Robot control method, device, storage medium and electronic device

Abstract

The present application proposes a control method, device, storage medium and electronic device for a robot. The robot includes a target joint, which is used to drive a first object to move. The control method includes: obtaining a recursive prediction model of the robot, a first motion parameter of the target joint and a second motion parameter of the first object; updating the recursive prediction model according to the first motion parameter; under target constraints, determining a first control instruction according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object, the first control instruction being a control instruction for the first object; and controlling the target joint action according to the first control instruction and the first motion parameter.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a robot control method, device, storage medium and electronic device. Background Art

[0002] In the related art, when a traditional controller controls a sampling robot to perform a sampling operation, there are problems of overshoot and phase delay, which results in low efficiency of the sampling operation and cannot ensure the comfort of the person being sampled. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems existing in the related art.

[0004] To this end, a first aspect of the present application is to propose a control method for a robot.

[0005] A second aspect of the present application is to provide a control device for a robot.

[0006] The third aspect of the present application is to provide another control device for a robot.

[0007] A fourth aspect of the present application is to provide a readable storage medium.

[0008] A fifth aspect of the present application is to provide an electronic device.

[0009] A sixth aspect of the present application is to provide a computer program product.

[0010] In view of this, according to one aspect of the present application, a control method for a robot is proposed, the robot includes a target joint, the target joint is used to drive a first object to move, the control method includes: obtaining a recursive prediction model of the robot, a first motion parameter of the target joint and a second motion parameter of the first object; updating the recursive prediction model according to the first motion parameter; under the target constraint condition, determining a first control instruction according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object, the first control instruction being a control instruction for the first object; controlling the target joint movement according to the first control instruction and the first motion parameter.

[0011] It should be noted that the executor of the robot control method proposed in the present application may be the control device of the robot. In order to more clearly illustrate the robot control method proposed in the present application, the following technical scheme will exemplify the executor of the robot control method as the control device of the robot.

[0012] In this technical solution, the robot refers to a robot used for sampling the human oral cavity, the target joint refers to a joint on the machine used to perform sampling work, the first object refers to an object driven by the target joint, such as a sampling cotton swab, etc., the recursive prediction model refers to a pre-established mathematical model of the robot, the target constraint conditions refer to physical constraints on the first object and the movement of the robot; the target motion parameters refer to the position that the user expects the first object to reach, the movement speed of the first object, or the force that the first object is expected to be subjected to.

[0013] Specifically, the control device first obtains a pre-established mathematical model of the robot, namely the above-mentioned recursive prediction model; obtains the motion parameters of the above-mentioned target joint, namely the above-mentioned first motion parameters; obtains the motion parameters of the above-mentioned first object, namely the above-mentioned second motion parameters.

[0014] Specifically, the control device can clarify the current state of the above-mentioned target joint according to the above-mentioned first motion parameter, for example, the current position and current speed of the target joint, etc.; and can clarify the current state of the above-mentioned first object according to the above-mentioned second motion parameter, for example, the current position of the first object, the current speed of the first object and the current force acting on the first object, etc.

[0015] Furthermore, the control device updates the recursive prediction model according to the first motion parameter. Specifically, during the process of the robot sampling the human oral cavity, the configuration of the robot changes from time to time. If the movement of the robot is directly controlled according to the recursive prediction model of the robot itself, the movement of the robot will be biased. Therefore, the control device needs to first update the recursive prediction model according to the first motion parameter of the target joint.

[0016] Furthermore, the control device determines a first control instruction under the target constraint condition according to the target operation parameter, the second motion parameter and the updated recursive prediction model. Specifically, the first control instruction represents a control instruction for the first object.

[0017] Specifically, based on the updated recursive prediction model, the objective function of the preset optimization algorithm can be constructed. With the second motion parameter and the target motion parameter as the input of the objective function, the optimal control law with the smallest deviation between the target motion parameter and the actual motion parameter of the first object under the target constraint condition can be solved. The control device can determine how to control the action of the first object according to the optimal control law, that is, it can determine the first control instruction for the first object.

[0018] Further, the control device controls the movement of the target joint according to the first control instruction and the first motion parameter. Specifically, since the first object moves driven by the target joint, the control device cannot directly control the movement of the first object. Therefore, the control device needs to control the movement of the target joint according to the first control instruction and the parameters of the target joint.

[0019] In the related art, when a sampling robot is controlled to perform sampling operations through traditional controllers such as PID (Proportional Integral Derivative) controllers, the controller has overshoot and phase delay. In this case, when normal damping PID control instructions are used, the overshoot delay will cause the first object and the human mouth to be unable to achieve comfortable contact. Using high damping PID control instructions will cause the robot to respond slowly due to phase delay, resulting in extended sampling time.

[0020] Therefore, in the above-mentioned technical scheme of the present application, the control device adopts a recursive prediction model updated according to the first motion parameter in the process of solving the first control instruction. In this way, the overshoot and phase delay of the control instruction are reduced, the response speed of the robot is improved, and the efficiency of the sampling operation is improved; at the same time, by limiting the above-mentioned target constraint conditions, physical constraints exist in the action process of the first object, thereby ensuring the comfort of the sampled person when the robot performs sampling operations on the human mouth, that is, the control method of the robot proposed in the above-mentioned technical scheme of the present application is adopted to achieve both sampling operation efficiency and sampling comfort, and solves the problem in the related technology that the sampling operation efficiency is low and the comfort of the sampled person cannot be guaranteed due to the overshoot and phase delay of the traditional controller.

[0021] In addition, the robot control method proposed in the above technical solution of the present application may also have the following additional technical features:

[0022] In the above technical scheme, the step of determining the first control instruction based on the updated recursive prediction model, the second motion parameters and the target motion parameters of the first object specifically includes: determining the objective function of the quadratic optimization algorithm based on the updated recursive prediction model; taking the second motion parameters and the target motion parameters as input, solving the objective function under the target constraints, and determining the first control instruction.

[0023] In this technical solution, the control device can determine the objective function of the optimization algorithm used, that is, the objective function of the quadratic optimization algorithm, based on the updated recursive prediction model, and can solve the determined objective function based on the second motion parameter of the first object and the target motion parameter under the target constraint condition to determine the above-mentioned first control instruction. In this way, it is ensured that when the target joint action is subsequently controlled according to the first control instruction, that is, when the robot is controlled to perform sampling operations, the requirements of sampling efficiency and comfort can be taken into account at the same time.

[0024] In the above technical solution, the robot is used to sample the human oral cavity. Before determining the first control instruction based on the updated recursive prediction model, the second motion parameters and the target motion parameters of the first object, the control method also includes: obtaining the depth coordinates of the human oral cavity and the basic parameters of the first object; and constructing target constraints based on the depth coordinates and the basic parameters.

