Joint control method, device, controller, storage medium and program product

Abstract

Embodiments of the present application disclose a joint control method, device, controller, storage medium and program product. The method comprises: obtaining control data associated with a rotation increment of a target joint to be controlled in a master operating hand of a robot; obtaining an influence parameter based on the control data and a fuzzy controller, wherein the influence parameter is used to represent the influence of the control data on the rotation increment; obtaining the rotation increment based on the influence parameter, and controlling the target joint to rotate based on the rotation increment. The technical solution of the embodiments of the present application can improve the operation fluency of the master operating hand of the robot.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of motion control, and more particularly to a joint control method, device, controller, storage medium, and program product. Background Technology

[0002] The main manipulator of a medical robot is the carrier through which doctors operate the robot. The 7-DOF main manipulator with redundant joints is flexible and can reduce the burden on doctors during surgery.

[0003] However, during the doctor's operation, when dealing with joints in the same direction of rotation, if the upper part of the joint has not reached its rotation limit, it is impossible to control the rotation of the lower part of the joint, resulting in low operation smoothness, which urgently needs to be solved. Summary of the Invention

[0004] This invention provides a joint control method, device, controller, storage medium, and program product to improve the operational smoothness of a robot's main operator.

[0005] According to one aspect of the present invention, a joint control method is provided, which may include:

[0006] For the target joint to be controlled in the robot's main operator, acquire control data associated with the rotation increment of the target joint;

[0007] Based on the control data and the fuzzy controller, the influence parameters are obtained, which are used to characterize the influence of the control data on the rotation increment.

[0008] The rotation increment is obtained based on the influencing parameters, and the target joint is controlled to rotate based on the rotation increment.

[0009] According to another aspect of the present invention, a joint control device is provided, which may include:

[0010] The control data acquisition module is used to acquire control data associated with the rotation increment of the target joint in the robot's main manipulator.

[0011] The influence parameter acquisition module is used to obtain influence parameters based on control data and fuzzy controller, wherein the influence parameters are used to characterize the influence of control data on rotation increment;

[0012] The target joint rotation module is used to obtain the rotation increment based on the influence parameters, and to control the target joint to rotate based on the rotation increment.

[0013] According to another aspect of the present invention, a controller is provided, configured in the master operator hand of a robot, and may include:

[0014] At least one processor; and

[0015] A memory that is communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, such that when the at least one processor executes the program, it implements the joint control method provided in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided having computer instructions stored thereon for causing a processor to execute and implement the joint control method provided in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising: a computer program that, when executed by a processor, implements any joint control method provided in any embodiment of the present invention.

[0019] The technical solution in this embodiment of the invention acquires control data associated with the rotation increment of the target joint to be controlled by the robot's main operator, and controls the target joint based on the control data. Based on the control data and a fuzzy controller, influence parameters are obtained, whereby the influence parameters characterize the impact of the control data on the rotation increment. These influence parameters can be obtained by designing control rules based on expert experience and knowledge using a fuzzy controller, resulting in suitable influence parameters for the target joint. The rotation increment is obtained based on the influence parameters, and the target joint is controlled to rotate based on this increment, thus achieving control of the target joint. This technical solution, by processing the control data through a fuzzy controller to control the rotation of the target joint, can improve the operational smoothness of the robot's main operator.

[0020] It should be understood that the description in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a joint control method provided according to an embodiment of the present invention;

[0023] Figure 2This is a schematic diagram of a 7-DOF master manipulator according to an embodiment of the present invention;

[0024] Figure 3 This is a flowchart of another joint control method provided according to an embodiment of the present invention;

[0025] Figure 4 This is a fuzzy calculation flowchart of a specific example of another joint control method provided according to an embodiment of the present invention;

[0026] Figure 5 This is a flowchart illustrating parameter calculation in a specific example of another joint control method provided according to an embodiment of the present invention.

