Unmanned aerial vehicle visual servo method based on model predictive control under time-varying visualization constraint

Abstract

The invention particularly relates to an unmanned aerial vehicle cluster visual servo method based on model prediction control under time-varying visualization constraint, and belongs to the field of unmanned aerial vehicle visual servo. Firstly, a virtual camera coordinate system is constructed, and an image moment of a tracking target on the virtual camera coordinate system is obtained through image processing to describe the relative distance between the unmanned aerial vehicle and the target. Because the camera is fixed on the unmanned aerial vehicle, the invention designs a time-varying visualization constraint to describe the view field range changing along with the attitude of the unmanned aerial vehicle. Then, a model predictive control algorithm is provided to solve the visual servo problem with time-varying visualization constraints, and it is ensured that the tracking target is always within the field of view of the unmanned aerial vehicle cluster.

Description

Technical Field

[0001] This invention relates to the field of UAV visual servoing, and specifically to a UAV visual servoing method based on model predictive control under time-varying visualization constraints. Background Technology

[0002] Unmanned aerial vehicles (UAVs) are aerial delivery platforms equipped with advanced sensing technologies, autonomous decision-making algorithms, and intelligent control systems. Thanks to their flexible maneuverability and payload capacity, UAVs play a crucial role in aerial missions such as environmental monitoring, traffic control, material delivery, and geographic mapping. In environments where GPS signals are denied, quadcopters equipped with aerial cameras can be guided to predetermined targets based on image data and visual servoing technology. Therefore, research related to UAV visual servoing has received increasing attention in recent years. To obtain real-time status feedback during visual servoing, keeping the target within the camera's field of view is crucial, which presents new challenges to controller design.

[0003] In the process of visual servo control, the roll and pitch movements of the UAV will significantly affect its visual range. In order to solve the problem of target loss, many studies have simplified the visualization constraints, assuming that the roll and pitch angles of the UAV change little and that the influence of these changes on the visualization constraints can be ignored, thus designing fixed visualization constraints ([1] Zhang K, Shi Y, Sheng H. Robust nonlinear model predictive control based visual servoing of quadrotor UAVs[J]. IEEE / ASMETransactions on Mechatronics, 2021, 26(2): 700-708. [2] Xie H and Lynch AF, State transformation-based dynamic visual servoing for an unmanned aerial vehicle[J]. Int. J. Control, 2016, 89(5): 892–908.). However, this fixed visualization constraint strategy severely limits the motion performance of the UAV and is difficult to meet the urgent needs of high maneuverability and high flexibility of the UAV in practical applications. Therefore, designing a visual constraint that changes with the UAV's attitude can fully unleash the UAV's flight potential and significantly improve control performance. Model predictive control (MPC) has significant advantages in solving UAV visual servoing problems due to its constraint handling capabilities and rolling optimization mechanism. To this end, MPC is used to solve the UAV visual servoing problem with time-varying visual constraints, ensuring that the tracked target remains within its field of view while efficiently controlling the UAV to complete the tracking task.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To address the problem of fixed visualization constraints limiting the flexibility and maneuverability of UAVs in existing visual servoing processes, this invention provides a UAV visual servoing method based on model predictive control under time-varying visualization constraints.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to a first aspect of the present invention, a visual servoing method for unmanned aerial vehicles (UAVs) based on model predictive control under time-varying visualization constraints is provided, the method comprising: Step 1: Establish the virtual camera coordinate system for the drone; Step 2: Extract pixel feature points of the target being tracked using image processing, and then calculate the image moments of the target being tracked in the virtual camera coordinate system; Step 3: Define the image moments of the tracked target as the UAV tracking error, and construct the tracking error update equation based on the UAV's dynamic model; Step 4: Map the visualization range of the airborne camera onto the virtual camera coordinate system, and design visualization constraints that change with the attitude of the UAV; Step 5: Design a model predictive control algorithm to solve the visual servoing problem with time-varying visualization constraints, ensuring that the target being tracked by the UAV swarm is always within its field of view during the tracking process; Step 6: Design a dual-loop control framework for the UAV. The outer loop vision controller calculates the UAV acceleration and yaw rate required by the inner loop controller based on the image features of the target being tracked. The inner loop tracking controller drives the quadcopter UAV to the desired attitude, so that the UAV swarm can complete the target tracking task in a set formation while ensuring that the tracked object is always within the UAV's field of vision.