[0025] In this technical solution, before solving the first control instruction, the control device can determine the target constraint condition for solving the first control instruction according to the acquired depth coordinates in the human oral cavity and the basic information of the first object. In this way, the target constraint condition corresponds to the internal conditions of the human oral cavity and the conditions of the first object, ensuring the accuracy of the first control instruction solved according to the target constraint condition in the subsequent steps.

[0026] In the above technical solution, the basic parameters include the friction coefficient between the first object and the inner wall of the human oral cavity and the size parameters of the first object, the target constraint conditions include the first constraint conditions and the second constraint conditions, and the steps of constructing the target constraint conditions according to the depth coordinates and the basic parameters specifically include: determining the first constraint conditions according to the depth coordinates; determining the second constraint conditions according to the friction coefficient and the size parameters.

[0027] In this technical solution, the control device can construct an inequality constraint on the position of the first object according to the depth coordinates in the human mouth, that is, the above-mentioned first constraint condition. In this way, it can be ensured that when the robot performs the sampling operation, it will not cause danger to the oral cavity of the sampled person; the control device can construct an inequality constraint on the friction cone of the first object according to the above-mentioned friction coefficient and size information, that is, the above-mentioned second constraint condition. In this way, it can be ensured that when the robot performs the sampling operation, it will not cause discomfort to the sampled person due to excessive force, nor will it cause inaccurate sampling points touched due to the bending of the first object, or inaccurate force control feedback.

[0028] In the above technical scheme, the first motion parameter includes a first position and a first speed of the target joint, and the step of controlling the target joint movement according to the first control instruction and the first motion parameter specifically includes: converting the first control instruction into a second control instruction for the target joint; controlling the target joint movement according to the first position, the first speed and the second control instruction.

[0029] In this technical solution, when controlling the target joint movement, the control device converts the first instruction, determines the second control instruction, and comprehensively considers the current position and current speed of the target joint, that is, the above-mentioned first position and the above-mentioned first speed. In this way, the accuracy of the control of the target joint movement is guaranteed, and then the stability of the robot operation is guaranteed.

[0030] In the above technical scheme, the step of controlling the target joint movement according to the first position, the first speed and the second control instruction specifically includes: determining the first compensation parameter according to the first position and the first speed; optimizing the second control instruction according to the first compensation parameter, and controlling the target joint movement according to the optimized second control instruction.

[0031] In this technical solution, before controlling the target joint action, the control device determines the first compensation parameter by the current position and current speed of the target joint, that is, the first position and the first speed, and performs feedforward compensation of the nonlinear term on the second control instruction by the first compensation parameter. In this way, the nonlinearity in the nonlinear dynamic equation is partially linearized so that it can be applied to the linear optimization method, so that the second control instruction for controlling the target joint action is nonlinearly optimized, ensuring the accuracy of the subsequent control of the target joint action according to the optimized second control instruction.

[0032] In the above technical solution, the first motion parameter includes a first position of the target joint, and the step of updating the recursive prediction model according to the first motion parameter specifically includes:

[0033] The recursive prediction model is updated according to the first position.

[0034] In this technical solution, the control device updates the recursive prediction model according to the current position of the target joint, that is, the above-mentioned first position, so that the updated recursive prediction model can predict the states of the robot and the first object in the future as accurately as possible, and then can derive the optimal first control instruction in the future based on the states of the robot and the first object.

[0035] In the above technical solution, the second motion parameter includes at least one of the second position of the first object, the second speed of the first object and the first force acting on the first object, but is not limited thereto.

[0036] According to the second aspect of the present application, a control device of a robot is proposed, the robot includes a target joint, the target joint is used to drive the movement of a first object, the control device of the robot includes: an acquisition module, used to acquire a recursive prediction model of the robot, a first motion parameter of the target joint and a second motion parameter of the first object; a first processing module, used to update the recursive prediction model according to the first motion parameter; a second processing module, used to determine a first control instruction according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object under the target constraint condition, the first control instruction being a control instruction for the first object; and a third processing module, used to control the movement of the target joint according to the first control instruction and the first motion parameter.

[0037] In this technical solution, the robot refers to a robot used for sampling the human oral cavity, the target joint refers to a joint on the machine used to perform sampling work, the first object refers to an object driven by the target joint, such as a sampling cotton swab, etc., the recursive prediction model refers to a pre-established mathematical model of the robot, the target constraint conditions refer to physical constraints on the first object and the movement of the robot; the target motion parameters refer to the position that the user expects the first object to reach, the movement speed of the first object, or the force that the first object is expected to be subjected to.

[0038] Specifically, firstly, the mathematical model of the robot established in advance, namely the recursive prediction model, is acquired through the acquisition module; the motion parameters of the target joint, namely the first motion parameters, are acquired; and the motion parameters of the first object, namely the second motion parameters, are acquired.

[0039] Specifically, the robot's control device can clarify the current state of the above-mentioned target joint according to the above-mentioned first motion parameter, for example, the current position and current speed of the target joint, etc.; according to the above-mentioned second motion parameter, the current state of the above-mentioned first object can be clarified, for example, the current position of the first object, the current speed of the first object and the current force acting on the first object, etc. Therefore, it is necessary to first obtain the above-mentioned recursive prediction model, the above-mentioned first motion parameter and the above-mentioned second motion parameter through the above-mentioned acquisition module.

[0040] Furthermore, the first processing module updates the recursive prediction model according to the first motion parameter. Specifically, in the process of the robot sampling the human oral cavity, the configuration of the robot changes from time to time. If the movement of the robot is directly controlled according to the recursive prediction model of the robot itself, the movement of the robot will be biased. Therefore, the first processing module needs to first update the recursive prediction model according to the first motion parameter of the target joint.

[0041] Furthermore, the second processing module determines a first control instruction under the target constraint condition according to the target operation parameter, the second motion parameter and the updated recursive prediction model. Specifically, the first control instruction represents a control instruction for the first object.

[0042] Specifically, the objective function of the preset optimization algorithm can be constructed based on the updated recursive prediction model. With the second motion parameters and the target motion parameters as inputs of the objective function, the optimal control law with the smallest deviation between the target motion parameters and the actual motion parameters of the first object under the target constraint conditions can be solved. The second processing module can determine how to control the action of the first object based on the optimal control law, that is, it can determine the first control instruction for the first object.

[0043] Furthermore, the third processing module controls the movement of the target joint according to the first control instruction and the first motion parameter. Specifically, since the first object moves driven by the target joint, the third processing module cannot directly control the movement of the first object. Therefore, the third processing module needs to control the movement of the target joint according to the first control instruction and the parameters of the target joint.