[0027] Figure 6 This is a fuzzy distribution diagram of a specific example of another joint control method provided according to an embodiment of the present invention;

[0028] Figure 7 This is a fuzzy logic reasoning diagram of a specific example of another joint control method provided according to an embodiment of the present invention.

[0029] Figure 8 This is a schematic diagram of the surface averaging method, which is a specific example of another joint control method provided according to an embodiment of the present invention.

[0030] Figure 9 This is another fuzzy distribution schematic diagram of a specific example of another joint control method provided according to an embodiment of the present invention;

[0031] Figure 10 This is yet another fuzzy distribution schematic diagram of a specific example of another joint control method provided in an embodiment of the present invention;

[0032] Figure 11 This is another fuzzy distribution schematic diagram of a specific example of another joint control method provided according to an embodiment of the present invention;

[0033] Figure 12 This is a structural block diagram of a joint control device according to an embodiment of the present invention;

[0034] Figure 13 This is a schematic diagram of the structure of an electronic device that implements the joint control method of this invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The same applies to "target," "original," etc., and will not be repeated here. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] Figure 1 This is a flowchart of a joint control method provided in an embodiment of the present invention. This embodiment is applicable to joint control situations, especially to joint control of the joints in the master operator of a medical robot. The method can be executed by the joint control device provided in this embodiment of the invention, which can be implemented by software and / or hardware. The device can be integrated into an electronic device, which can be various user terminals or servers.

[0038] See Figure 1 The method of this invention specifically includes the following steps:

[0039] S110. For the target joint to be controlled in the robot's main operator, acquire control data associated with the rotation increment of the target joint.

[0040] Here, the target joint can be understood as the joint in the robot's master manipulator that requires attitude redundancy control. Optionally, the robot can be a medical robot, and the master manipulator can be a 7-DOF master manipulator with a 4-DOF attitude mechanism and a 3-DOF position mechanism, such as... Figure 2As shown. In a 7-DOF master manipulator, the position and posture of the master manipulator can be changed by dragging the 7th joint. The movement of the 5th, 6th and 7th joints can be directly controlled, but the 4th joint cannot be directly controlled and requires posture redundancy control to achieve the following and avoidance of the 6th joint by the 4th joint.

[0041] For the target joint to be controlled by the robot's main manipulator, it is necessary to acquire control data associated with the rotation increment of the target joint, such as the rotation angle and rotation speed of the associated joint, in order to achieve control of the target joint.

[0042] S120. Based on the control data and the fuzzy controller, the influence parameters are obtained, whereby the influence parameters are used to characterize the influence of the control data on the rotation increment.

[0043] In this process, influence parameters characterizing the effect of control data on rotational increments can be obtained based on control data and a fuzzy controller. For example, control data can be input into a fuzzy controller, and the fuzzy logic within the controller can be used to process the control data to obtain the influence parameters.

[0044] S130. Obtain the rotation increment based on the influence parameters, and control the target joint to rotate based on the rotation increment.

[0045] After obtaining the influencing parameters, the required rotation increment for the target joint can be calculated based on these parameters, and the target joint can be controlled to rotate based on this rotation increment. Optionally, controlling the target joint to rotate based on the rotation increment includes: determining the current joint angle of the target joint; adding the rotation increment to the current joint angle to obtain the target rotation angle of the target joint; and controlling the target joint to rotate towards the target rotation angle. In other words, by adding the rotation increment to the current joint angle of the target joint to obtain the target rotation angle to which the target joint needs to rotate, and controlling the target joint to rotate towards the target rotation angle, control of the target joint can be achieved through the specific target rotation angle.

[0046] The technical solution in this embodiment of the invention acquires control data associated with the rotation increment of the target joint to be controlled by the robot's main operator, and controls the target joint based on the control data. Based on the control data and a fuzzy controller, influence parameters are obtained, whereby the influence parameters characterize the impact of the control data on the rotation increment. These influence parameters can be obtained by designing control rules based on expert experience and knowledge using a fuzzy controller, resulting in suitable influence parameters for the target joint. The rotation increment is obtained based on the influence parameters, and the target joint is controlled to rotate based on this increment, thus achieving control of the target joint. This technical solution, by processing the control data through a fuzzy controller to control the rotation of the target joint, can improve the operational smoothness of the robot's main operator.

