CN112462797A - Visual servo control method and system using grey prediction model - Google Patents
Visual servo control method and system using grey prediction model Download PDFInfo
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Abstract
The invention provides a visual servo control method, a system, a device and a storage medium by utilizing a gray prediction model, which comprises the steps of obtaining a target position acquired by an airborne image acquisition device of an unmanned aerial vehicle and an expected position corresponding to the target position; performing visual servo control according to the output values of the target position and the expected position; obtaining a predicted value of the next state of the unmanned aerial vehicle through a grey prediction module; and comparing the output value with the predicted value to obtain a prediction error, and continuously adjusting the operation of the unmanned aerial vehicle so as to enable the unmanned aerial vehicle to achieve early flight control. And the running control error of the next state of the flying robot is obtained through the grey prediction model prediction, and compared with the given error, the switching of different PID control parameters is completed, so that the motion process of the flying robot is controlled in advance, the position and attitude information is continuously corrected in the flying process, and more accurate flying control is realized.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a visual servo control method and a visual servo control system by using a gray prediction model.
Background
In actual industrial control, with the continuous development of industrial technology, and the complexity, time-varying property and uncertainty of industrial processes, the traditional industrial control method is difficult to meet the requirements of modern industry, so people gradually research into intelligent control theory. Fuzzy control, predictive control, and probabilistic statistical methods are common control methods for uncertain systems. However, due to the complexity and time-varying nature of the system, it is difficult to build an effective mathematical model, so that the predictive control based on the accurate model of the system cannot adapt to the control requirements. The novel system or the complex system has no excessive experience and large amount of historical data, so that the fuzzy control based on expert experience, the probability algorithm depending on large amount of historical data and the neural network control can not achieve good control effect. The grey dynamic model is characterized by less modeling data, simple calculation and the like, so the grey prediction control is one of the methods for effectively controlling the intrinsic grey system based on the grey dynamic model. However, as the grey system theory is a new theoretical system, there are many imperfect places, and thus the grey prediction control is required to be further expanded and improved.
Autonomous flight technology of Unmanned Aerial Vehicles (UAVs) has been an important content of aviation research, automatic control research, and robot research. Related art there is also a great need in various applications. The specific application scenarios include: the method comprises the following steps of personal aerial photography, movie and television production, reconnaissance and search and rescue, atmospheric data collection, exploration, agricultural pest control, video monitoring and the like. If the unmanned aerial vehicle is adopted to realize the tasks listed before, on one hand, the cost can be reduced, the efficiency of the tasks can be improved, and on the other hand, the safety guarantee of personnel and even the tasks which cannot be completed by the personnel can be improved.
Four rotor unmanned aerial vehicle do with other traditional unmanned vehicles and compare, the former mechanical structure is simpler. The four rotors have symmetrical bodies. The movement of the four rotors can be controlled by changing the rotation angular speeds of the rotors. The four rotors are more flexible in narrow space, and the flight performance is stronger. On the other hand, quad-rotor unmanned aerial vehicles have higher control performance. Can take off and land vertically in narrow space. The aircraft can hover at a fixed point, fly at low altitude near the ground, and fly at slow cruise. Unlike conventional helicopters that cannot lean close to a target due to their large rotors, quad-rotor drones hover at a position closer to the target. Therefore, the research on the four rotors is also receiving attention from more and more researchers.
At present, an outdoor autonomous aircraft generally adopts a GPS system to realize positioning, however, the positioning precision is greatly influenced by the problem of strong and weak GPS signals, and particularly in environments with weak or even invalid GPS such as indoor environments, forests, caves and cities with various buildings, a reliable method is needed to control autonomous flight of the aircraft, so that visual servo is favored by researchers. Under the condition of onboard information processing, the visual servo can be applied to an independent and GPS failure environment, the visual servo acquires visual information through the camera to control the movement of the aircraft, and the camera not only has the characteristics of portability, low loss and the like, but also can provide high-resolution data of position and speed information; in addition, GPS's accuracy can only reach the meter level, and visual algorithm can rise the target location to centimetre level precision under indoor microenvironment, and this also makes rotor type unmanned aerial vehicle can carry out more accurate task. Visual servoing has been widely used in tasks such as obstacle avoidance, ranging, hovering, and simultaneous localization and mapping (SLAM).
