Estimation method for translation external parameters of visual servo system of mobile robot

Abstract

The invention discloses a method for estimating translation external parameters of a visual servo system of a mobile robot, and relates to the technical field of computers. The method comprises the following steps of: converting translation external parameter estimation of a visual servo system of the mobile robot into a parameter estimation problem of a nonlinear system; representing the nonlinear system as a parameterized form; performing first-order low-pass filtering processing on the first signal, the second signal and the third signal to respectively obtain a first filtering signal, a second filtering signal and a third filtering signal; obtaining an auxiliary variable based on the first filtering signal, the second filtering signal and the third filtering signal; generating an auxiliary signal based on the auxiliary variable; and based on the auxiliary signal, dynamically adjusting the gain matrix through an updating rule, carrying out convergence in a fixed time to obtain an estimated value of the to-be-estimated parameter, and obtaining an estimated value of the translation external parameter according to the estimated value of the to-be-estimated parameter. The method can be used for estimating translation external parameters within fixed time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a kind of mobile robot vision servo system translation external parameter estimation method. BACKGROUND

[0002] At present, mobile robot visual servoing system (MRVSS) is a key component of autonomous mobile robot, seamlessly integrates vision sensor, image processing algorithm and control strategy, and can obtain and analyze real-time image information from the environment. MRVSS improves environmental perception, target detection and tracking, path planning and navigation. Visual servoing technology significantly improves the perception and decision-making ability of robots. MRVSS has been widely used in industrial automation, cleaning and security, medical assistance and agriculture and other fields. With the continuous progress of artificial intelligence and automation technology, MRVSS is expected to promote innovation in intelligent manufacturing, smart city, health care and precision agriculture, thereby improving the convenience of daily life.

[0003] Since the vision sensor and the mobile robot usually come from different suppliers, the installation position of the vision sensor is usually determined by the user. Mechanical constraints and difficulties in measuring the center of the optical axis make it challenging to directly obtain the external parameters of the camera.

[0004] Existing methods fail to achieve accurate estimation of external parameters. SUMMARY

[0005] Therefore, it is necessary to provide a mobile robot visual servoing system translation external parameter estimation method to solve the above technical problems. The method can accurately estimate the translation external parameters within a fixed time.

[0006] The present application adopts the following technical solutions: The present application provides a mobile robot visual servoing system translation external parameter estimation method, comprising: Converting the translation external parameter estimation of the mobile robot visual servoing system into a parameter estimation problem of a nonlinear system; Expressing the nonlinear system in a parameterized form; the parameterized form includes a first signal, a second signal, a third signal and a parameter to be estimated; First-order low-pass filtering the first signal, the second signal and the third signal to obtain a first filtered signal, a second filtered signal and a third filtered signal, respectively; Based on the first filtered signal, the second filtered signal and the third filtered signal, an auxiliary variable is obtained; Based on the auxiliary variable, an auxiliary signal is generated; Based on the auxiliary signals, the gain matrix is dynamically adjusted by an updating law, and the estimated value of the to-be-estimated parameter is converged in a fixed time, and the estimated value of the translation external parameter is obtained according to the estimated value of the to-be-estimated parameter.

[0007] Preferably, the nonlinear system is: wherein, is the angular velocity of the robot, , , D, L is the translation external parameter of the mobile robot visual servo system, is an unknown constant, is the angle between the current pose and the desired pose of the robot, , and is the relative pose deviation signal, , and are respectively , and the derivative with respect to time, is the coordinate of the i th feature point in the camera desired coordinate system.

[0008] Preferably, the parameterized form of the nonlinear system is: wherein, is the derivative with respect to time, , , , , v is the linear velocity of the robot, , , i is the i th feature point, , , , are all auxiliary signals, is the coordinate of the i th feature point in the camera coordinate system, is the coordinate of the i th feature point in the camera desired coordinate system, is the first signal, is the second signal, is the third signal, is the to-be-estimated parameter.

[0009] Preferably, the first-order low-pass filtering process is: wherein, the gain of the first-order low-pass filter, is a first filtered signal, is a second filtered signal, is a third filtered signal, , and are , and derivatives with respect to time.

[0010] Preferably, the auxiliary variable is generated by an auxiliary variable generation equation, the auxiliary variable generation equation being: wherein, is a positive real gain, P and Q is an auxiliary variable, and are P and Q derivatives with respect to time.