[0044] In the related art, when a sampling robot is controlled by a traditional controller to perform sampling operations, the controller has overshoot and phase delay. In this case, when normal damping PID control instructions are used, the overshoot will cause the first object and the human mouth to be unable to achieve comfortable contact. Using high damping PID control instructions will cause the robot to respond slowly due to phase delay, resulting in extended sampling time.

[0045] Therefore, in the above-mentioned technical scheme of the present application, the second processing module adopts a recursive prediction model updated according to the first motion parameter in the process of solving the first control instruction. In this way, the overshoot and phase delay of the control instruction are reduced, the response speed of the robot is improved, and the efficiency of the sampling operation is improved; at the same time, by limiting the above-mentioned target constraint conditions, physical constraints exist in the action process of the first object, thereby ensuring the comfort of the sampled person when the robot performs sampling operations on the human mouth, that is, the control device of the robot proposed in the above-mentioned technical scheme of the present application is adopted to achieve both sampling operation efficiency and sampling comfort, thereby solving the problem in the related technology that the sampling operation efficiency is low and the comfort of the sampled person cannot be guaranteed due to the overshoot and phase delay of the traditional controller.

[0046] According to the third aspect of the present application, a robot control device is proposed, comprising: a memory, in which programs or instructions are stored; a processor, which executes the programs or instructions stored in the memory to implement the steps of the robot control method proposed in the above technical scheme of the present application, and thus has all the beneficial technical effects of the robot control method proposed in the above technical scheme of the present application, which will not be elaborated herein.

[0047] According to the fourth aspect of the present application, a readable storage medium is provided, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the control method of the robot proposed in the above technical solution of the present application is implemented. Therefore, the readable storage medium has all the beneficial effects of the control method of the robot proposed in the above technical solution of the present application, which will not be repeated here.

[0048] According to the fifth aspect of the present application, an electronic device is proposed, comprising a control device of a robot as proposed in the above technical solution of the present application, and / or a readable storage medium as proposed in the above technical solution of the present application. Therefore, the electronic device has all the beneficial effects of the control device of the robot proposed in the above technical solution of the present application and / or the readable storage medium proposed in the above technical solution of the present application, which will not be repeated here.

[0049] According to the sixth aspect of the present application, a computer program product is proposed, which includes a computer program, and when the computer program is executed by a processor, the control method of the robot proposed in the above technical solution of the present application is implemented. Therefore, the computer program product has all the beneficial effects of the control method of the robot proposed in the above technical solution of the present application, which will not be repeated here.

[0050] Additional aspects and advantages of the present application will become apparent in the following description or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0052] Figure 1 One of the flow charts of the robot control method according to the embodiment of the present application is shown;

[0053] Figure 2 A schematic diagram showing overshoot and phase delay of a conventional controller in the related art is shown;

[0054] Figure 3 A second flow chart of the robot control method according to an embodiment of the present application is shown;

[0055] Figure 4The third flowchart of the robot control method according to the embodiment of the present application is shown;

[0056] Figure 5 A schematic diagram of a human oral cavity according to an embodiment of the present application is shown;

[0057] Figure 6 A fourth flowchart of the robot control method according to an embodiment of the present application is shown;

[0058] Figure 7 A schematic diagram showing a friction cone of a first object of an embodiment of the present application is shown;

[0059] Figure 8 One of the schematic block diagrams of the control device of the robot according to the embodiment of the present application is shown;

[0060] Fig. 9 A second schematic block diagram showing a control device for a robot according to an embodiment of the present application;

[0061] Fig.10 A control block diagram of a robot control method according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0062] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0063] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0064] Combine the following Figures 1 to 10 , a robot control method, device, storage medium and electronic device provided in an embodiment of the present application are described in detail through specific embodiments and their application scenarios.

[0065] Embodiment 1:

[0066] Figure 1 A flow chart of a control method for a robot according to an embodiment of the present application is shown, wherein the robot includes a target joint, the target joint is used to drive a first object to move, and the control method includes:

[0067] S102, obtaining a recursive prediction model of the robot, a first motion parameter of a target joint, and a second motion parameter of the first object;

[0068] S104, updating the recursive prediction model according to the first motion parameter;

[0069] S106, under the target constraint condition, determining a first control instruction according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object, where the first control instruction is a control instruction for the first object;

[0070] S108, controlling the target joint movement according to the first control instruction and the first motion parameter.

[0071] It should be noted that the executor of the robot control method proposed in the present application may be the control device of the robot. In order to more clearly illustrate the robot control method proposed in the present application, the following embodiments will exemplify the executor of the robot control method as the control device of the robot.

[0072] In this embodiment, the robot represents a robot used to take samples from the human oral cavity, the target joint represents a joint on the machine used to perform sampling work, and a package end controller can be set on the joint; the first object represents an object driven by the target joint, such as a sampling cotton swab, etc., the recursive prediction model represents a pre-established mathematical model of the robot, the target constraint condition represents the physical constraint condition for the first object and the movement of the robot; the target motion parameter represents the position that the user expects the first object to reach, the expected movement speed of the first object, or the expected force on the first object.

[0073] Specifically, the control device first obtains a pre-established mathematical model of the robot, namely the above-mentioned recursive prediction model; obtains the motion parameters of the above-mentioned target joint, namely the above-mentioned first motion parameters; obtains the motion parameters of the above-mentioned first object, namely the above-mentioned second motion parameters.

[0074] Specifically, the control device can clarify the current state of the above-mentioned target joint according to the above-mentioned first motion parameter, for example, the current position and current speed of the target joint, etc.; and can clarify the current state of the above-mentioned first object according to the above-mentioned second motion parameter, for example, the current position of the first object, the current speed of the first object and the current force acting on the first object, etc.

[0075] Exemplarily, the recursive prediction model of the robot may be constructed in advance according to the configuration of the robot or the full dynamics equation of the robot, but is not limited thereto.

[0076] Exemplarily, the construction of the recursive prediction model is described by taking the construction of the recursive prediction model according to the full dynamics equation of the robot as an example, wherein the expression of the full dynamics equation of the robot is as follows:

[0077]

[0078] Where M represents the inertia matrix of the target joint, represents the acceleration of the target joint, V represents the nonlinear term of the target joint, and τ represents the torque of the target joint.

[0079] Further, invert the above full dynamic equation and multiply both sides by JM -1 , we get the following expression:

[0080]

[0081] Where J represents the Jacobian matrix calculated based on the position vector q of the target joint.

[0082] Further, according to the mapping relationship between the acceleration and torque of the target joint and the first object Consider nonlinear compensation Eliminate and make the inverse of the nominal inertia matrix of the first object Λ = JM -1 K T , the above mapping relationship can be transformed into

[0083] in, represents the acceleration of the first object, represents the velocity of the target joint, F represents the current force acting on the first object, and T represents the torque of the first object.