Visual servo obtains extensive research in the aspect of small-size four rotor unmanned aerial vehicle autonomous flight control, and the main visual servo based on the position and the visual servo based on the image have obtained certain achievement, but two kinds of visual servo schemes all have respective shortcoming, and rotor flying robot's control method has to a great extent not enough and huge development space. The method can be embodied in the following aspects:
(1) the position-based visual servo control method can intuitively define the motion of a target in a rectangular coordinate space, accords with the working mode of the existing robot, and has the advantages that the control precision depends on the pose estimation precision to a great extent, and the pose estimation precision depends on the calibration precision of a camera and the robot, and the like; in addition, the calculation amount is large
(2) The vision servo control method based on the image has the advantages that three-dimensional space positioning is not needed, and the method is insensitive to calibration of a camera and a robot; the calculation amount is small, and the defects are that a servo controller is complex and lacks adaptability; additional sensors are required to acquire depth information; too much displacement can result in unpredictable camera motion.
(3) The general control method of the flying robot is to control the flying robot by judging whether the system behavior meets the predetermined requirement, which is generally called as 'after-the-fact control', and the control method has many defects: the method can not prevent the diseases in the bud, can not realize the instant control, and has poor adaptability and the like.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method and the system for controlling the visual servo by using the gray prediction model solve the problem that the existing control method of the flying robot cannot control timely by judging whether the system behavior meets the preset requirement or not.
In order to solve the technical problems, the invention adopts the technical scheme that:
the first aspect of the embodiments of the present invention provides a visual servo control method using a gray prediction model, including:
acquiring a target actual position acquired by an airborne image acquisition device of an unmanned aerial vehicle and an expected position corresponding to the target actual position;
calculating an output deviation value according to the target actual position and the expected position;
obtaining a predicted value of the next state of the unmanned aerial vehicle through a grey prediction module;
and comparing the output deviation value with the predicted value to obtain a predicted error, and controlling the unmanned aerial vehicle according to the predicted error.
In some embodiments, control parameters that control the attitude, angle, and speed of the drone flight are also included.
In some embodiments, calculating an output offset value from the target actual position and the desired position further comprises visual servoing, the visual servoing comprising:
acquiring an image characteristic error of the actual target position and the expected target position of the unmanned aerial vehicle, and establishing a servo relation between the image characteristic error and the speed;
calculating a speed control law by adopting a position controller according to the servo relation, and obtaining an expected attitude through the speed control law;
and carrying out attitude tracking control on the unmanned aerial vehicle through sliding mode control.
In some embodiments, the gray prediction module comprises a GM (1,1) model, the GM (1,1) model making location accuracy predictions by a regulatory factor.
A second aspect of an embodiment of the present invention provides a visual servo control apparatus using a gray prediction model, including:
the acquisition module is used for acquiring the actual position of a target acquired by an airborne image acquisition device of the unmanned aerial vehicle and an expected position corresponding to the actual position of the target;
the calculation module is used for calculating an output deviation value according to the target actual position and the expected position;
the prediction module is used for obtaining a predicted value of the next state of the unmanned aerial vehicle through the grey prediction module;
and the comparison module compares the output deviation value with the predicted value to obtain a predicted error, and controls the unmanned aerial vehicle according to the predicted error.
A third aspect of an embodiment of the present invention provides a visual servo control system, including:
the unmanned aerial vehicle system is used for acquiring images, preprocessing the acquired images, returning acquired image signals and controlling the transmission of signals;
the ground station system is used for processing and displaying the returned collected image, controlling the unmanned aerial vehicle to reach a target position, carrying out visual servo control on the error between the image of the target actual position and the image of the expected position, and controlling the error in advance through a gray prediction module;
and the terminal system is used for controlling and managing the control instruction of the unmanned aerial vehicle.
In some embodiments, the ground station system comprises:
the grey prediction module is used for obtaining a predicted value of the next state of the unmanned aerial vehicle;
the visual servo module is used for establishing a relation between an image and a tracking expected position and directly controlling the unmanned aerial vehicle by using an imaging measurement method;
the image processing unit is used for carrying out definition configuration on the image and can utilize the visual processor to carry out pretreatment on the image;
the data storage management unit is used for displaying and storing the returned video image in the ground station;
and the target position tracking and controlling unit is used for issuing an instruction to the unmanned aerial vehicle system and controlling the unmanned aerial vehicle to reach the target position.