[0008] In some exemplary embodiments, the virtual camera coordinate system of the UAV is specifically as follows: Define the world coordinate system as The coordinate system of the airborne camera is Drones in the world coordinate system The attitude, linear velocity, and angular velocity in the figure are respectively , and ,in, For roll angle, For pitch angle and Yaw angle; The transformation matrix from the camera coordinate system to the world coordinate system is:

[0009] in, , ; The imaging plane is defined as the plane perpendicular to the z-axis of the camera coordinate system. In the image plane, the tracked target is composed of its contour pixels. For a point in the camera coordinate system Its projection in the real image plane can be based on We obtain the coordinate system, where λ is the camera focal length; to facilitate target feature extraction and UAV control, a virtual camera coordinate system is designed. The virtual camera coordinate system With camera coordinate system They have the same origin, and their z-axis is parallel to the world coordinate system; UAV in virtual camera coordinate system The position and linear velocity in the middle are respectively and Subsequently, a virtual image plane was constructed in the virtual camera coordinate system, parallel to the plane formed by the x and y axes of the world coordinate system. To facilitate drone control, the coordinates of the tracked target were transformed from the image plane to the virtual image plane. .

[0010] In some exemplary embodiments, the image moments of the tracking target in the camera coordinate system are specifically:

[0011] in, and It is the pixel center of the feature pattern in the virtual camera plane, and its calculation formula is: and ; and It is to track the target The th feature point τ The position of each point , When the UAV reaches the desired attitude, record the characteristic parameters at this time. .

[0012] In some exemplary embodiments, the tracking error update equation is specifically:

[0013] in, It is the image moment error. It is the yaw angle difference between the drone and the target being tracked. It is the yaw angle of the drone. and It is the relative image moment distance between the drone and the target being tracked. These are features in the target image moments that are highly correlated with the desired outcome. It tracks the height of the target in the camera coordinate system. and It tracks the velocity of the target in the virtual camera coordinate system. and For drones Velocity in the virtual camera coordinate system.

[0014] In some exemplary embodiments, the visual constraints of the design as the UAV attitude changes are specifically as follows:

[0015]

[0016]

[0017] in, The time-varying visual constraints are related to the upper bound of the UAV's error. , To adjust the parameters, To control the interval.

[0018] In some exemplary embodiments, the design model predictive control algorithm solves the visual servoing problem with time-varying visualization constraints, specifically as follows:

[0019] in, It is a collaborative weight matrix. It is a drone Image moment error, including and , It is based on the control input Predicted tracking error, It is a drone Received drone The prediction tracking error.

[0020] In some exemplary embodiments, the formula for calculating the desired pose is specifically as follows:

[0021]

[0022]

[0023]

[0024] in, and These are the desired speed and attitude, respectively.

[0025] According to a second aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the UAV visual servoing method based on model predictive control under time-varying visualization constraints as described in the first aspect.

[0026] According to a third aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the UAV visual servoing method based on model predictive control under time-varying visualization constraints as described in the first aspect.

[0027] According to a fourth aspect of the present invention, an electronic device is provided, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to implement the UAV visual servoing method based on model predictive control under time-varying visualization constraints as described in the first aspect by executing the executable instructions.

[0028] The UAV visual servoing method based on model predictive control and visual constraint modeling provided in the embodiments of the present invention first proposes a visual constraint modeling method related to UAV attitude in order to accurately construct the correspondence between the UAV flight attitude and its visual range. Then, to prevent the quadcopter UAV from losing track of the target during flight, a robust model predictive control algorithm is designed to solve the optimization control problem with time-varying constraints and external disturbances. This improves the maneuverability and flexibility of the UAV while ensuring that the tracked target remains within the field of view of the onboard camera.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0031] Figure 1 This is a flowchart of an embodiment of the present invention; Figure 2 This is a schematic diagram of the visual servoing process for a drone. Figure 3 This is a schematic diagram of the target tracking process of a UAV based on visual servoing. Figure 4 This is a schematic diagram of the virtual camera coordinate system; Figure 5 This is a schematic diagram of time-varying visual constraints; Figure 6 A schematic diagram of time-varying visualization constraints for a distributed drone swarm. Detailed Implementation

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0033] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0034] To address the shortcomings and deficiencies of existing technologies, this example implementation provides a UAV visual servoing method based on model predictive control under time-varying visualization constraints, which is used to solve the visual servoing problem of UAVs under external interference and visualization constraints. (Reference) Figure 1 As shown, the specific steps may include: Step 1: Establish the virtual camera coordinate system for the drone: Step 2: Extract pixel feature points of the target being tracked using image processing, and then calculate the image moments of the target being tracked in the virtual camera coordinate system; Step 3: Define the image moments of the tracked target as the UAV tracking error, and construct the tracking error update equation based on the UAV's dynamic model; Step 4: Map the visualization range of the airborne camera onto the virtual camera coordinate system, and design visualization constraints that change with the attitude of the UAV; Step 5: Design a model predictive control algorithm to solve the visual servoing problem with time-varying visualization constraints, ensuring that the target being tracked by the UAV swarm is always within its field of view during the tracking process.