[0084] Furthermore, considering the discrete servo cycle, the above mapping relationship after transformation has the following expression:

[0085]

[0086]

[0087] Where x represents the position of the first object, represents the speed of the first object, Δt represents the length of the discrete servo cycle, I represents the unit diagonal matrix, A represents the state transfer matrix, B represents the state input matrix, C represents the state output matrix, y n represents the position coordinates of the first object in the human mouth in the nth servo cycle, and Λ represents the inverse of the nominal inertia matrix.

[0088] Specifically, the expressions of the state transfer matrix A, the state input matrix B, and the state output matrix C are as follows:

[0089]

[0090]

[0091] C = [I0];

[0092] Furthermore, based on the above-mentioned state transfer matrix A, state input matrix B and state output matrix C, a recursive prediction model of the robot can be obtained.

[0093] Furthermore, the control device updates the recursive prediction model according to the first motion parameter. Specifically, during the process of the robot sampling the human oral cavity, the configuration of the robot changes from time to time. If the robot's motion is directly controlled according to the robot's own recursive prediction model, the robot's motion will be biased. Therefore, the control device needs to first update the recursive prediction model according to the first motion parameter of the target joint.

[0094] Furthermore, the control device determines a first control instruction under the target constraint condition according to the target operation parameter, the second motion parameter and the updated recursive prediction model. Specifically, the first control instruction represents a control instruction for the first object.

[0095] Specifically, based on the updated recursive prediction model, the objective function of the preset optimization algorithm can be constructed. With the second motion parameter and the target motion parameter as the input of the objective function, the optimal control law with the smallest deviation between the target motion parameter and the actual motion parameter of the first object under the target constraint condition can be solved. The control device can determine how to control the action of the first object according to the optimal control law, that is, it can determine the first control instruction for the first object.

[0096] Further, the control device controls the movement of the target joint according to the first control instruction and the first motion parameter. Specifically, since the first object moves driven by the target joint, the control device cannot directly control the movement of the first object. Therefore, the control device needs to control the movement of the target joint according to the first control instruction and the parameters of the target joint.

[0097] In the related art, when a sampling robot is controlled by a traditional controller to perform a sampling operation, the controller has the following problems: Figure 2 As shown in the figure, the overshoot and phase delay, in this case, when the normal damping PID control instruction is used, the overshoot will cause the first object and the human mouth to be unable to achieve comfortable contact, and the high damping PID control instruction will cause the robot to respond slowly due to the phase delay, resulting in a longer sampling time, where L1 represents the PID standard control instruction, L2 represents the normal damping PID control instruction, and L3 represents the high damping PID control instruction.

[0098] Therefore, in the above-mentioned embodiment of the present application, the control device adopts a recursive prediction model updated according to the first motion parameter in the process of solving the first control instruction. In this way, the overshoot and phase delay of the control instruction are reduced, the response speed of the robot is improved, and the efficiency of the sampling operation is improved; at the same time, by limiting the above-mentioned target constraint conditions, physical constraints exist in the action process of the first object, thereby ensuring the comfort of the sampled person when the robot performs sampling operations on the human mouth. That is, the control method of the robot proposed in the above-mentioned embodiment of the present application is adopted to achieve both sampling operation efficiency and sampling comfort, thereby solving the problem in the related art that the sampling operation efficiency is low and the comfort of the sampled person cannot be guaranteed due to the overshoot and phase delay of the traditional controller.

[0099] Figure 3 A schematic flow chart of a control method for a robot according to an embodiment of the present application is shown, wherein the control method comprises:

[0100] S302, obtaining a recursive prediction model of the robot, a first motion parameter of a target joint, and a second motion parameter of the first object;

[0101] S304, updating the recursive prediction model according to the first motion parameter;

[0102] S306, determining the objective function of the quadratic optimization algorithm according to the updated recursive prediction model;

[0103] S308, using the second motion parameter and the target motion parameter as input, solving the target function under the target constraint condition, and determining the first control instruction;

[0104] S310, controlling the target joint movement according to the first control instruction and the first motion parameter.

[0105] In this embodiment, the quadratic optimization algorithm is an algorithm for solving a control law for minimizing the deviation between a target value and an actual value under constraints.

[0106] Specifically, the process of determining the above-mentioned first control instruction is: the control device first determines the objective function of the quadratic optimization algorithm according to the updated recursive prediction model.

[0107] Specifically, the current shape of the robot and the forces it is subjected to can be determined based on the updated recursive prediction model. The forces acting on the robot are taken as optimizable variables, and the error of the optimization steps of the quadratic optimization is set to obtain the above objective function.

[0108] Exemplarily, the expression of the updated recursive prediction model is as follows:

[0109] Y=θx n +ΦΓ;

[0110] Wherein, Y represents the position coordinate of the first object in the human mouth, Y={y n y n+1 … Y n+i-1}, i represents the number of steps of position prediction optimization, Γ represents the force on the end of the first object, Γ={F n F n+1 … F n+j-1}, j represents the number of steps of force prediction optimization, θ represents the state transfer inference matrix, Φ represents the state input inference matrix, and the calculation methods of θ and Φ are as follows:

[0111]

[0112]

[0113] Among them, A represents the state transfer matrix, B represents the state input matrix, and C represents the state output matrix.

[0114] Further, the error E=Y within the predicted optimization step i des -Y and the force Г on the first object are optimizable variables. Combined with the expression of the updated recursive prediction model above, the objective function of the quadratic optimization algorithm can be obtained as follows:

[0115]

[0116] Among them, J represents the Jacobian matrix, Y des represents the updated position coordinates of the first object in the human mouth, W 1 and W 2 Represents the weight of each optimization objective to the objective function.

[0117] Specifically, the above objective function needs to satisfy the equality constraint of dynamics, and the equality constraint expression of dynamics is as follows:

[0118]

[0119] Among them, I represents the unit diagonal matrix, x represents the position of the first object, θ represents the state transfer inference matrix, Φ represents the state input inference matrix, E represents the error within the predicted optimization step i, and Γ represents the force acting on the end of the first object.

[0120] Furthermore, the control device uses the second motion parameter and the target motion parameter as inputs of the target function, and solves the target function under the target constraint condition to determine the first control instruction.

[0121] Specifically, taking the second motion parameter as the current force acting on the first object and the target motion parameter as the force that the user expects the first object to be acted on as an example, the control device adopts the above-mentioned objective function and can solve the optimal control law with the smallest deviation between the current force acting on the first object and the force that the user expects the first object to be acted on under the target constraint condition. The control device can determine how to control the movement of the first object based on the optimal control law, that is, it can determine the first control instruction for the first object.