[0047] An optional technical solution, based on control data and a fuzzy controller, obtains influencing parameters, including: determining the input variables of the fuzzy controller based on the control data; fuzzifying the input variables to obtain fuzzy subsets, constructing a fuzzy distribution map based on the fuzzy subsets, and establishing fuzzy rules based on the input variables; constructing a control rule adjustment table based on the fuzzy subsets and fuzzy rules; determining the variable values ​​of the input variables based on the control data; obtaining a fuzzy output based on the variable values, the fuzzy distribution map, and the control rule adjustment table; and defuzzifying the fuzzy output to obtain the influencing parameters.

[0048] In this process, all or part of the control data can be selected as input variables for the fuzzy controller. Then, the input variables can be divided into multiple fuzzy subsets and fuzzified to obtain fuzzy subsets. For example, five fuzzy subsets can be defined: Minimal (VS), Medium-Small (MS), Medium (MM), Medium-Large (MB), and Maximum (VB). A fuzzy distribution map is then constructed based on these subsets. Optionally, a triangular membership function can be used to construct the fuzzy distribution map, or fuzzy rules can be manually established based on the input variables and expert experience. Further, a control rule adjustment table can be constructed based on the fuzzy subsets and fuzzy rules to represent all possible control rules. Then, the values ​​of the input variables are determined based on the control data. Based on the variable values, the fuzzy distribution map, and the control rule adjustment table, the fuzzy output is obtained. Finally, the fuzzy output is sharpened to obtain the influencing parameters. Optionally, a surface averaging method can be used to sharpen the obtained fuzzy output, ultimately yielding the influencing parameters.

[0049] The above technical solution, by processing the control data through a fuzzy controller, can obtain influence parameters that conform to the control rules set by the user.

[0050] Based on this, optionally, fuzzy output can be obtained based on variable values, fuzzy distribution maps, and control rule adjustment tables, including: determining the membership degree corresponding to the variable values ​​based on variable values ​​and fuzzy distribution maps, and determining the control rules corresponding to the input variables based on variable values ​​and control rule adjustment tables; and obtaining fuzzy output based on membership degrees and control rules.

[0051] Based on the variable values, the membership degree and control rule corresponding to each variable value can be obtained from the fuzzy distribution diagram. Based on the membership degree and control rule, the fuzzy output can be calculated.

[0052] Figure 3 This is a flowchart of another joint control method provided in this embodiment of the invention. This embodiment is an optimization based on the above-described technical solutions. In this embodiment, optionally, there are multiple fuzzy controllers. Based on the control data and the fuzzy controllers, the influence parameters are obtained, including: dividing all control data into multiple data groups, wherein the number of data groups is the same as the number of fuzzy controllers; for each data group, inputting the data group into the fuzzy controller corresponding to the data group among the multiple fuzzy controllers to obtain the influence parameters. The explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here.

[0053] See Figure 3 The method in this embodiment may specifically include the following steps:

[0054] S210. For the target joint to be controlled in the robot's main operator, acquire control data associated with the rotation increment of the target joint.

[0055] S220. Divide all control data into multiple data groups, wherein the number of data groups is the same as the number of multiple fuzzy controllers.

[0056] As the number of inputs to a fuzzy controller increases, the rules become more complex, leading to a greater computation time. Therefore, to improve computational efficiency and accuracy, multiple fuzzy controllers can be used, and all control data can be divided into multiple data groups equal to the number of fuzzy controllers for synchronized processing.

[0057] S230. For each data group in the multiple data groups, input the data group into the fuzzy controller corresponding to the data group in the multiple fuzzy controllers to obtain the influence parameter, wherein the influence parameter is used to characterize the influence of the control data on the rotation increment.