[0035] Step 6: Design a dual-loop control framework for the UAV. The outer loop controller plans the acceleration and yaw rate of the UAV, while the inner loop controller controls the lift of the quadcopter UAV to make it move according to the output of the outer loop controller.

[0036] The steps in this exemplary embodiment will now be described in more detail with reference to the accompanying drawings and embodiments.

[0037] Example 1: The control objective of this example is to control a swarm of drones to track a dynamic target and maintain the desired altitude.

[0038] Step 1: Establish the virtual camera coordinate system for the drone like Figure 2 As shown, the world coordinate system is defined as The coordinate system of the airborne camera is Drones in the world coordinate system The attitude (including coordinates and attitude angles), linear velocity, and angular velocity are respectively... , and . For roll angle, For pitch angle and This is the yaw angle. For simplicity, it is defined as follows: and Therefore, the transformation matrix from the camera coordinate system to the world coordinate system is:

[0039] The imaging plane is defined as the plane perpendicular to the z-axis of the camera coordinate system. In the image plane, the tracked target is composed of its contour pixels. For a point in the camera coordinate system... Its projection in the real image plane can be based on Obtain, among which λ Let be the camera focal length. To facilitate target feature extraction and UAV control, a virtual camera coordinate system is designed. The virtual camera coordinate system With camera coordinate system They share the same origin, and their z-axis is parallel to the world coordinate system. The drone is in the virtual camera coordinate system. The position and linear velocity in the middle are respectively and Subsequently, a virtual image plane was constructed in the virtual camera coordinate system, parallel to the plane formed by the x and y axes of the world coordinate system. To facilitate drone control, the coordinates of the target being tracked were transformed from the image plane to the virtual image plane.

[0040]

[0041] Step 2: Extract pixel feature points of the target being tracked using image processing, thereby obtaining the image moments of the target being tracked in the camera coordinate system; The image moments for tracking the target are shown below:

[0042] in, and It is the pixel center of the feature pattern in the virtual camera plane, and its calculation formula is: and . and It is to track the target The position of the τ-th point among the feature points. , When the drone reaches the desired attitude, the characteristic parameters at this time are recorded. .

[0043] Step 3: Construct the tracking error update equation based on the UAV's dynamic model; like Figure 3 As shown, the control objective of this embodiment is to control the drone swarm to track a target and maintain a desired altitude. A dynamic target is set to move along the following trajectory. Among them, heading angle It is calculated based on the arctangent. The linear velocity and angular velocity of the dynamic target are respectively... and Quadrone drone The image moment for tracking the target is defined as Image moment error is defined as ,in It is the desired image moment. Let... For drones Velocity in the virtual camera coordinate system. Quadrotor drone. Image moment error for

[0044] Its update equation is as follows:

[0045] in, It is the yaw angle difference between the drone and the target being tracked. It is the yaw angle of the drone. and It is the relative image moment distance between the drone and the target being tracked. It is a feature in the target image moments that is highly correlated with the desired outcome. It tracks the height of the target in the camera coordinate system. and It tracks the target's velocity in the virtual camera coordinate system. (UAV velocity error) for

[0046] The corresponding update equation is defined as

[0047]

[0048]

[0049] Control input is . It is a drone , , Acceleration on the axis. The visual servo error setting for the UAV is... Therefore, the discrete tracking error update formula is: , To control the interval, It is an upper bound as External and systematic errors, .

[0050] Step 4: Map the visualization range of the camera on the drone onto the virtual camera coordinate system, and design visualization constraints that change with the drone's attitude. For a point in the camera coordinate system Its projection in the real image plane can be based on The calculation shows that, among which That is the camera's focal length. Let... This represents the actual visual constraint region, which is distorted as the drone's attitude changes. Image moments within this region indicate that the tracked target is within the drone's field of view. Subsequently, through... The boundary points of the real-world visualized area are transformed to a virtual image plane, where , .also, and These represent the upper bound of the visible region in the real image plane, and they are related to camera parameters. For example... Figure 4 As shown, and Let this be the upper bound of the drone's visualization area, assuming the drone's attitude angle is... After calculating the mapping points of the four boundary points of the visualization area onto the virtual image plane, the center point of the actual visualization area related to the UAV pose can be obtained by averaging. When both roll and pitch angles reach their maximum or minimum, the intersection of the actual visual constraint regions is calculated as the terminal visual constraint region. .