[0122] In this embodiment, the control device can determine the objective function of the optimization algorithm used, that is, the objective function of the quadratic optimization algorithm, according to the updated recursive prediction model, and can solve the determined objective function according to the second motion parameter of the first object and the target motion parameter under the target constraint condition to determine the above-mentioned first control instruction. In this way, it is ensured that when the target joint action is subsequently controlled according to the first control instruction, that is, when the robot is controlled to perform sampling operations, the requirements of sampling efficiency and comfort can be taken into account at the same time.

[0123] Figure 4 A schematic flow chart of a control method of a robot according to an embodiment of the present application is shown, wherein the robot is used to take samples from a human oral cavity, and the control method includes:

[0124] S402, obtaining a recursive prediction model of the robot, a first motion parameter of a target joint, and a second motion parameter of the first object;

[0125] S404, updating the recursive prediction model according to the first motion parameter;

[0126] S406, obtaining the depth coordinates of the human oral cavity and basic parameters of the first object;

[0127] S408, constructing target constraint conditions according to the depth coordinates and basic parameters;

[0128] S410, under the target constraint condition, determining a first control instruction according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object, where the first control instruction is a control instruction for the first object;

[0129] S412, controlling the target joint movement according to the first control instruction and the first motion parameter.

[0130] In this embodiment, the robot is specifically used to take samples from the human oral cavity, and the basic parameters of the first object represent characteristic parameters and size parameters of the first object.

[0131] Specifically, before determining the above-mentioned first control instruction, the control device also needs to determine the above-mentioned target constraint conditions. The specific process of determining the target constraint conditions is: the control device first obtains the depth coordinates inside the human mouth and the basic parameters of the above-mentioned first object.

[0132] Specifically, the robot is provided with a measuring device such as a depth camera or a laser sensor, and the control device can obtain the depth coordinates through the measuring device.

[0133] Specifically, the first object driven by the target joint of the robot is an object of uniform specifications, and its characteristic information and size information will be stored in the storage space of the robot. Therefore, the control device can obtain the above basic information by retrieving information from the storage space of the robot.

[0134] For example, the schematic diagram of the human oral cavity detected by the above measuring device is as follows: Figure 5 As shown, where Y ub Represents the constraints of the depth coordinate of the human mouth, Y des Indicates the coordinates of the first object that can reach the preset depth of the human mouth. ub and the above Y des The above target constraints can be determined by combining the above basic information.

[0135] Furthermore, the control device determines the target constraint conditions when solving the first control instruction according to the above basic information and the above depth coordinates. Specifically, the actual situation inside the human oral cavity can be determined according to the above depth coordinates, and the conditions for the first object to move in the oral cavity can be limited according to the situation; the restriction conditions for the first object to move can be clarified according to the above basic information, so the control device can determine the target constraint conditions according to the above basic information and the above depth coordinates.

[0136] In this embodiment, before solving the first control instruction, the control device can determine the target constraint condition for solving the first control instruction according to the acquired depth coordinates in the human oral cavity and the basic information of the first object. In this way, the target constraint condition corresponds to the internal conditions of the human oral cavity and the conditions of the first object, ensuring the accuracy of the first control instruction solved according to the target constraint condition in the subsequent steps.

[0137] Figure 6 A flow chart of a control method for a robot according to an embodiment of the present application is shown, wherein the basic parameters include the friction coefficient between the first object and the inner wall of the human oral cavity and the size parameter of the first object, the target constraint condition includes the first constraint condition and the second constraint condition, and the control method includes:

[0138] S602, obtaining a recursive prediction model of the robot, a first motion parameter of a target joint, and a second motion parameter of the first object;

[0139] S604, updating the recursive prediction model according to the first motion parameter;

[0140] S606, obtaining the depth coordinates of the human oral cavity and basic parameters of the first object;

[0141] S608, determining a first constraint condition according to the depth coordinate;

[0142] S610, determining a second constraint condition according to the friction coefficient and the size parameter;

[0143] S612, under the target constraint condition, determining a first control instruction according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object, where the first control instruction is a control instruction for the first object;

[0144] S614, controlling the target joint movement according to the first control instruction and the first motion parameter.

[0145] In this embodiment, the basic parameters of the first object specifically include characteristic parameters and size parameters of the first object, wherein the characteristic parameters are specifically the friction coefficient between the first object and the inner wall of the human oral cavity; the target constraint conditions are specifically divided into a first constraint condition and a second constraint condition, wherein the first constraint condition represents an inequality constraint on the position of the first object, and the second constraint condition represents an inequality constraint on the friction cone of the first object.

[0146] Specifically, the process of determining the above target constraint condition is: the control device constructs the above first constraint condition according to the above depth coordinates.

[0147] Exemplarily, the expression of the first constraint condition is as follows:

[0148]

[0149] Where E represents the error in the prediction optimization step i, Γ represents the force on the end of the first object, θ represents the state transfer inference matrix, Φ represents the state input inference matrix, x represents the position of the first object, and Y ub Represents the constraints of the above depth coordinates.

[0150] Furthermore, the control device constructs a friction cone constraint of the first object according to the friction coefficient and the size parameter, that is, a second constraint condition.

[0151] Specifically, the schematic diagram of the friction cone of the first object is as follows Figure 7As shown, l represents the above-mentioned size parameter, x represents the horizontal axis of the three-dimensional coordinate, y represents the vertical axis of the three-dimensional coordinate, and z represents the vertical axis of the three-dimensional coordinate.

[0152] Exemplarily, the expression of the second constraint condition constructed according to the above friction coefficient and the above size parameter is as follows:

[0153]

[0154] Among them, T represents the moment, F represents the force applied to the first object, μ represents the above-mentioned friction coefficient, l represents the above-mentioned dimensional parameter, and the subscripts x, y, and z represent the three-dimensional coordinates respectively.

[0155] In this embodiment, the control device can construct an inequality constraint on the position of the first object according to the depth coordinates in the human mouth, that is, the above-mentioned first constraint condition. In this way, it can be ensured that when the robot performs the sampling operation, it will not cause danger to the oral cavity of the sampled person; the control device can construct an inequality constraint on the friction cone of the first object according to the above-mentioned friction coefficient and size information, that is, the above-mentioned second constraint condition. In this way, it can be ensured that when the robot performs the sampling operation, it will not cause discomfort to the sampled person due to excessive force, nor will it cause inaccurate sampling points touched due to the bending of the first object, or inaccurate force control feedback.

[0156] In the above embodiment, the first motion parameter includes a first position and a first speed of the target joint, and the step of controlling the target joint movement according to the first control instruction and the first motion parameter specifically includes: converting the first control instruction into a second control instruction for the target joint; controlling the target joint movement according to the first position, the first speed and the second control instruction.

[0157] In this embodiment, the first motion parameter specifically includes the current position of the target joint, ie, the first position, and the current speed of the target joint, ie, the first speed.