[0011] Preferably, the auxiliary signal is calculated by an auxiliary signal calculation equation, the auxiliary signal calculation equation being: wherein, W is an auxiliary signal, is a parameter to be estimated, is an estimate of , is an estimation error of .

[0012] Preferably, the update law is: wherein, , , and are components of the auxiliary signal , and are real constants, is a tuning gain matrix, takes or .

[0013] Preferably, the translation of the estimated value of the external parameter is: wherein, , and are elements of the set , 、 are respectively estimated values of the translational extrinsic parameters.

[0014] The application provides an estimation device for translational extrinsic parameters of a mobile robot visual servo system, comprising: a conversion module configured to convert the estimation of the translational extrinsic parameters of the mobile robot visual servo system into a parameter estimation problem of a nonlinear system; a parameterization module configured to represent the nonlinear system in a parameterized form, wherein the parameterized form comprises a first signal, a second signal, a third signal and a parameter to be estimated; a filtering module configured to perform first-order low-pass filtering on the first signal, the second signal and the third signal to obtain a first filtered signal, a second filtered signal and a third filtered signal respectively; a first determination module configured to obtain an auxiliary variable based on the first filtered signal, the second filtered signal and the third filtered signal; a generation module configured to generate an auxiliary signal based on the auxiliary variable; a second determination module configured to obtain an estimated value of the parameter to be estimated by dynamically adjusting a gain matrix based on the auxiliary signal through an update rule and converging within a fixed time, and obtain an estimated value of the translational extrinsic parameters according to the estimated value of the parameter to be estimated.

[0015] The application provides a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned estimation method for translational extrinsic parameters of a mobile robot visual servo system.

[0016] The application provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned estimation method for translational extrinsic parameters of a mobile robot visual servo system when executing the program.

[0017] The above-mentioned at least one technical solution adopted by the application can achieve the following beneficial effects: The translational external parameter estimation of the mobile robot visual servo system is converted into a parameter estimation problem of a nonlinear system; the nonlinear system is expressed in a parameterized form, the nonlinear system is designed to ensure the stability and reliability of the mobile robot visual servo system; the parameterized form includes a first signal, a second signal, a third signal and to-be-estimated parameters; the to-be-estimated parameters include the translational external parameter; the first signal, the second signal and the third signal are subjected to first-order low-pass filtering processing to obtain a first filtered signal, a second filtered signal and a third filtered signal respectively, the first-order low-pass filtering effectively suppresses high-frequency noise, improves the signal-to-noise ratio of the signal and enhances the robustness of parameter estimation; based on the first filtered signal, the second filtered signal and the third filtered signal, an auxiliary variable is obtained, and an auxiliary signal is defined according to the auxiliary variable and an estimated value of the to-be-estimated parameters, the introduction of the auxiliary signal irrelevant to noise can eliminate the parameter estimation deviation in the traditional least square method; an update rule is designed based on the auxiliary signal and the estimated value of the to-be-estimated parameters; a gain matrix is dynamically adjusted based on the auxiliary signal and through the update rule, so that the estimated value of the to-be-estimated parameters converges to the to-be-estimated parameters within a fixed time; and an estimated value of the translational external parameter is derived based on the estimated value of the to-be-estimated parameters. The method can accurately estimate the translational external parameter of the mobile robot visual servo system. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and serve to explain the principles of the application, and do not limit the application in any way. In the drawings:

[0019] Figure 1 A flowchart of a mobile robot visual servo system translational external parameter estimation method provided by the application; Figure 2 A robot kinematic model schematic diagram provided by the application; Figure 3 A system block diagram of a mobile robot visual servo system translational external parameter estimation method provided by the application; Figure 4 A trajectory schematic diagram of an adjustment error provided by the application; Figure 5 A feature point image trajectory schematic diagram in a camera coordinate system provided by the application; Figure 6 A trajectory schematic diagram of an angular velocity and a linear velocity of a mobile robot provided by the application; Figure 7 A trajectory schematic diagram of an adaptive controller parameter provided by the application; Figure 8 A trajectory schematic diagram of an improved parameter estimation method provided by the application ; and Figure 9 A mobile robot visual servoing system translation external parameter estimation device provided by the present application schematic diagram; Figure 10 A mobile robot visual servoing system translation external parameter estimation device provided by the present application schematic diagram; DETAILED DESCRIPTION

[0020] To make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described below in connection with specific embodiments and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0021] Devices such as desktop computers, servers, notebook computers, etc. that can execute the solutions of the present application. For the convenience of description, only servers are taken as the execution subjects for description below.