[0058] In this approach, for each of the multiple data groups, the data group can be input into the corresponding fuzzy controller among multiple fuzzy controllers. One fuzzy controller processes a set of corresponding data, thereby reducing the complexity of processing and improving processing efficiency.

[0059] S240. Obtain the rotation increment based on the influence parameters, and control the target joint to rotate based on the rotation increment.

[0060] The technical solution of this invention utilizes multiple fuzzy controllers to process grouped control data in parallel, which can effectively reduce the complexity of control rules and improve calculation speed.

[0061] An optional technical solution includes proportional parameters as all influencing parameters. The proportional parameters are obtained based on proportional data groups from multiple data groups. The control data in the proportional data groups are data associated with at least one of the target joint, the first joint, and the second joint. The first joint is the joint in the master operator that is located before the target joint, and the second joint is the joint in the master operator that is located before the first joint.

[0062] Among all the influencing parameters is the proportional parameter, which is obtained based on the proportional data group in multiple data groups. The control data in the proportional data group is data associated with at least one of the first joint before the target joint and the second joint before the first joint, such as the speed and angle data of the first and second joints.

[0063] The above technical solution, based on data associated with the joints preceding the target joint, obtains proportional parameters, which can associate the control of the target joint with the joints preceding the target joint, thereby improving the smoothness of the operator's operation.

[0064] Based on this, an optional technical solution is provided, wherein the proportional parameters include a first proportional parameter, a second proportional parameter, and a third proportional parameter; the proportional data set includes a first data set corresponding to the first proportional parameter, a second data set corresponding to the second proportional parameter, and a third data set corresponding to the third proportional parameter; the rotation increment is applied in the current control cycle; the control data in the first data set includes the first joint angle of the first joint and the second joint angle of the second joint; the control data in the second data set includes the joint rotation speed of the first joint and the historical total proportional parameter calculated based on all historical proportional parameters applied in the previous control cycle; and the control data in the third data set includes the current joint angle of the target joint.

[0065] The above technical solution can calculate different proportional parameters using data from different data groups. This reduces the complexity of fuzzy computing and improves its speed. It also allows multiple proportional parameters to fully reflect the impact of data from different data groups on the rotation increment.

[0066] Based on any of the above embodiments, an optional technical solution includes step size parameters as all influencing parameters. The step size parameters are obtained based on step size data groups from multiple data groups. The control data in the step size data groups include the joint rotation speed of the second joint and the historical increment, wherein the historical increment is the rotation increment applied by the target joint in the previous control cycle.

[0067] The above technical solution uses step size parameters to characterize the impact of data in the supplementary data set on the rotation increment, which can further improve the operational smoothness of the robot's main operator.

[0068] Based on this, optionally, when the influencing parameters include a first proportional parameter, a second proportional parameter, and a third proportional parameter, the rotation increment is obtained based on the influencing parameters, including:

[0069] Multiply the first proportional parameter, the second proportional parameter, and the third proportional parameter to obtain the current total proportional parameter for the current control cycle; multiply the current total proportional parameter and the step size parameter to obtain the rotation increment.

[0070] The total proportional parameter can be obtained by multiplying the multiple proportional parameters together, and then multiplied by the step size parameter to obtain the rotation increment.

[0071] The above technical solution, by calculating the obtained proportional parameters and step size parameters, can comprehensively consider the impact of all control data on the rotation increment, thereby improving the smoothness of the robot's main operator's operation.

[0072] To better understand the various technical solutions described above, a specific example is provided below. In this specific example, the structural diagram of the main operator is shown below. Figure 2 As shown, the target joint has 4 joints, the first joint has 5 joints, and the second joint has 6 joints. The specific flowchart is as follows. Figure 4 As shown, the specific steps are as follows:

[0073] Step 1: Acquire control data and determine the structure of the fuzzy controller.