[0158] Specifically, the process of controlling the above-mentioned target joint action is: the control device first converts the above-mentioned first control instruction to determine the second control instruction that can control the target joint action. Specifically, since the above-mentioned first control instruction is a control instruction for the first object, and the first object can only move by being driven by the target joint, the control device cannot directly control the first object action. Therefore, the control device needs to first convert the first control instruction into the second control instruction for the target joint.

[0159] Exemplarily, the method of converting the first control instruction into the second control instruction is determined according to the parameter type contained in the instruction to be converted. For example, if the control instruction is to control the first object to reach a certain position, then the instruction can be multiplied by the Jacobian matrix of the target joint during conversion.

[0160] Furthermore, the control device controls the movement of the target joint according to the second control instruction, the first speed and the first position. Specifically, when the movement of the target joint is controlled by the second control instruction, the control device can perform nonlinear compensation on the movement of the target joint according to the first speed and the first position, thereby ensuring the accuracy of controlling the movement of the target joint.

[0161] In this embodiment, when controlling the target joint movement, the control device converts the first instruction, determines the second control instruction, and comprehensively considers the current position and current speed of the target joint, that is, the above-mentioned first position and the above-mentioned first speed. In this way, the accuracy of the control of the target joint movement is guaranteed, and then the stability of the robot operation is guaranteed.

[0162] In the above embodiment, the step of controlling the target joint movement according to the first position, the first speed and the second control instruction specifically includes: determining the first compensation parameter according to the first position and the first speed; optimizing the second control instruction according to the first compensation parameter, and controlling the target joint movement according to the optimized second control instruction.

[0163] In this embodiment, the process of controlling the target joint action is as follows: the control device first determines a first compensation parameter according to the first position and the first speed. Specifically, the first compensation parameter is a feedforward compensation for the nonlinear term of the second control instruction.

[0164] Furthermore, the control device optimizes the second control instruction according to the first compensation parameter, and controls the movement of the target joint through the optimized second control instruction. Specifically, since some parameters of nonlinear terms are usually not considered in the process of solving the first control instruction through the recursive prediction model of the robot, the second motion parameter and the target motion parameter, when controlling the movement of the target joint, the control device will determine the first compensation parameter as a feedforward compensation for the nonlinear term and compensate it to the second control instruction.

[0165] In this embodiment, before controlling the target joint action, the control device determines the first compensation parameter by the current position and current speed of the target joint, i.e., the first position and the first speed, and performs feedforward compensation of the nonlinear term on the second control instruction by the first compensation parameter. In this way, the nonlinearity in the nonlinear dynamic equation is partially linearized so that it can be applied to the linear optimization method, thereby performing nonlinear optimization on the second control instruction for controlling the target joint action, and ensuring the accuracy of the subsequent control of the target joint action according to the optimized second control instruction.

[0166] In the above embodiment, the first motion parameter includes the first position of the target joint, and the step of updating the recursive prediction model according to the first motion parameter specifically includes:

[0167] The recursive prediction model is updated according to the first position.

[0168] In this embodiment, the first motion parameter of the target joint specifically includes the current position of the target joint, that is, the first position.

[0169] Specifically, the process of updating the recursive prediction model is as follows: the control device updates the recursive prediction model according to the first position. Specifically, according to the process formula for building the recursive prediction model listed in the above embodiment, the position of the target joint is an important parameter in the process of building the recursive prediction model. For example, And J are all obtained through the above-mentioned first position q. Therefore, the control device can update the recursive prediction model of the robot according to the above-mentioned first position.

[0170] In this embodiment, the control device updates the recursive prediction model according to the current position of the target joint, i.e., the above-mentioned first position, so that the updated recursive prediction model can predict the states of the robot and the first object in the future as accurately as possible, and then can derive the optimal first control instruction in the future based on the states of the robot and the first object.

[0171] In the above embodiment, the second motion parameter includes at least one of the second position of the first object, the second speed of the first object, and the first force acting on the first object.

[0172] In this embodiment, the second position represents the current position of the first object, the second speed represents the current speed of the first object, and the first force represents the force currently applied to the first object or the force output by the end of the first object.

[0173] Specifically, the second motion parameter obtained includes one or more of the first force, the second position and the second speed, but is not limited thereto.

[0174] Exemplarily, the second motion parameter may also include the current torque of the first object, characteristic parameters of the first object, etc.

[0175] Embodiment 2:

[0176] Figure 8A schematic block diagram of a control device of a robot according to an embodiment of the present application is shown, the robot includes a target joint, the target joint is used to drive the first object to move, the control device 800 of the robot includes: an acquisition module 802, used to acquire a recursive prediction model of the robot, a first motion parameter of the target joint and a second motion parameter of the first object; a first processing module 804, used to update the recursive prediction model according to the first motion parameter; a second processing module 806, used to determine a first control instruction according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object under a target constraint condition, the first control instruction being a control instruction for the first object; and a third processing module 808, used to control the target joint movement according to the first control instruction and the first motion parameter.

[0177] In this embodiment, the robot represents a robot used to take samples from the human oral cavity, the target joint represents a joint on the machine used to perform sampling work, the first object represents an object driven by the target joint, such as a sampling cotton swab, etc., the recursive prediction model represents a pre-established mathematical model of the robot, the target constraint condition represents the physical constraint condition for the first object and the movement of the robot; the target motion parameter represents the position that the user expects the first object to reach, the expected movement speed of the first object, or the expected force on the first object.

[0178] Specifically, firstly, the mathematical model of the robot established in advance, that is, the recursive prediction model, is obtained through the acquisition module 802; the motion parameters of the target joint, that is, the first motion parameters, are obtained; the motion parameters of the first object, that is, the second motion parameters, are obtained.

[0179] Specifically, the robot's control device 800 can clarify the current state of the above-mentioned target joint according to the above-mentioned first motion parameters, for example, the current position and current speed of the target joint, etc.; according to the above-mentioned second motion parameters, it can clarify the current state of the above-mentioned first object, for example, the current position of the first object, the current speed of the first object and the current force acting on the first object, etc. Therefore, it is necessary to first obtain the above-mentioned recursive prediction model, the above-mentioned first motion parameters and the above-mentioned second motion parameters through the above-mentioned acquisition module 802.

[0180] Furthermore, the first processing module 804 updates the recursive prediction model according to the first motion parameter. Specifically, in the process of the robot sampling the human oral cavity, the configuration of the robot changes from time to time. If the robot's motion is directly controlled according to the robot's own recursive prediction model, the robot's motion will be biased. Therefore, the first processing module 804 needs to first update the recursive prediction model according to the first motion parameter of the target joint.

[0181] Furthermore, the second processing module 806 determines a first control instruction under the target constraint condition according to the target operation parameter, the second motion parameter and the updated recursive prediction model. Specifically, the first control instruction represents a control instruction for the first object.