[0022] In the past decade, researchers have proposed many methods to solve the problem of unknown translation external parameters. Researchers have proposed a two-stage controller for stabilizing MRVSS using adaptive control and backstepping techniques. The first stage implements an adaptive velocity controller to minimize angular and lateral errors to near zero; the second stage uses a proportional controller to adjust the longitudinal error. Similarly, researchers have also proposed a two-stage controller, but in the first stage, an adaptive invariant manifold control method is used to achieve exponential convergence of angular error and quasi-exponential convergence of lateral error. Based on the above technical accumulation, researchers have designed a non-adaptive three-stage controller, which simplifies the control law and effectively minimizes system overshoot. Researchers have developed adaptive or observer-based control laws to achieve trajectory tracking control of MRVSS under unknown external parameters.

[0023] Although MRVSS combines the flexibility of mobile robots and the advanced perception capabilities of visual sensors, the design of its controller still faces significant challenges. Mobile robots usually have unknown dynamic parameters and disturbances (including slipping and skidding), making the control problem of this nonholonomic system a focus of continuous research in the control field. In addition, the integration of visual sensors also introduces additional complexity, such as image noise and distortion, uncalibrated intrinsic and extrinsic parameters, unknown depth information, loss of feature information, and difficulty in ensuring real-time performance.

[0024] Based on the installation location of the vision sensor, research on MRVSS falls into two main structural types: eye-to-hand and hand-to-eye. In the eye-to-hand structure, the vision sensor is fixed within the workspace to acquire environmental and global robot information. In this configuration, the servo system model is very similar to that of a non-visual feedback mobile robot, allowing for the reference and application of many control methods from non-visual feedback systems. However, using a single vision sensor in this setup limits the field of view, while using multiple sensors, although expanding the field of view, increases complexity and cost. In contrast, the hand-to-eye structure mounts the vision sensor directly on the mobile robot, allowing it to move with the robot. This significantly enhances the robot's ability to perceive its environment. However, in this structure, the design of control strategies becomes more complex.

[0025] In MRVSS, accurately identifying the camera's intrinsic parameters is crucial for correcting image distortion and improving feature matching accuracy. This optimizes vision algorithms and enhances the accuracy and robustness of mobile robot navigation and control. Currently, convenient and practical general methods exist for calibrating camera intrinsic parameters. Since vision sensors and mobile robots often come from different vendors, the mounting location of the vision sensor is usually determined by the user. Mechanical constraints and the difficulty of measuring the optical axis center make directly obtaining the camera's extrinsic parameters challenging. Furthermore, wind, vibration, or other factors can alter the camera's mounting location, rendering the calibrated extrinsic parameters invalid. However, when extrinsic parameters exist, they will be incorporated into the MRVSS model, thus requiring visual servoing strategies to account for their effects.

[0026] Existing technologies have failed to achieve accurate estimation of external parameters and have a slow error convergence rate.

[0027] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0028] Figure 1 This is a flowchart illustrating a method for estimating translational external parameters of a mobile robot visual servoing system according to the present invention, specifically including the following steps: S101: Transform the estimation of translational extrinsic parameters of a mobile robot's visual servoing system into a parameter estimation problem for a nonlinear system.

[0029] like Figure 2 The figure shown is the robot kinematics model provided by the present invention. The present invention assumes that the MRVSS is in the world coordinate system. horizontal plane For continuous motion, the robot's kinematic model is shown in equation (1): (1) in, It's a robot. The position in the middle, It's a robot. In the direction of, It is the robot's linear velocity. It is the robot's angular velocity.

[0030] In this invention, a static point is defined. In robot coordinate system and world coordinate system The coordinates in are respectively and static point P The relationship between the robot coordinate system and the world coordinate system is shown in formula (2): (2) in, static point P Coordinates in the world coordinate system static point P Coordinates in the robot coordinate system static point P Direction in the world coordinate system yes and Between along z The components of the axis.

[0031] In this invention, a camera coordinate system is also defined. Robot coordinate system and camera coordinate system There are translational extrinsic parameters. static point P In the camera coordinate system and in robot coordinate system The relationship between them is shown in formula (3): (3) in, It is the translation extrinsic parameter, and also and The components along the three axes.