[0074] The rotational increment Δθ4 of joint 4 is directly affected by the angles θ4, θ5, and θ6 of joints 4, 5, and 6, as well as the movement velocities ν5 and ν6 of joints 5 and 6. To ensure the smoothness of joint 4 rotation, the calculated Δθ4 in each control cycle should be as smooth as possible. Δθ4 is also influenced by the rotational increment of joint 4 calculated in the previous cycle. The historical total proportion parameter k obtained from the previous period calculation t-1 The impact of this. Therefore, it is necessary to obtain the aforementioned control data.

[0075] Step 2: Control the data grouping for fuzzy computation

[0076] The control data are divided into (θ5, θ6) and (ν5, k). t-1 ), The four groups (θ1, θ2, and θ4) are used to input the data of each group into the corresponding fuzzy controller for fuzzy computation. The specific flowchart is as follows: Figure 5 As shown.

[0077] 2.1 Calculation of proportional parameter k1

[0078] 2.1.1 Define the fuzzy distribution of input and output quantities. The distance |θ5-90| between θ5 and 90°, and the distance |θ6-θ6| between θ6 and the center point of the 6th joint motion space are defined. 6，0 The proportionality coefficient k1 is divided into five fuzzy subsets: minimal (VS), medium-small (MS), medium (MM), medium-large (MB), and maximal (VB). A fuzzy distribution is constructed using triangular membership functions, and the fuzzy distribution diagram is shown below. Figure 6 As shown;

[0079] 2.1.2 Establishing fuzzy rules based on input variables. The farther the current angle θ6 of joint 6 is from the center point, i.e., |θ6-θ 6，0 The larger the |θ5 is, the larger k1 is; the farther the current angle θ5 of joint 5 is from 90°, i.e., the larger |θ5-90| is, the larger k1 is; the control rule adjustment table is drawn based on fuzzy rules and fuzzy subsets, as shown in Table 1 below:

[0080] Table 1

[0081]

[0082] 2.1.3 Fuzzy Logic Reasoning. Substituting the values ​​of θ5 and θ6, we get |θ5-90| and |θ6-θ 6，0 The value of | is as follows Figure 7 As shown in the left figure, according to Table 1, four rules are activated: R2 = VS(2), R3 = MS(3), R7 = MS(7), and R8 = MM(8). The corresponding membership degrees are:

[0083] MS(|θ5-90|)=i1

[0084] MM(|θ5-90|)=i2

[0085] VS(|θ6-θ 6，0 |)=i3

[0086] MS(|θ6-θ 6，0 |)=i4

[0087] According to the formula To calculate the fuzzy value, that is:

[0088]

[0089]

[0090]

[0091]

[0092] U = U2 + U3 + U7 + U8

[0093] in, This indicates that after taking the minimum value among membership degrees i1 and i3, a synthesis operation is performed on the corresponding fuzzy rule VS(2). The resulting fuzzy value is as follows: Figure 7 As shown in the right figure.

[0094] 2.1.4. Sharpen the output blur value. For example... Figure 8 As shown, the surface averaging method is used to sharpen the output blurry value, and the scaling factor k1 is finally obtained.

[0095] 2.2 Calculation of proportional parameter k2

[0096] 2.2.1 Define the fuzzy distribution of input and output quantities. The motion velocities of the 5 joints, ν5 and k... t-1 The proportionality coefficient k2 is fuzzily divided into five fuzzy subsets: positive large PB, positive medium PM, positive small PS, zero O, negative small NS, negative medium NM, and negative large NB. A fuzzy distribution is constructed using triangular membership functions, and the fuzzy distribution diagram is shown below. Figure 9 As shown;

[0097] 2.2.2 Establishing fuzzy rules based on input variables. The larger ν5 is, the larger k2 is; k t-1 The larger the value, the larger k2 becomes; the control rule adjustment table is drawn based on fuzzy rules and fuzzy subsets, as shown in Table 2 below:

[0098] Table 2

[0099]

[0100] 2.2.3 Fuzzy logic reasoning. The specific process is the same as step 2.1.3.

