the present invention;

[0040] Figure 3 A schematic diagram of the UAV velocity simulation results for a quadratic programming orbit controller provided in an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of the UAV orbiting trajectory result provided by the waypoint tracking orbiting controller in an embodiment of the present invention;

[0042] Figure 5 A schematic diagram of the simulation results of the circular motion trajectory of the waypoint tracking surround controller provided in an embodiment of the present invention;

[0043] Figure 6 This is a schematic diagram of the UAV speed simulation results for the waypoint tracking and orbiting controller provided in an embodiment of the present invention. Detailed Implementation

[0044] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] In the field of UAV orbiting motion control, quadratic programming methods demonstrate significant advantages over conventional tracking point methods. Conventional tracking point methods typically rely on preset waypoints, with the UAV orbiting the target by sequentially flying to these waypoints. However, this method often proves rigid in complex environments or with dynamic targets, struggling to flexibly adjust flight paths and exhibiting poor adaptability to environmental changes. In contrast, quadratic programming methods optimize the UAV's flight trajectory in real time, enabling more flexible responses to target motion changes and environmental uncertainties. It not only ensures stable target tracking while maintaining a safe distance but also effectively reduces trajectory deviations and improves tracking accuracy. Furthermore, quadratic programming methods excel in handling multiple constraints, generating optimal flight paths while satisfying various flight limitations (such as speed and acceleration limits), thereby improving UAV flight efficiency and mission completion quality. Regarding trajectory smoothness, the trajectory generated by the optimization algorithm of quadratic programming methods is smoother, helping to reduce UAV energy consumption and improve flight stability. Simultaneously, its online planning capability allows for rapid response to environmental changes and real-time adjustments to flight strategies, ensuring successful mission execution. In summary, the quadratic programming method outperforms the traditional tracking point method in terms of flexibility, accuracy, adaptability, and efficiency, providing a more advanced and reliable solution for UAV orbital motion control.

[0046] This invention addresses the limitations of existing UAV orbit control methods in complex environments and dynamic target tracking by proposing a quadratic programming-based UAV orbit control method. Existing methods, such as the conventional tracking point method, often struggle to achieve flexible and precise flight control when faced with rapidly changing targets, complex environmental obstacles, and multiple constraints, exhibiting problems such as large trajectory deviations, poor real-time performance, and insufficient adaptability. This invention introduces a quadratic programming optimization algorithm, decomposing the UAV orbit control into radial and tangential constraints to ensure orbit control. Furthermore, it adjusts the UAV's flight trajectory in real time to ensure that multiple constraints, including velocity, acceleration, and safe distance, are satisfied on top of the radial and tangential constraints, generating an optimal orbit path. This method effectively addresses dynamic target changes and environmental uncertainties, improving UAV flight stability and trajectory accuracy. Moreover, through online planning and real-time feedback adjustments, this invention significantly enhances the UAV's adaptability and mission execution capabilities in complex environments, reduces the need for manual intervention, and improves data acquisition efficiency and quality, providing a more advanced and reliable solution for UAV applications in monitoring, tracking, and data acquisition. The present invention aims to provide a UAV orbit control method based on quadratic programming to overcome the shortcomings of the prior art and achieve accurate and stable orbit control of UAVs around moving targets (such as ships) in complex dynamic environments.

[0047] Specific objectives include:

[0048] Maintain a fixed distance: This enables the drone to adjust its position in real time and maintain a fixed distance radius R from the target.

[0049] Maintaining circular motion: Ensures that the drone's trajectory conforms to a circular path, improving the accuracy of orbit control.

[0050] Physical limitations must be taken into full account: the speed and acceleration limitations of the drone must be fully considered to ensure the feasibility and safety of control commands.

[0051] Provides a flexible optimization framework: Through quadratic programming, a flexible optimization framework is provided, which can adjust the optimization objectives and constraints according to different scenarios and needs.

[0052] Achieve continuous control input: Transform the control input of the UAV into a continuous input, avoid the discontinuity caused by discrete path point tracking, and improve the stability and accuracy of the UAV's motion.

[0053] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present invention can be understood more clearly and thoroughly, and that the scope of the present invention can be fully conveyed to those skilled in the art.

[0054] Firstly, this embodiment provides a UAV orbit control method based on quadratic programming, including:

[0055] This embodiment considers the control problem of a drone moving in a circular motion around a moving target (such as a ship).

[0056] S1. Establish a coordinate system with the UAV takeoff point as the origin, the east direction as the positive x-axis, and the north direction as the positive y-axis.

[0057] S2, Obtain the drone's location P u (t)=(x u ,y u ) and velocity V u (t)=(v ux v uy ), Target location P s (t)=(x s y s ) and velocity V s (t)=(v sx v sy ).