[0032] Based on formulas (1) and (3), the relationship shown in formula (4) is obtained: (4) in, for The derivative with respect to time, for The derivative with respect to time.

[0033] In this invention, it is assumed that the robot's desired coordinate system is... The desired coordinate system for the camera is Suppose there is N One feature point, N The coordinates of the feature points in the camera coordinate system are: , N The coordinates of the feature points in the camera's desired coordinate system are: ,in , i For the first i 1 feature point. N The homogeneous pixel coordinates of each feature point in the camera coordinate system and homogeneous pixel coordinates in the camera's desired coordinate system As shown in formula (5):

[0034] (5) in, For pixel coordinates, These are the expected pixel coordinates corresponding to the pixel coordinates.

[0035] Based on the principle of pinhole imaging, the present invention obtains the relationship shown in formula (6): (6) in, The calibrated camera intrinsic parameter matrix is ​​also an invertible matrix, defined in this invention. and Then formula (6) can be rewritten as shown in formula (7): (7) in, and Obtained through image feature extraction, due to It is reversible, therefore it can be calculated. and In this invention, auxiliary signals are defined. , , , The auxiliary signal can be obtained from and It is deduced that, in order to complete the visual servoing task, this invention uses feature points... i For example, the deviation signal is defined as shown in formula (8): (8) in, Let be the angle between the robot's current pose and its desired pose. , and This represents the relative pose deviation signal.

[0036] The present application is based on a direct pose estimation method of feature point pairs, estimating 、 and .

[0037] Equation (2) - Equation (4) is true for any stationary point in the world coordinate system, so it is also applicable to feature points i , and are constants, and . From equation (1), equation (4) and equation (8), a nonlinear system containing unknown parameters or dynamic uncertainties can be obtained as shown in equation (9):

[0038] (9) where the unknown constant is the axis component of , is an unknown constant, 、 and are the 、 and derivatives with respect to time.

[0039] In order to ensure the effective operation of the mobile robot visual servo system translation external parameter estimation method proposed in the present application, the present application designs an efficient adaptive control strategy, which is essential to generate input and output data in real time. For the nonlinear system in equation (9), the adaptive controller applied by the present application is shown in equation (10): (10) where the control gain and are positive real numbers, is the current time, and are auxiliary signals.

[0040] The update rule defined in the present application is shown in equation (11): (11) where and are the and derivatives with respect to time, is a positive real number, is a diagonal positive definite matrix, and are the gains of the adaptive law.

[0041] auxiliary signals and Given as shown in formula (12): (12) Among them, signal Yes The estimate, signal Yes The estimate.

[0042] Consider the adjustment error in a nonlinear system driven by an adaptive controller and an update law. , and It converges asymptotically to zero.

[0043] S102: Represent the nonlinear system in parameterized form; the parameterized form includes a first signal, a second signal, a third signal, and the parameter to be estimated.

[0044] This invention relates to nonlinear systems. and The subsystem is represented in parameterized form, as shown in formula (13): (13) in, As the first signal, for The derivative with respect to time, For the parameters to be estimated, and These are the second signal and the third signal, respectively, and their definitions are shown in formula (14): (14) in, For the second signal, This is the third signal.

[0045] S103: Perform first-order low-pass filtering on the first signal, the second signal, and the third signal to obtain the first filtered signal, the second filtered signal, and the third filtered signal, respectively.

[0046] The first signal Second signal and the third signal The process is performed using a first-order low-pass filter, as shown in equation (15): (15) in, The gain of a first-order low-pass filter. yes initial value, for an initial value of an initial value of is a first filtered signal, is a second filtered signal, is a third filtered signal, and are derivatives with respect to time, respectively. and are derivatives with respect to time, respectively.

[0047] Equation (16) is derived from Equation (13) and Equation (15) and is shown as follows: (16) wherein is an initial value of

[0048] S104: An auxiliary variable is obtained based on the first filtered signal, the second filtered signal and the third filtered signal.

[0049] The auxiliary variable generation equation is shown as Equation (17): (17) wherein is a positive real gain, P and Q are the auxiliary variables, and are derivatives with respect to time, respectively. P and Q are derivatives with respect to time, respectively.

[0050] The solution of the auxiliary variable generation equation (17) is shown as Equation (18) and Equation (19): (18) (19) Equation (20) is derived from Equation (16), Equation (18) and Equation (19) and is shown as follows: (20) S105: An auxiliary signal is generated based on the auxiliary variable.