[0101] 2.2.4. Sharpening the output blurry value. The surface averaging method is used to sharpen the output blurry value, and the scaling factor k2 is finally obtained.

[0102] 2.3 Calculation of proportional parameter k3

[0103] 2.3.1 Define the fuzzy distribution of input and output quantities. The distance |θ4 - θ_4| between θ_4 and the center point of the 4-joint motion space is defined. 4，0The fuzzy distribution is divided into five subsets based on the proportionality coefficient k3: minimum (VS), medium-small (MS), medium (MM), medium-large (MB), and maximum (VB). A triangular membership function is used to construct the fuzzy distribution, as shown in the figure. Figure 10 As shown;

[0104] 2.3.2 Establishing fuzzy rules based on input variables. |θ4-θ 4，0 The larger the value, the larger k3 becomes; the control rule adjustment table is drawn based on fuzzy rules and fuzzy subsets, as shown in Table 3 below:

[0105] Table 3

[0106]

[0107] 2.3.3 Fuzzy logic reasoning. The specific process is the same as step 2.1.3.

[0108] 2.3.4. Sharpening the output blurry value. The surface averaging method is used to sharpen the output blurry value, and the scaling factor k3 is finally obtained.

[0109] 2.4 Calculation of step size parameter L

[0110] 2.4.1 Define the fuzzy distribution of input and output quantities. This involves combining the 6-joint motion velocity ν6 with the 4-joint position increment command calculated in the previous cycle. The step length L of the 4-joint motion is fuzzily divided into seven fuzzy subsets: positive large PB, positive middle PM, positive small PS, zero O, negative small NS, negative middle NM, and negative large NB. A fuzzy distribution is constructed using triangular membership functions, and the fuzzy distribution diagram is shown below. Figure 11 As shown;

[0111] 2.4.2 Establishing fuzzy rules based on input variables. The larger ν6 is, the larger L is; The larger L is, the larger L is; the control rule adjustment table is drawn based on fuzzy rules and fuzzy subsets, as shown in Table 4 below:

[0112] Table 4

[0113]

[0114] 2.4.3 Fuzzy logic reasoning. The specific process is the same as step 2.1.3.

[0115] 2.4.4. Sharpening the Output Blurred Values. The surface averaging method is used to sharpen the output blurred values, ultimately obtaining the scaling factor L.

[0116] Step 3: Calculate the rotation increment and control the rotation.

[0117] According to the formula Δθ4=k t*L=k1*k2*k3*L, calculate the rotation increment Δθ4 of joint 4 in the current control cycle. Further, based on the position θ'4 of joint 4 in the previous control cycle, obtain θ4=θ'4+Δθ4 as the position command for joint 4 in the current control cycle, controlling joint 4 to rotate towards angle θ4.

[0118] This specific example demonstrates how a fuzzy controller processes control data to control the rotation of the target joint, thereby improving the smoothness of the robot's main operator's operation.

[0119] Figure 12 This is a structural block diagram of a joint control device provided in an embodiment of the present invention. This device is used to execute the joint control method provided in any of the above embodiments. This device and the joint control methods of the above embodiments belong to the same inventive concept. Details not described in detail in the embodiments of the joint control device can be found in the embodiments of the above joint control methods. See also... Figure 12 Specifically, the device may include: a control data acquisition module 310, an influence parameter acquisition module 320, and a target joint rotation module 330.

[0120] Among them, the control data acquisition module 310 is used to acquire control data associated with the rotation increment of the target joint for the target joint to be controlled in the main manipulator of the robot.

[0121] The influence parameter acquisition module 320 is used to obtain influence parameters based on control data and fuzzy controller, wherein the influence parameters are used to characterize the influence of control data on rotation increment;

[0122] The target joint rotation module 330 is used to obtain the rotation increment based on the influence parameters and control the target joint to rotate based on the rotation increment.