[0051] The tracked target within the terminal's visual constraint region is visible regardless of the UAV's attitude. However, the actual visual constraint region changes with the UAV's attitude, increasing the computational complexity of the optimization problem. Therefore, to simplify the calculation of time-varying constraints, the visual constraint region is designed to have a fixed radius. and the center point of time variation A circle. Like... Figure 5 As shown, , , and The upper bound of the drone's visualization area, with a fixed radius. The region is defined by the inscribed circle of the actual visual constraint area when the drone is hovering. As the drone's attitude changes, the simplified time-varying visualization area is as follows: Figure 6 As shown. The midpoint of this region coincides with the midpoint of the actual visualized constraint. The simplified time-varying visualization constraint is given by the following formula:

[0052]

[0053]

[0054] in, The time-varying visual constraints are related to the upper bound of the UAV's error. , To adjust the parameters, To control the interval. To ensure that the drone can still keep the target within its field of vision even when it is subject to external interference.

[0055] Step 5: Design a robust model predictive control algorithm to solve the visual servoing problem with time-varying visualization constraints, ensuring that the target being tracked by the UAV is always within its field of vision during the tracking process; To ensure that the tracked target remains within the field of view of the aerial camera, a robust model predictive control algorithm is designed to solve the following optimization problem with time-varying visualization constraints:

[0056] in, It is a collaborative weight matrix. It is a drone Image moment error, It is based on the control input Predicted tracking error. It is a drone Received drone The prediction tracking error is reduced. The optimal solution calculated by the robust model predictive control is used as the input to the inner-loop controller, thereby enabling the UAV swarm to complete the target tracking task in a set formation while ensuring that the tracked object is always within the UAV's field of vision.

[0057] Step 6: Design a dual-loop control framework for the UAV. The outer loop controller plans the acceleration and yaw rate of the UAV, while the inner loop controller controls the lift of the quadcopter UAV to make it move according to the output of the outer loop controller.

[0058] This invention does not directly generate actuator-level instructions, but instead employs a dual-loop control structure. The outer-loop vision controller calculates the required UAV acceleration and yaw rate for the inner-loop controller based on the image features of the target being tracked. Then, the inner-loop tracking controller drives the quadcopter UAV to the desired attitude calculated by the following formula.

[0059]

[0060]

[0061]

[0062]

[0063] in, and These represent the desired velocity and attitude, respectively. After receiving instructions from the outer loop controller, the inner loop controller uses currently mature flight control algorithms to solve the corresponding attitude tracking problem.

[0064] Example 2: The control objective of this example is to control a single quadcopter drone to track a static target.

[0065] Image processing is used to extract pixel feature points of static targets, thereby obtaining the image moments of the tracked target in the camera coordinate system. The image moment error is defined as... ,in It is the desired image moment. Let... Let be the speed of the drone in the virtual camera coordinate system. Next, the image-based visual servoing error of the quadcopter drone is set to . The dynamic derivation is as follows:

[0066] Among them, the control input is . It is a drone , , Acceleration on the axis. It tracks the target's altitude in the camera coordinate system. Then, dual-loop control is used to bring the UAV to the desired attitude.

[0067] Subsequently, based on steps 4 and 5, visual constraints are designed, and the following distributed optimization problem with time-varying constraints is solved to control the UAV to track a static target and ensure that the static target is always within the UAV's field of vision.

[0068]

[0069] in, , and It is a positive definite matrix. These are control input constraints designed based on the maneuverability of the UAV. Furthermore, It is the image moment error, including and .in, It is based on the control input The predicted tracking error. The optimal solution calculated by the robust model predictive control is used as the input to the inner-loop controller, thereby ensuring that the UAV keeps the target within its field of view while completing the target tracking task.

[0070] Example 3: The control objective of this example is to control a single quadcopter drone to track an unmanned vehicle. Therefore, we define an autonomous vehicle moving along the following trajectory. Among them, heading angle It is calculated based on the arctangent. The linear velocity and angular velocity of the autonomous vehicle are respectively... and The image-based visual servoing error setting for the quadcopter drone is... The dynamic derivation is as follows:

[0071] Among them, the control input is . It is a drone , , Acceleration on the axis. It tracks the height of the target in the camera coordinate system. and This is the speed of the unmanned vehicle in the virtual camera coordinate system. The drone's speed error is... The corresponding update equation is defined as

[0072]

[0073] Subsequently, based on steps 4 and 5, visual constraints are designed, and a rolling optimization problem with time-varying constraints is solved to control the drone to track the moving unmanned vehicle and ensure that the unmanned vehicle is always within the drone's field of vision.