[0182] Specifically, the objective function of the preset optimization algorithm can be constructed based on the updated recursive prediction model. With the second motion parameters and the target motion parameters as inputs of the objective function, the optimal control law with the smallest deviation between the target motion parameters and the actual motion parameters of the first object under the target constraint conditions can be solved. The second processing module 806 can determine how to control the action of the first object according to the optimal control law, that is, it can determine the first control instruction for the first object.

[0183] Further, the third processing module 808 controls the movement of the target joint according to the first control instruction and the first motion parameter. Specifically, since the first object moves driven by the target joint, the third processing module 808 cannot directly control the movement of the first object. Therefore, the third processing module 808 needs to control the movement of the target joint according to the first control instruction and the parameters of the target joint.

[0184] In the related art, when a sampling robot is controlled by a traditional controller to perform sampling operations, the controller has overshoot and phase delay. In this case, when normal damping PID control instructions are used, the overshoot will cause the first object and the human mouth to be unable to achieve comfortable contact. Using high damping PID control instructions will cause the robot to respond slowly due to phase delay, resulting in extended sampling time.

[0185] Therefore, in the above-mentioned embodiment of the present application, the second processing module 806 adopts a recursive prediction model updated according to the first motion parameter in the process of solving the first control instruction. In this way, the overshoot and phase delay of the control instruction are reduced, the response speed of the robot is improved, and the efficiency of the sampling operation is improved; at the same time, by limiting the above-mentioned target constraint conditions, physical constraints exist in the action process of the first object, thereby ensuring the comfort of the sampled person when the robot performs sampling operations on the human mouth, that is, the control device 800 of the robot proposed in the above-mentioned embodiment of the present application is adopted to achieve both sampling operation efficiency and sampling comfort, thereby solving the problem in the related art that the sampling operation efficiency is low and the comfort of the sampled person cannot be guaranteed due to the overshoot and phase delay of the traditional controller.

[0186] In the above embodiment, the second processing module 806 is specifically used to determine the objective function of the quadratic optimization algorithm according to the updated recursive prediction model; using the second motion parameter and the target motion parameter as input, solving the objective function under the target constraint condition, and determining the first control instruction.

[0187] In this embodiment, the second processing module 806 can determine the objective function of the optimization algorithm used, that is, the objective function of the quadratic optimization algorithm, according to the updated recursive prediction model, and can solve the determined objective function according to the second motion parameter of the first object and the target motion parameter under the target constraint condition to determine the above-mentioned first control instruction. In this way, it is ensured that the subsequent third processing module 808 can take into account the requirements of sampling efficiency and comfort when controlling the target joint action according to the first control instruction, that is, when controlling the robot to perform sampling operation.

[0188] In the above embodiment, the robot is used to sample the human oral cavity, and the acquisition module 802 is also used to obtain the depth coordinates of the human oral cavity and the basic parameters of the first object; the second processing module 806 is also used to construct target constraints according to the depth coordinates and the basic parameters.

[0189] In this embodiment, before solving the first control instruction, the second processing module 806 can determine the target constraint condition for solving the first control instruction according to the acquired depth coordinates in the human oral cavity and the basic information of the first object. In this way, the target constraint condition corresponds to the internal conditions of the human oral cavity and the conditions of the first object, ensuring the accuracy of the first control instruction solved according to the target constraint condition in the subsequent steps.

[0190] In the above embodiment, the basic parameters include the friction coefficient between the first object and the inner wall of the human oral cavity and the size parameters of the first object, the target constraint conditions include the first constraint conditions and the second constraint conditions, and the second processing module 806 is specifically used to determine the first constraint conditions based on the depth coordinates; and determine the second constraint conditions based on the friction coefficient and the size parameters.

[0191] In this embodiment, the second processing module 806 can construct an inequality constraint on the position of the first object according to the depth coordinates in the human mouth, that is, the above-mentioned first constraint condition. In this way, it can be ensured that when the robot performs the sampling operation, it will not cause danger to the oral cavity of the sampled person; the second processing module 806 can construct an inequality constraint on the friction cone of the first object according to the above-mentioned friction coefficient and size information, that is, the above-mentioned second constraint condition. In this way, it can be ensured that when the robot performs the sampling operation, it will not cause discomfort to the sampled person due to excessive force, nor will it cause inaccurate sampling points touched due to the bending of the first object, or inaccurate force control feedback.

[0192] In the above embodiment, the first motion parameter includes the first position and the first speed of the target joint, and the third processing module 808 is specifically used to convert the first control instruction into a second control instruction for the target joint; and control the target joint movement according to the first position, the first speed and the second control instruction.

[0193] In this embodiment, when controlling the target joint movement, the third processing module 808 converts the first instruction, determines the second control instruction, and comprehensively considers the current position and current speed of the target joint, that is, the above-mentioned first position and the above-mentioned first speed. In this way, the accuracy of the control of the target joint movement is guaranteed, and then the stability of the robot operation is guaranteed.

[0194] In the above embodiment, the third processing module 808 is specifically used to determine the first compensation parameter according to the first position and the first speed; optimize the second control instruction according to the first compensation parameter, and control the target joint action according to the optimized second control instruction.

[0195] In this embodiment, before controlling the target joint action, the third processing module 808 determines the first compensation parameter by the current position and current speed of the target joint, i.e., the first position and the first speed, and performs feedforward compensation of the nonlinear term on the second control instruction by the first compensation parameter. In this way, the nonlinearity in the nonlinear dynamic equation is partially linearized so that it can be applied to the linear optimization method, thereby performing nonlinear optimization on the second control instruction for controlling the target joint action, and ensuring the accuracy of the subsequent control of the target joint action according to the optimized second control instruction.

[0196] In the above embodiment, the first motion parameter includes a first position of the target joint, and the first processing module 804 is specifically configured to update the recursive prediction model according to the first position.

[0197] In this embodiment, the first processing module 804 updates the recursive prediction model according to the current position of the target joint, i.e., the above-mentioned first position, so that the updated recursive prediction model can predict the states of the robot and the first object in the future as accurately as possible, and then derive the optimal first control instruction in the future based on the states of the robot and the first object.

[0198] In the above embodiment, the second motion parameter includes at least one of the second position of the first object, the second speed of the first object, and the first force acting on the first object.

[0199] Embodiment three:

[0200] Fig. 9A schematic block diagram of a control device of a robot according to an embodiment of the present application is shown, wherein the control device 900 of the robot includes: a memory 902, in which programs or instructions are stored; a processor 904, which executes the programs or instructions stored in the memory 902 to implement the steps of the control method of the robot proposed in the above-mentioned embodiment of the present application, and thus has all the beneficial technical effects of the control method of the robot proposed in the above-mentioned embodiment of the present application, which will not be elaborated herein.