[0123] An optional configuration includes multiple fuzzy controllers and an influence parameter acquisition module 320, comprising:

[0124] The data group partitioning submodule is used to divide all control data into multiple data groups, where the number of data groups is the same as the number of fuzzy controllers.

[0125] The influence parameter acquisition module is used to obtain influence parameters for each of multiple data groups by inputting the data group into the corresponding fuzzy controller among multiple fuzzy controllers.

[0126] Based on this, optionally, all influencing parameters include proportional parameters, which are obtained based on proportional data groups from multiple data groups. The control data in the proportional data groups are data associated with at least one of the target joint, the first joint, and the second joint, wherein the first joint is the joint in the master operator that is located before the target joint, and the second joint is the joint in the master operator that is located before the first joint.

[0127] Based on this, optionally, the proportional parameters include a first proportional parameter, a second proportional parameter, and a third proportional parameter, and the proportional data group includes a first data group corresponding to the first proportional parameter, a second data group corresponding to the second proportional parameter, and a third data group corresponding to the third proportional parameter, and the rotation increment is applied in the current control cycle;

[0128] The control data in the first data group includes the first joint angle of the first joint and the second joint angle of the second joint.

[0129] The control data in the second data set includes the joint rotation speed of the first joint and the historical total proportional parameter calculated based on all historical proportional parameters applied in the previous control cycle of the current control cycle.

[0130] The control data in the third data group includes the current joint angle of the target joint.

[0131] Based on any of the above devices, optionally, all influencing parameters include step size parameters, which are obtained based on step size data groups from multiple data groups. The control data in the step size data groups include the joint rotation speed of the second joint and the historical increment, where the historical increment is the rotation increment applied by the target joint in the previous control cycle.

[0132] Based on this, optionally, when the influencing parameters include a first proportional parameter, a second proportional parameter, and a third proportional parameter, the target joint rotation module 330 includes:

[0133] The current total proportional parameter acquisition module is used to multiply the first proportional parameter, the second proportional parameter, and the third proportional parameter to obtain the current total proportional parameter for the current control cycle;

[0134] The rotation increment module is used to multiply the current total proportional parameter and the step size parameter to obtain the rotation increment.

[0135] Another optional parameter acquisition module 320 includes:

[0136] The input variable determination submodule is used to determine the input variables of the fuzzy controller based on the control data;

[0137] The fuzzy rule establishment submodule is used to fuzzify the input variables to obtain fuzzy subsets, construct fuzzy distribution maps based on the fuzzy subsets, and establish fuzzy rules based on the input variables.

[0138] The control rule adjustment table construction submodule is used to construct a control rule adjustment table based on fuzzy subsets and fuzzy rules.

[0139] The variable value-driven submodule is used to determine the value of the input variable based on the control data;

[0140] The fuzzy output submodule is used to obtain fuzzy output based on variable values, fuzzy distribution plots, and control rule adjustment tables;

[0141] The submodule for obtaining influence parameters is used to sharpen the fuzzy output and obtain the influence parameters.

[0142] Based on this, optional fuzzy output yields submodules, including:

[0143] The control rule determination unit is used to determine the membership degree corresponding to the variable value based on the variable value and the fuzzy distribution map, and to determine the control rule corresponding to the input variable based on the variable value and the control rule adjustment table.

[0144] The fuzzy output is obtained from the unit, which is used to obtain fuzzy output based on membership degree and control rules.

[0145] Another optional target joint rotation module 330 includes:

[0146] The current joint angle determination submodule is used to determine the current joint angle of the target joint;

[0147] The rotation control submodule is used to add a rotation increment to the current joint angle to obtain the target rotation angle of the target joint, and to control the target joint to rotate toward the target rotation angle.