In some embodiments, the drone system includes:
the image acquisition processing module acquires image information through a mobile machine vision system and transmits the image information back to the ground station;
the wireless communication transmission module is used for communication between the ground station and the unmanned aerial vehicle, collecting return of image signals and controlling real-time transmission of signals, so that the ground station can know the condition of an operation site in real time, and the unmanned aerial vehicle can be effectively controlled.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium having stored thereon executable instructions that, when executed, perform a method according to the first aspect of embodiments of the present invention.
The invention has the beneficial effects that: by means of the image-based visual servo control system, a relationship between the image-based and the tracking desired position is established, and then the drone is controlled. The position information of the flying robot which causes image formation can be deduced by utilizing a certain image target on the ground, and the motion direction, the speed and the like of the unmanned aerial vehicle can be controlled according to the difference value of the expected image position information and the actual position information. The advanced control of a visual servo system of the flying robot is realized, the operation control error of the next state of the flying robot is obtained through the grey prediction model prediction, the operation control error is compared with the given error, and the switching of different PID control parameters is completed, so that the motion process of the flying robot is controlled in advance, the position and attitude information is continuously corrected in the flying process, and the more accurate flying control is realized.
Drawings
The detailed structure of the invention is described in detail below with reference to the accompanying drawings
Fig. 1 is a flowchart of a visual servo control method using a gray prediction model according to an embodiment of the present invention.
Fig. 2 is a flowchart of image-based visual servoing control according to an embodiment of the present invention.
Fig. 3 is a diagram of a visual servo process of a four-rotor flying robot according to an embodiment of the present invention.
Fig. 4 is a basic framework diagram of a gray prediction module according to an embodiment of the present invention.
Fig. 5 is a flowchart of a visual servo control apparatus using a gray prediction model according to an embodiment of the present invention.
Fig. 6 is a block diagram of a visual servo control system according to an embodiment of the present invention.
Fig. 7 is a block diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
For purposes of promoting a clear understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present embodiments of the invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements throughout. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
Referring to fig. 1, fig. 1 is a flowchart illustrating a visual servo control method using a gray prediction model according to an embodiment of the present invention.
As shown in fig. 1, a first embodiment of the present invention provides a visual servo control method using a gray prediction model, which includes the following steps S1 to S4.
S1, acquiring a target actual position acquired by an airborne image acquisition device of the unmanned aerial vehicle and an expected position corresponding to the target actual position;
s2, calculating an output deviation value according to the target actual position and the expected position;
s3, obtaining a predicted value of the next state of the unmanned aerial vehicle through a grey prediction module;
and S4, comparing the output deviation value with the predicted value to obtain a prediction error, and controlling the unmanned aerial vehicle according to the prediction error.
Here, unmanned aerial vehicle's airborne image acquisition device is the camera. The advanced control of a visual servo system of the flying robot is realized, the operation control error of the next state of the flying robot is obtained through the grey prediction model prediction, the operation control error is compared with the given error, and the switching of different PID control parameters is completed, so that the motion process of the flying robot is controlled in advance, the position and attitude information is continuously corrected in the flying process, and the more accurate flying control is realized.
Example 2
The unmanned aerial vehicle further comprises control parameters for controlling the flying attitude, angle and speed of the unmanned aerial vehicle. Calculating an output deviation value according to the target actual position and the expected position, and then performing visual servo control, wherein the visual servo control comprises the following steps:
acquiring an image characteristic error of the actual target position and the expected target position of the unmanned aerial vehicle, and establishing a servo relation between the image characteristic error and the speed;
calculating a speed control law by adopting a position controller according to the servo relation, and obtaining an expected attitude through the speed control law;
and carrying out attitude tracking control on the unmanned aerial vehicle through sliding mode control.
Specifically, referring to fig. 2 and 3, fig. 2 is a flowchart illustrating a visual servo control based on an image according to a second embodiment of the present invention. Fig. 3 is a view of a visual servo process of the four-rotor flying robot of the embodiment. A double-ring cascade control structure of a position controller and an attitude controller is adopted. The dynamics of the quad-rotor unmanned aerial vehicle and the image dynamics are fused, a position controller is built, and a speed control law is obtained. The nonlinear and underactuated dynamic characteristics of the quad-rotor unmanned aerial vehicle enable the quad-rotor unmanned aerial vehicle to move at a linear velocity in space by adjusting the attitude of the quad-rotor unmanned aerial vehicle. The desired attitude and thrust are extended directly by the velocity control law, rather than being solved for by servo calculations. And (3) designing sliding mode control to carry out attitude tracking control, solving all virtual control quantities of the quad-rotor unmanned aerial vehicle, and realizing control on the quad-rotor unmanned aerial vehicle. By means of the image-based visual servo control system, a relationship between the image-based and the tracking desired position is established, and then the drone is controlled. The position information of the flying robot which causes image formation can be deduced by utilizing a certain image target on the ground, and the motion direction, the speed and the like of the unmanned aerial vehicle can be controlled according to the difference value of the expected image position information and the actual position information.