[0201] Embodiment 4:

[0202] According to the fourth embodiment of the present application, a readable storage medium is provided, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the control method of the robot proposed in the above embodiment of the present application is implemented. Therefore, the readable storage medium has all the beneficial effects of the control method of the robot proposed in the above embodiment of the present application, which will not be repeated here.

[0203] Embodiment five:

[0204] According to the fifth embodiment of the present application, an electronic device is proposed, including a control device of a robot as proposed in the above-mentioned embodiment of the present application, and / or a readable storage medium as proposed in the above-mentioned embodiment of the present application. Therefore, the electronic device has all the beneficial effects of the control device of the robot proposed in the above-mentioned embodiment of the present application and / or the readable storage medium proposed in the above-mentioned embodiment of the present application, which will not be repeated here.

[0205] Embodiment six:

[0206] According to the sixth embodiment of the present application, a computer program product is proposed, which includes a computer program, and when the computer program is executed by a processor, the control method of the robot proposed in the above embodiment of the present application is implemented. Therefore, the computer program product has all the beneficial effects of the control method of the robot proposed in the above embodiment of the present application, which will not be repeated here.

[0207] Embodiment seven:

[0208] like Fig.10 As shown, it is a control block diagram of the robot control method proposed in this application. Fig.10 The control process of the robot control method proposed in the present application is exemplarily described.

[0209] like Fig.10 As shown, in the process of controlling the robot, the control device will obtain the first position and the first speed of the target joint of the robot in real time; the control device will obtain the second position, the second speed and the first force and other parameters of the first object in real time according to the forward kinematics, and feed back the above parameters to the quadratic optimization online solver.

[0210] Specifically, the recursive prediction model updater can update the recursive prediction model of the robot according to the above-mentioned first position.

[0211] Specifically, the quadratic optimization online solver can perform quadratic optimization solution according to the updated recursive prediction model, the target motion parameters, the second position, the second speed and the first force, and determine the first control instruction for the first object.

[0212] Specifically, after determining the first control instruction, the control device may convert the first instruction into a second control instruction for the target joint through a workspace instruction to joint space instruction converter.

[0213] Specifically, the control device can optimize the second control instruction according to the first compensation parameter based on the feedforward compensation of the first position and the first speed nonlinear term, that is, the first compensation parameter, and then control the target joint action of the nucleic acid robot according to the optimized second control instruction.

[0214] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance unless otherwise clearly specified and limited; the terms "connection", "installation", "fixation" and the like should be understood in a broad sense, for example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0215] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0216] In addition, the embodiments of the present application may be combined with each other, but this must be based on the fact that ordinary technicians in the field can implement it. When the combination of the embodiments is contradictory or cannot be implemented, it should be deemed that such combination of embodiments does not exist and is not within the scope of protection required by the present application.

[0217] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A robot control method, It is characterized in that The robot includes a target joint, and the target joint is used to drive the first object to move. The control method includes: Acquire a recursive prediction model of the robot, a first motion parameter of the target joint, and a second motion parameter of the first object; updating the recursive prediction model according to the first motion parameter; Under the target constraint condition, determining a first control instruction according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object, wherein the first control instruction is a control instruction for the first object; Controlling the target joint action according to the first control instruction and the first motion parameter; The determining the first control instruction according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object specifically includes: Determine the objective function of the quadratic optimization algorithm according to the updated recursive prediction model; Taking the second motion parameter and the target motion parameter as input, solving the target function under the target constraint condition to determine the first control instruction; The robot is used to sample a human oral cavity. Before determining a first control instruction according to the updated recursive prediction model, the second motion parameter, and the target motion parameter of the first object, the control method further includes: Acquire the depth coordinates of the human oral cavity and basic parameters of the first object; The target constraint condition is constructed according to the depth coordinate and the basic parameters.

2. The control method according to claim 1, It is characterized in that The basic parameters include the friction coefficient between the first object and the inner wall of the human oral cavity and the size parameters of the first object, the target constraint condition includes a first constraint condition and a second constraint condition, and constructing the target constraint condition according to the depth coordinate and the basic parameters specifically includes: determining the first constraint condition according to the depth coordinate; The second constraint condition is determined according to the friction coefficient and the size parameter.

3. The control method according to claim 1 or 2, It is characterized in that The first motion parameter includes a first position and a first speed of the target joint, and controlling the target joint action according to the first control instruction and the first motion parameter specifically includes: converting the first control instruction into a second control instruction for the target joint; The target joint action is controlled according to the first position, the first speed and the second control instruction.

4. The control method according to claim 3, It is characterized in that The controlling the target joint action according to the first position, the first speed and the second control instruction specifically includes: determining a first compensation parameter according to the first position and the first speed; The second control instruction is optimized according to the first compensation parameter, and the target joint action is controlled according to the optimized second control instruction.

5. The control method according to claim 1 or 2, It is characterized in that The first motion parameter includes a first position of the target joint, and updating the recursive prediction model according to the first motion parameter specifically includes: The recursive prediction model is updated according to the first position.

6. The control method according to claim 1 or 2, It is characterized in that The second motion parameter includes at least one of a second position of the first object, a second speed of the first object, and a first force acting on the first object.

7. A robot control device, It is characterized in that The robot includes a target joint, and the target joint is used to drive the first object to move. The control device includes: An acquisition module, used for acquiring a recursive prediction model of the robot, a first motion parameter of the target joint, and a second motion parameter of the first object; The robot is used to sample the human oral cavity, and the acquisition module is also used to acquire the depth coordinates of the human oral cavity and the basic parameters of the first object; A first processing module, configured to update the recursive prediction model according to the first motion parameter; A second processing module is used to determine a first control instruction under a target constraint condition according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object, wherein the first control instruction is a control instruction for the first object; The second processing module is further used to construct the target constraint condition according to the depth coordinate and the basic parameter; A third processing module, configured to control the target joint motion according to the first control instruction and the first motion parameter; The determining the first control instruction according to the updated recursive prediction model, the second motion parameter and the target motion parameter of the first object specifically includes: Determine the objective function of the quadratic optimization algorithm according to the updated recursive prediction model; The second motion parameter and the target motion parameter are used as input, the target function is solved under the target constraint condition, and the first control instruction is determined.

8. A robot control device, It is characterized in that include: A memory and a processor, wherein the memory stores a program, and when the processor executes the program, the steps of the robot control method according to any one of claims 1 to 6 are implemented.

9. A readable storage medium, It is characterized in that The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of the robot control method according to any one of claims 1 to 6 are implemented.

10. An electronic device, It is characterized in that include: A robot control device as claimed in claim 7 or 8; and / or The readable storage medium as claimed in claim 9.

11. A computer program product, It is characterized in that The invention comprises a computer program, which implements the steps of the robot control method according to any one of claims 1 to 6 when the computer program is executed by a processor.

Images