[0058] Where, x u Let x be the position of the drone on the x-axis, and y be the position of the drone on the x-axis. u Let v be the position of the drone on the y-axis. ux Let v be the velocity of the drone in the x-axis direction. uy Let x be the velocity of the drone in the y-axis direction. s Let x be the position of the target on the x-axis, y s v is the position of the target on the y-axis. sx Let v be the velocity of the target in the x-axis direction. sy Let be the velocity of the target in the y-axis direction.

[0059] The controller's goal is to output the drone's speed V. u (t)=(v ux ,v uy This allows the drone to meet the following conditions when orbiting the target:

[0060] 1. Maintain a fixed distance (i.e., the desired radius) relative to the target.

[0061] 2. Make the drone's movement conform to a circular trajectory.

[0062] To achieve the above objectives, this invention transforms the problem into a quadratic programming optimization problem, including the objective function and constraints.

[0063] The objective function represents the error that we want to minimize.

[0064] Maintain distance:

[0065] S3, determine the distance r(t) between the UAV and the target based on the position of the UAV and the position of the target.

[0066] Optionally, the distance between the UAV and the target is r(t) = (x u -x s ) 2 +(y u -y s ) 2 .

[0067] S4, determine the distance error Cost based on r(t) and the expected distance value r0. r .

[0068] Optionally, distance error Cost r =(r(t)-r0) 2 .

[0069] Cost r =((x) u -x s ) 2 +(y u -y s ) 2 -r0) 2 .

[0070] Maintaining circular motion: The speed of the drone should be such that it moves in a circle.

[0071] S5 determines the velocity error Cost based on the drone's position and the target's position. v .

[0072] Optionally, speed error Cost v =(v r (t)) 2 +(v tan (t)-v desired ) 2 , where v r (t) is the radial velocity, v r (t)=ε(P u (t)-P s (t)-r0) 2 ε is the integral term, vtan (t) is the tangential velocity. v desired The desired tangential velocity is determined by the angular velocity ω of the given UAV orbiting the target, i.e., v desired =ω×r0.

[0073] S6, construct the objective function J based on distance error and velocity error.

[0074] Alternatively, the objective function J = λ r Cost r +λ v Cost v , where λ r and λ v These are weighting coefficients used to adjust the importance of distance error and velocity error in the overall target.

[0075] S7 transforms the objective function into a quadratic programming problem by combining the speed of the UAV and the speed of the target with speed and acceleration constraints.

[0076] Optionally, the velocity constraint is ||V u (t)||≤V max , where V max This is the maximum speed of the drone.

[0077] Optionally, the acceleration constraint is ||V u (t)-V u (t-Δt)||≤a max , where V u (t) represents the velocity of the drone in the current cycle, V u (t-Δt) represents the speed of the UAV in the previous cycle.

[0078] Optionally, based on the speed of the UAV and the speed of the target, and combining speed constraints and acceleration constraints, the objective function can be transformed into a quadratic programming problem as follows:

[0079]

[0080] stAV u ≤b,CV u =d

[0081] Among them, V u Let H be the speed of the drone, and H be the quadratic term matrix of the objective function. I is the identity matrix; f is a linear term vector.

[0082] T stands for transpose operation.

[0083] Decision variable (to be optimized): UAV velocity vector V u (t)=(v ux ,v uy ).

[0084] Given parameters:

[0085] Target position (x) s ,y s ), velocity (v) sx ,v sy (Real-time measurement value);

[0086] Desired radius r0, desired tangential velocity v desired (Task setting parameters);

[0087] Maximum speed V max Maximum acceleration a max (UAV physical parameters);

[0088] Weighting coefficient λ r , λ v (Optimized parameters adjusted according to the scenario).

[0089] The above quadratic programming problem is obtained by constructing a matrix based on the given parameters.

[0090] S8, Solve the quadratic programming problem using a quadratic programming solver to obtain the optimal control command V. u (t).

[0091] Quadratic programming solvers include: cvxopt and qpOASES.

[0092] S9, based on real-time control commands V u (t) Perform orbital control on the UAV.

[0093] This invention uses a quadratic programming method to transform the control input of a UAV into continuous speed adjustment, instead of traditional discrete pathpoint tracking. This approach ensures smoother UAV movement and avoids the jitter and instability caused by discrete control inputs.

[0094] This embodiment provides a quadratic programming-based UAV orbit control method. Through optimization algorithms, the UAV can accurately maintain a fixed distance from the target and move stably along a circular trajectory, significantly improving the accuracy and stability of orbit control. Continuous control input significantly improves the UAV's motion smoothness, reducing jitter and instability caused by discrete control inputs. It can adapt to dynamic changes in the target (such as a ship), ensuring the UAV's orbital motion remains unaffected even in complex dynamic environments. It considers the speed and acceleration limitations of the UAV, avoiding control failures due to exceeding physical limits, thus improving system safety and reliability. A general optimization framework is provided, capable of adjusting optimization objectives and constraints according to different scenarios and needs, exhibiting good flexibility and scalability.