Example 3
The grey prediction module comprises a GM (1,1) model, and the GM (1,1) model carries out position precision prediction through a regulating factor.
Specifically, please refer to fig. 4, fig. 4 is a basic frame diagram of a gray prediction module according to a third embodiment of the present invention. The invention provides a gray prediction model improved based on a background value of a regulating factor, which is mainly applied to a traditional GM (1,1) model in a gray prediction theory, provides a new GM (1,1) model combining a new background value with the regulating factor and a changed initial condition, provides an optimization method of the regulating factor, and is applied to modeling of a visual servo control system of a flying robot. The new model is subjected to simulation and prediction comparison on a pure exponential sequence, so that the GM (1,1) new model optimized by the regulatory factor has higher precision and application range than other improved models, is hardly limited by the size of development parameters, is still higher in precision even if the development coefficient is higher and the accuracy is still higher when the model is used for multi-step prediction, and is greatly improved in model precision compared with the traditional GM (1,1) model.
Example 4
Referring to fig. 5, fig. 5 shows a fourth embodiment of the present invention. The present embodiment provides a visual servoing control apparatus 100 using a gray prediction model, including:
the acquisition module 101 is used for acquiring a target actual position acquired by an airborne image acquisition device of the unmanned aerial vehicle and an expected position corresponding to the target actual position;
a calculating module 102, configured to calculate an output deviation value according to the actual target position and the expected position;
the prediction module 103 is used for obtaining a predicted value of the next state of the unmanned aerial vehicle through the grey prediction module;
and the comparison module 104 is used for comparing the output deviation value with the predicted value to obtain a predicted error, and controlling the unmanned aerial vehicle according to the predicted error.
Specifically, firstly, a virtual image plane is constructed, an image moment is selected as a characteristic, image dynamics decoupling is carried out, and a servo relation between a characteristic error and a speed is further established by combining a four-rotor unmanned aerial vehicle dynamics model; secondly, decoupling the system into position control and attitude control according to the dynamics decoupling relation of the quad-rotor unmanned aerial vehicle; the position controller obtains a speed control law according to the servo relation, and directly obtains an expected attitude in a dynamic expansion mode; because the nonlinear characteristic of the system causes attitude disturbance, a sliding mode controller is designed for attitude tracking control, finally, a gray prediction control module can calculate control error output values of the next steps in advance through a GM (1,1) model, a predicted value is compared with a given output value to obtain a predicted error, the system completes switching of different PID control parameters according to the magnitude of the predicted error, advanced control is realized on future behaviors of the whole control system, the real-time performance of system control is improved, and the control performance of a visual servo system is improved.
Example 5
Referring to fig. 6, fig. 6 is a block diagram of a visual servo control system module according to a fifth embodiment of the present invention. The present embodiment provides a visual servo control system 200, comprising:
the unmanned aerial vehicle system 201 is used for acquiring images, preprocessing the acquired images, returning acquired image signals and controlling the transmission of signals;
the ground station system 202 is used for processing and displaying the returned collected images, controlling the unmanned aerial vehicle to reach a target position, carrying out visual servo control on the error between the actual position image and the expected position image of the target, and controlling the error in advance through a grey prediction module;
and the terminal system 203 is used for controlling and managing the control instruction of the unmanned aerial vehicle. The terminal system comprises a Windows and Linux system.
The ground station system includes:
the grey prediction module is used for obtaining a predicted value of the next state of the unmanned aerial vehicle;
the visual servo module is used for establishing a relation between an image and a tracking expected position and directly controlling the unmanned aerial vehicle by using an imaging measurement method;
the image processing unit is used for carrying out definition configuration on the image and can utilize the visual processor to carry out pretreatment on the image;
the data storage management unit is used for displaying and storing the returned video image in the ground station;
and the target position tracking and controlling unit is used for issuing an instruction to the unmanned aerial vehicle system and controlling the unmanned aerial vehicle to reach the target position.