[0051] Let be an estimated value of , then the auxiliary signal can be defined as shown in Equation (21): (21) wherein W is the auxiliary signal. ​​​​

[0052] It can be proved that the equation as shown in formula (22) is correct: (22) Wherein, is the estimation error of the parameter .

[0053] S106: Based on the auxiliary signal, the gain matrix is dynamically adjusted by the update rule, and the estimated value of the to-be-estimated parameter is obtained within a fixed time, and the estimated value of the translation external parameter is obtained according to the estimated value of the to-be-estimated parameter.

[0054] The update rule of the auxiliary signal W and is as shown in formula (23): (23) Wherein, , , and are components of the auxiliary signal , and are real constants, is the adjustment gain matrix, takes or .

[0055] Considering the nonlinear system with the update law, if the matrix satisfies the persistent excitation condition, that is, there are positive real numbers and , so that the inequality as shown in formula (24) is satisfied, then converges to within a fixed time, and formula (24) is as follows: (24) Wherein, is a unit matrix.

[0056] In order to facilitate the proof of the fixed-time stability of the nonlinear system, the present application proposes a corresponding stability criterion.

[0057] The nonlinear system is as shown in formula (25): (25) Wherein, is the system state, is a locally continuous vector field, and satisfies , let be the solution of formula (25).

[0058] A nonlinear system is said to be globally fixed-time stable if the following conditions are met: (1) The origin of equation (25) is globally Lyapunov stable.

[0059] (2) There exists a stabilization time function such that for any , there exists , and it satisfies the requirement of .

[0060] Fixed-time stability is a special case of finite-time stability, characterized by a convergence time that has an upper bound independent of the initial state. Compared to asymptotic stability, fixed-time stability has significant advantages. Fixed-time stability ensures that the system reaches a stable state within a predetermined finite time, providing stronger predictability in response to initial conditions. Unlike asymptotic stability, which may require infinite time to approach equilibrium, fixed-time stability guarantees timely convergence, making it particularly valuable in real-time applications. Additionally, fixed-time stability often remains robust in the face of disturbances and parameter variations, enhancing the overall performance and reliability of the system.

[0061] To assess whether a system has fixed-time stability, the following criteria are applied: If system (25) is globally fixed-time stable, there exists a continuous radially unbounded function that satisfies the inequality shown in equation (26), which is as follows: (26) where is the derivative of with respect to time, is a positive real set, and the real number , , , and , the stabilization time satisfies equation (27), which is as follows: (27) where is the stabilization time.

[0062] The proof process for fixed-time stability is as follows: Define the Lyapunov candidate function (28) for Taking the time derivative and substituting equation (26) into the derivative expression, we obtain equation (29): (29) where is the derivative with respect to time.

[0063] In the second step of equation (29), the symmetry of is used, and the Jensen inequality is applied. We obtain equations (30) and (31): (30) (31) Substituting equations (30) and (31) into equation (29), we obtain equation (32): (32) Obviously, Therefore, based on , we can deduce that asymptotically converges to zero. According to the definition of , we obtain equation (33): (33) Then, we obtain equations (34) and (35): (34) (35) According to equations (18) and (24), when , we obtain equation (36): (36) where .

[0064] According to equations (22) and (36), we obtain equations (37) and (38): (37) (38) From equations (32), (34), (35), (37), and (38), we obtain equation (39): (39) where , .

[0065] By using a fixed-time stability criterion, it is possible to obtain... It converges to zero within a fixed time; its convergence time is... Defined by the inequality shown in formula (40), formula (40) is as follows: (40) according to The definition can lead to It converges to within a fixed time. .

[0066] Q.E.D.

[0067] Based on The estimated value It is possible to deduce the relationship between the two sides. , and The estimate is shown in the following formula (41): (41) in , and yes The elements, namely .

[0068] In one exemplary embodiment, the present invention provides as follows Figure 3 The system block diagram shown illustrates the entire process of the external parameter estimation strategy. For example... Figure 3 As shown, the translational extrinsic parameter estimation strategy proposed in this invention combines an adaptive control method, which effectively achieves visual stabilization and generates input and output data in real time, which is crucial for the parameter estimation process. When selecting the adaptive control method, a signal that satisfies the persistent excitation condition shown in equation (24) must be chosen, since in equation (10)... and All of them contain trigonometric functions of time, thus ensuring that the condition in (24) is satisfied. Unlike the traditional translational extrinsic parameter identification, the parameter estimation strategy proposed in this invention is performed online, not offline. This method has minimal requirements for data processing and ensures continuous real-time performance. In addition, in equations (16), (17) and (19), this invention modifies the nonlinear system parameter estimation method, and this updated method is called the improved parameter estimation method.