[0095] To verify the effectiveness of the above method, a simulation comparison experiment was designed as follows:

[0096] Experimental setup:

[0097] Comparison method: Ordinary tracking point method (preset waypoint tracking).

[0098] Dynamic target model: Ship's variable speed broken line motion (speed range 0m / s~5m / s).

[0099] Drone parameters: Maximum speed V max =10m / s, maximum acceleration a max =2m / s 2 .

[0100] The desired circumferential radius is r0 = 50m.

[0101] Optimize weights: λ r =10, λ v =5, control period Δt = 0.1s.

[0102] Simulation platform: MATLAB / Simulink, QP solver: qpOASES.

[0103] The experimental results are shown in Table 1:

[0104] Table 1

[0105]

[0106]

[0107] Key performance analysis:

[0108] High-precision tracking: comparison Figure 1 and Figure 4 When the target speed changes, the distance error of this invention is controlled within ±0.5m (compared to ±5m for ordinary methods).

[0109] Dynamic Adaptability: Comparison Figure 2 and Figure 5 When the target suddenly changes direction, the present invention can restore a stable circular trajectory within 3 seconds, while the conventional method requires 12 seconds and suffers from overshoot oscillation.

[0110] Stability advantage: comparison Figure 3 and Figure 6 Continuous speed control avoids the jitter of discrete tracking, reduces the speed standard deviation by 75%, and is more suitable for scenarios that require a stable viewpoint, such as aerial photography and monitoring.

[0111] Constraints and Guarantees: V is satisfied throughout the process u ≤10m / s and a u ≤2m / s 2 There are no physical limits.

[0112] In a second aspect, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed, implements the UAV orbit control method based on quadratic programming described in any of the first aspects above.

[0113] Thirdly, embodiments of the present invention provide a storage device, including a storage medium and a processor, wherein the storage medium stores a computer program, and when the program is executed by the processor, it implements the UAV orbit control method based on quadratic programming described in any of the first aspects above.

[0114] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, then this invention should also include these modifications and variations.

[0116] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for controlling the orbit of an unmanned aerial vehicle (UAV) based on quadratic programming, characterized in that, include: Establish a coordinate system with the UAV takeoff point as the origin, the east direction as the positive x-axis, and the north direction as the positive y-axis; Get the location of the drone and speed Target location and speed ,in, Let the drone be positioned on the x-axis. Let the drone be positioned on the y-axis. Let x be the velocity of the drone in the x-axis direction. Let be the velocity of the drone in the y-axis direction. The position of the target on the x-axis. The target's position on the y-axis. Let the velocity of the target be in the x-axis direction. The velocity of the target in the y-axis direction; Determine the distance between the drone and the target based on the drone's position and the target's position. ; according to and expected distance Determine distance error ; Determine the velocity error based on the positions of the drone and the target. ; Construct an objective function based on distance error and velocity error. ; Based on the speeds of the UAV and the target, and combining speed and acceleration constraints, the objective function is transformed into a quadratic programming problem, as follows: ； ； in, Let H be the speed of the drone, and H be the quadratic term matrix of the objective function. , f is the identity matrix; f is the linear term vector. , , , , T represents the transpose operation. The weighting coefficients for the expected radius. V is the weighting coefficient for velocity. max For the maximum speed, a max This is the maximum acceleration; The optimal control command is obtained by solving the quadratic programming problem using a quadratic programming solver. ; Based on real-time control commands To control the drone in a circular motion.

2. The UAV orbit control method based on quadratic programming according to claim 1, characterized in that, Distance between the drone and the target .

3. The UAV orbit control method based on quadratic programming according to claim 2, characterized in that, Distance error .

4. The UAV orbit control method based on quadratic programming according to claim 3, characterized in that, speed error ,in, It is radial velocity. , For integration, It is the tangential velocity. , It is the set desired tangential velocity.

5. The UAV orbit control method based on quadratic programming according to claim 4, characterized in that, objective function ,in, and It is the weighting coefficient.

6. The UAV orbit control method based on quadratic programming according to claim 5, characterized in that, Speed ​​constraint is ,in, This is the maximum speed of the drone.

7. The UAV orbit control method based on quadratic programming according to claim 6, characterized in that, Acceleration constraints are ,in, The speed of the drone in its current cycle. This refers to the speed of the drone in the previous cycle.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the UAV orbit control method based on quadratic programming as described in any one of claims 1 to 7.

9. A storage device comprising a storage medium and a processor, wherein the storage medium stores a computer program, characterized in that, When the processor executes the computer program, it implements the UAV orbit control method based on quadratic programming as described in any one of claims 1 to 7.

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