The unmanned aerial vehicle system includes:
the image acquisition processing module acquires image information through a mobile machine vision system and transmits the image information back to the ground station;
the wireless communication transmission module is used for communication between the ground station and the unmanned aerial vehicle, collecting return of image signals and controlling real-time transmission of signals, so that the ground station can know the condition of an operation site in real time, and the unmanned aerial vehicle can be effectively controlled.
Example 6
Referring to fig. 7, fig. 7 is a block diagram of a computer-readable storage medium according to an embodiment of the present invention.
As shown in fig. 7, a sixth embodiment of the present invention provides a computer-readable storage medium 300, where the computer-readable storage medium 300 has stored thereon executable instructions 301, and the executable instructions 301 when executed perform the method according to any one of the first to third embodiments of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital subscriber line) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk), among others.
It should be noted that, in the summary of the present invention, each embodiment is described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the method class embodiment, since it is similar to the product class embodiment, the description is simple, and the relevant points can be referred to the partial description of the product class embodiment.
It is further noted that, in the present disclosure, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined in this disclosure may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A visual servo control method using a gray prediction model, comprising:
acquiring a target actual position acquired by an airborne image acquisition device of an unmanned aerial vehicle and an expected position corresponding to the target actual position;
calculating an output deviation value according to the target actual position and the expected position;
obtaining a predicted value of the next state of the unmanned aerial vehicle through a grey prediction module;
and comparing the output deviation value with the predicted value to obtain a predicted error, and controlling the unmanned aerial vehicle according to the predicted error.
2. The visual servo control method using a gray prediction model of claim 1, further comprising controlling parameters for controlling the attitude, angle and speed of the unmanned aerial vehicle.
3. The visual servoing method using a gray predictive model of claim 2, further comprising visual servoing after calculating an output deviation value based on the target actual position and the desired position, the visual servoing comprising:
acquiring an image characteristic error of the actual target position and the expected target position of the unmanned aerial vehicle, and establishing a servo relation between the image characteristic error and the speed;
calculating a speed control law by adopting a position controller according to the servo relation, and obtaining an expected attitude through the speed control law;
and carrying out attitude tracking control on the unmanned aerial vehicle through sliding mode control.
4. The visual servo control method using a gray prediction model of claim 1, wherein the gray prediction module comprises a GM (1,1) model, and the GM (1,1) model performs position accuracy prediction by a regulatory factor.
5. A visual servoing control apparatus using a gray prediction model, comprising:
the acquisition module is used for acquiring the actual position of a target acquired by an airborne image acquisition device of the unmanned aerial vehicle and an expected position corresponding to the actual position of the target;
the calculation module is used for calculating an output deviation value according to the target actual position and the expected position;
the prediction module is used for obtaining a predicted value of the next state of the unmanned aerial vehicle through the grey prediction module;
and the comparison module compares the output deviation value with the predicted value to obtain a predicted error, and controls the unmanned aerial vehicle according to the predicted error.
6. A visual servo control system, comprising:
the unmanned aerial vehicle system is used for acquiring images, preprocessing the acquired images, returning acquired image signals and controlling the transmission of signals;
the ground station system is used for processing and displaying the returned collected image, controlling the unmanned aerial vehicle to reach a target position, carrying out visual servo control on the error between the image of the target actual position and the image of the expected position, and controlling the error in advance through a gray prediction module;
and the terminal system is used for controlling and managing the control instruction of the unmanned aerial vehicle.
7. The visual servo control system of claim 6 wherein the ground station system comprises:
the grey prediction module is used for obtaining a predicted value of the next state of the unmanned aerial vehicle;
the visual servo module is used for establishing a relation between an image and a tracking expected position and directly controlling the unmanned aerial vehicle by using an imaging measurement method;
the image processing unit is used for carrying out definition configuration on the image and can utilize the visual processor to carry out pretreatment on the image;
the data storage management unit is used for displaying and storing the returned video image in the ground station;
and the target position tracking and controlling unit is used for issuing an instruction to the unmanned aerial vehicle system and controlling the unmanned aerial vehicle to reach the target position.
8. The visual servo control system of claim 7 wherein the drone system comprises:
the image acquisition processing module acquires image information through a mobile machine vision system and transmits the image information back to the ground station;
the wireless communication transmission module is used for communication between the ground station and the unmanned aerial vehicle, collecting return of image signals and controlling real-time transmission of signals, so that the ground station can know the condition of an operation site in real time, and the unmanned aerial vehicle can be effectively controlled.
9. A computer-readable storage medium having stored thereon executable instructions that, when executed, perform the method of any one of claims 1-4.
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