[0074] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments.

[0075] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0076] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0077] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0078] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is defined only by the appended claims.

Claims

1. A model predictive control based visual servoing method for unmanned aerial vehicles under time-varying visual constraints, characterized in that, The method comprises: Step 1: establishing a virtual camera coordinate system of the unmanned aerial vehicle; Step 2: extracting pixel feature points of the tracking target by image processing to calculate the image moment of the tracking target in the virtual camera coordinate system; Step 3: defining the image moment of the tracking target as the tracking error of the unmanned aerial vehicle, and constructing a tracking error update equation according to the dynamic model of the unmanned aerial vehicle; Step 4: mapping the visual range of the onboard camera to the virtual camera coordinate system, and designing a visual constraint that changes with the attitude of the unmanned aerial vehicle; Step 5: designing a model predictive control algorithm to solve the visual servo problem with time-varying visual constraints, to ensure that the tracking target is always within the visual range of the unmanned aerial vehicle cluster during tracking; Step 6: designing a double-loop control framework for the unmanned aerial vehicle, wherein the outer visual controller calculates the required acceleration and yaw rate of the unmanned aerial vehicle based on the image features of the tracking target, and the inner tracking controller drives the quadrotor unmanned aerial vehicle to reach the desired attitude, so that the unmanned aerial vehicle cluster completes the target tracking task while ensuring that the tracking object is always within the visual range of the unmanned aerial vehicle.

2. The method of claim 1, wherein, The virtual camera coordinate system of the unmanned aerial vehicle is specifically: The world coordinate system is defined as , the onboard camera coordinate system is , the attitude, linear velocity and angular velocity of the UAV in the world coordinate system are , and respectively, wherein is the roll angle, is the pitch angle and is the yaw angle; The transformation matrix from the camera coordinate system to the world coordinate system is: wherein , ; The imaging plane is defined as the plane perpendicular to the z-axis of the camera coordinate system, and in the image plane, the tracking target is composed of its contour pixel points; For a point in the camera coordinate system its projection in the real image plane can be obtained according to where λ is the focal length of the camera; in order to facilitate the tracking of target feature extraction and UAV control, a virtual camera coordinate system is designed, where the virtual camera coordinate system has the same origin as the camera coordinate system and its z-axis is parallel to the world coordinate system; UAV in virtual camera coordinate system The position and linear velocity in the middle are respectively and Subsequently, a virtual image plane was constructed in the virtual camera coordinate system, parallel to the plane formed by the x and y axes of the world coordinate system. To facilitate drone control, the coordinates of the tracked target were transformed from the image plane to the virtual image plane. 。 3. The method of claim 2, wherein, The image moment of the tracking target in the camera coordinate system is specifically: wherein, and are the pixel centers of the feature pattern in the virtual camera plane, which are calculated as and ; and are the positions of the th point of the τ th feature point of the tracking target , ; when the UAV reaches the desired pose, the feature parameters at this time are recorded as .

4. The method of claim 3, wherein, The tracking error update equation is specifically: wherein, is the image moment error, is the yaw angle difference between the UAV and the tracked target, is the yaw angle of the UAV, and is the relative image moment distance between the UAV and the tracked target, is the feature in the target image moment related to the expected height, is the height of the tracked target in the camera coordinate system, and is the velocity of the tracked target in the virtual camera coordinate system, and is the velocity of the UAV in the virtual camera coordinate system.

5. The method of claim 4, wherein, The visual constraint that changes with the attitude of the unmanned aerial vehicle is specifically: wherein, represents a time-varying visualization constraint related to an error upper bound of the UAV , is a tuning parameter, is a control interval.

6. The method of claim 5, wherein, The design of the model predictive control algorithm to solve the visual servo problem with time-varying visual constraints is specifically: wherein, is a cooperative weight matrix, is a UAV image error, including and , is a predicted tracking error from control input , is a UAV received by the UAV predicted tracking error.

7. The method of claim 6, wherein, The calculation formula of the desired attitude is specifically: wherein, and are the desired velocity and attitude, respectively.

8. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the model predictive control based unmanned aerial vehicle visual servo method under time-varying visual constraints as claimed in any one of claims 1 to 7.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the model predictive control based unmanned aerial vehicle visual servo method under time-varying visual constraints as claimed in any one of claims 1 to 7.

10. An electronic device, comprising: Comprise: A processor; And A memory for storing executable instructions of the processor; Wherein the processor is configured to execute the executable instructions to perform the model predictive control based unmanned aerial vehicle visual servo method under time-varying visual constraints as claimed in any one of claims 1 to 7.

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