[0069] In an exemplary embodiment, the effectiveness of the translational extrinsic parameter estimation for a mobile robot visual servoing system proposed in this invention is verified through numerical simulation. It is assumed that there are three feature points in the scene, which... The coordinates in the equation are shown in formula (42): (42) where the unit of the feature point coordinates is meter In the simulation, the present invention chooses the feature points as the reference feature points to obtain , and Therefore, .

[0070] The intrinsic parameter matrix of the camera is set as shown in equation (43): (43) where is the intrinsic parameter matrix of the camera.

[0071] The resolution of the camera is pixels, the translation extrinsic parameters and are set as .

[0072] , and are set as .

[0073] The parameters of the adaptive controller are chosen as: .

[0074] The parameters of the fixed-time parameter estimation method are set as: .

[0075] Figures 4-7 The simulation results of the adaptive control method are shown. Figure 4 The curves of the regulation errors , and are plotted. It can be clearly seen from Figure 4 that the regulation errors , and asymptotically converge to zero, proving that the visual stabilization task has been successfully completed. Figure 5 The image trajectories of the three feature points in the current camera are shown. Obviously, the feature points can be adjusted from the initial positions (indicated by circles) to the desired positions (indicated by squares). Figure 6 The curves of the two inputs of the mobile robot: the angular velocity and the linear velocity are shown. Figure 7 The and the trajectory. Although the estimated parameters eventually converge to constants, the estimated parameters do not converge to the actual values, and the bias remains significant.

[0076] Figure 8 Simulation results of the improved parameter estimation method are shown. From Figure 8 it can be observed that the three components of the trajectory, i.e. , and , converge to constants in less than 5 seconds. In addition, , and eventually converge to 0.094, 0.047 and 0.47, respectively, which are consistent with the true values. Therefore, the values of , and can be accurately estimated using equation (41).

[0077] The estimation method of the translational extrinsic parameters of the mobile robot visual servo system provided by the present application can be applied to the estimation problem of the rotational extrinsic parameters.

[0078] In the application of the estimation method of the translational extrinsic parameters of the mobile robot visual servo system provided by the present application, the order of execution of each step shown in Figure 1 may not be executed, and the execution order of each step can be determined as needed, and the present application does not limit this.

[0079] The above is an estimation method of translational extrinsic parameters of a mobile robot visual servo system provided by one or more embodiments of the present application, based on the same idea, the present application also provides a corresponding estimation device of translational extrinsic parameters of a mobile robot visual servo system, as shown in Figure 9 .

[0080] Figure 9 The estimation device of translational extrinsic parameters of a mobile robot visual servo system provided by the present application is a schematic diagram, which includes: The conversion module 901 is used to convert the estimation of the translational extrinsic parameters of the mobile robot visual servo system into a parameter estimation problem of a nonlinear system.

[0081] The parameterization module 902 is used to represent the nonlinear system in a parameterized form; the parameterized form includes a first signal, a second signal, a third signal and a parameter to be estimated.

[0082] The filtering module 903 is used to perform first-order low-pass filtering processing on the first signal, the second signal and the third signal, to obtain a first filtered signal, a second filtered signal and a third filtered signal, respectively.

[0083] The first determination module 904 is configured to obtain an auxiliary variable based on the first filtered signal, the second filtered signal and the third filtered signal.

[0084] The generation module 905 is configured to generate an auxiliary signal based on the auxiliary variable.

[0085] The second determination module 906 is configured to dynamically adjust a gain matrix by an updating rule based on the auxiliary signal, converge to obtain an estimated value of the to-be-estimated parameter within a fixed time, and obtain an estimated value of the translational external parameter according to the estimated value of the to-be-estimated parameter.

[0086] The specific definition of the device for estimating the translational external parameter of the mobile robot visual servo system can refer to the definition of the method for estimating the translational external parameter of the mobile robot visual servo system, which will not be repeated here. Each module in the device for estimating the translational external parameter of the mobile robot visual servo system can be realized by software, hardware or a combination thereof. The modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0087] The application further provides a computer readable storage medium, which stores a computer program, and the computer program can be used to execute the method for estimating the translational external parameter of the mobile robot visual servo system. Figure 1 The application further provides a computer readable storage medium, which stores a computer program, and the computer program can be used to execute the method for estimating the translational external parameter of the mobile robot visual servo system.

[0088] The application further provides a computer readable storage medium, which stores a computer program, and the computer program can be used to execute the method for estimating the translational external parameter of the mobile robot visual servo system. Figure 10 The computer device shown in the structural schematic diagram, as shown in the structural schematic diagram, the computer device includes a processor, an internal bus, a network interface, a memory and a non-volatile memory, and of course can also include other hardware required by a business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to realize the method for estimating the translational external parameter of the mobile robot visual servo system provided by the application. Figure 10 The computer device shown in the structural schematic diagram, as shown in the structural schematic diagram, the computer device includes a processor, an internal bus, a network interface, a memory and a non-volatile memory, and of course can also include other hardware required by a business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to realize the method for estimating the translational external parameter of the mobile robot visual servo system provided by the application. Figure 1 The computer device shown in the structural schematic diagram, as shown in the structural schematic diagram, the computer device includes a processor, an internal bus, a network interface, a memory and a non-volatile memory, and of course can also include other hardware required by a business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to realize the method for estimating the translational external parameter of the mobile robot visual servo system provided by the application.

[0089] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of the methods. In the embodiments of the present application, any reference to memory, storage, database or other medium can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0090] The technical features of the above embodiments can be combined in any way. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, but as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

Claims

1. A method for estimating translational extrinsic parameters of a mobile robot visual servo system, characterized in that, include: The problem of estimating translational extrinsic parameters of a mobile robot's visual servoing system is transformed into the problem of parameter estimation for a nonlinear system. The nonlinear system is represented in parameterized form; the parameterized form includes a first signal, a second signal, a third signal, and the parameter to be estimated. The first signal, the second signal, and the third signal are subjected to first-order low-pass filtering to obtain the first filtered signal, the second filtered signal, and the third filtered signal, respectively. Based on the first filtered signal, the second filtered signal, and the third filtered signal, auxiliary variables are obtained; Based on the aforementioned auxiliary variables, an auxiliary signal is generated; Based on the auxiliary signal, the gain matrix is ​​dynamically adjusted by the update rule, and the estimated value of the parameter to be estimated is obtained within a fixed time. The estimated value of the external parameter is obtained by shifting the estimated value of the parameter to be estimated.

2. The method as described in claim 1, characterized in that, The nonlinear system is: in, For the robot's angular velocity, , , D, L For the translational external parameters of the mobile robot's visual servo system, For unknown constants, Let be the angle between the robot's current pose and its desired pose. , and This represents the relative pose deviation signal. , and They are respectively , and The derivative with respect to time, For the first i The coordinates of each feature point in the desired camera coordinate system.

3. The method as described in claim 2, characterized in that, The parameterization form of the nonlinear system is as follows: in, for The derivative with respect to time, , , , v For the robot's linear velocity, , , i For the first i One feature point, , , , All are auxiliary signals. For the first i The coordinates of each feature point in the camera coordinate system For the first i The coordinates of each feature point in the camera's desired coordinate system As the first signal, For the second signal, As the third signal, These are the parameters to be estimated.

4. The method as described in claim 3, characterized in that, The first-order low-pass filtering process is as follows: in, The gain of a first-order low-pass filter, This is the first filtered signal. This is the second filtered signal. This is the third filtered signal. , and They are respectively , and The derivative with respect to time.

5. The method as described in claim 4, characterized in that, The auxiliary variable is generated through an auxiliary variable generation equation, which is: in, For positive real gain, P and Q As an auxiliary variable, and They are respectively P and Q The derivative with respect to time.

6. The method as described in claim 5, characterized in that, The formula for calculating the auxiliary signal is: in, W As an auxiliary signal, For the parameters to be estimated, for The estimated value, for The estimation error.

7. The method as described in claim 6, characterized in that, The update rule is as follows: in, , , and It is an auxiliary signal The amount, and are real constants. To adjust the gain matrix, Pick or .

8. The method as described in claim 7, characterized in that, The estimated values ​​of the translation extrinsic parameters are: in, , and yes The elements, namely , , These are the estimated values ​​of the translation extrinsic parameters.

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