[0148] The joint control device in this embodiment of the invention acquires control data associated with the rotation increment of the target joint in the robot's main operator's hand through a control data acquisition module. Based on this control data, the target joint is controlled. An influence parameter acquisition module obtains influence parameters based on the control data and a fuzzy controller. These influence parameters characterize the impact of the control data on the rotation increment and can be obtained by designing control rules using a fuzzy controller based on expert experience and knowledge. Finally, a target joint rotation module obtains the rotation increment based on the influence parameters and controls the target joint to rotate accordingly, thus achieving control of the target joint. This technical solution, by processing the control data through a fuzzy controller to control the rotation of the target joint, can improve the smoothness of the robot's main operator's operation.

[0149] The joint control device provided in the embodiments of the present invention can execute the joint control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0150] It is worth noting that in the above embodiments of the joint control device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0151] Figure 13 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0152] like Figure 13As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0153] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0154] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as joint control methods.

[0155] In some embodiments, the joint control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the joint control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the joint control method by any other suitable means (e.g., by means of firmware).

[0156] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0158] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0159] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0160] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0161] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0162] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0163] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A joint control method characterized by, The method comprises: obtaining control data associated with a rotation increment of a target joint to be controlled in a master manipulator; obtaining an influence parameter based on the control data and a fuzzy controller, wherein the influence parameter is used to represent an influence of the control data on the rotation increment; obtaining the rotation increment based on the influence parameter, and controlling the target joint to rotate based on the rotation increment; the number of the fuzzy controllers is plural, and the obtaining of the influence parameter based on the control data and the fuzzy controller comprises: dividing all the control data into a plurality of data groups, wherein the number of the data groups is the same as the number of the fuzzy controllers; for each of the data groups, inputting the data group into a fuzzy controller corresponding to the data group among the plurality of fuzzy controllers to obtain an influence parameter; all the influence parameters include a proportional parameter, the proportional parameter is obtained based on a proportional data group among the plurality of data groups, the control data in the proportional data group is data associated with at least one joint among the target joint, a first joint and a second joint, wherein the first joint is a joint in the master manipulator located before the target joint, and the second joint is a joint in the master manipulator located before the first joint; the proportional parameter includes a first proportional parameter, a second proportional parameter and a third proportional parameter, the proportional data group includes a first data group corresponding to the first proportional parameter, a second data group corresponding to the second proportional parameter and a third data group corresponding to the third proportional parameter, and the rotation increment is applied in a current control period; the control data in the first data group includes a first joint angle of the first joint and a second joint angle of the second joint; the control data in the second data group includes a joint rotation speed of the first joint and a historical total proportional parameter calculated based on all historical proportional parameters applied in a last control period of the current control period; the control data in the third data group includes a current joint angle of the target joint.

2. The method of any one of claim 1, wherein, all the influence parameters include a step parameter, the step parameter is obtained based on a step data group among the plurality of data groups, and the control data in the step data group includes a joint rotation speed of the second joint and a historical increment, wherein the historical increment is a rotation increment of the target joint applied in a last control period.

3. The method of claim 2, wherein, the obtaining of the rotation increment based on the influence parameter comprises: multiplying the first proportional parameter, the second proportional parameter and the third proportional parameter to obtain a current total proportional parameter in the current control period; multiplying the current total proportional parameter and the step parameter to obtain the rotation increment.

4. The method of claim 1, wherein, the obtaining of the influence parameter based on the control data and the fuzzy controller comprises: determining an input variable of the fuzzy controller based on the control data; fuzzifying the input variables to obtain fuzzy subsets, and constructing a fuzzy distribution graph based on the fuzzy subsets, and establishing fuzzy rules based on the input variables; constructing a control rule adjustment table based on the fuzzy subsets and the fuzzy rules; determining a variable value of the input variable based on the control data; obtaining a fuzzy output based on the variable value, the fuzzy distribution graph and the control rule adjustment table; defuzzifying the fuzzy output to obtain an influence parameter.

5. The method of claim 4, wherein, The obtaining of the fuzzy output based on the variable value, the fuzzy distribution graph and the control rule adjustment table comprises: determining a membership degree corresponding to the variable value based on the variable value and the fuzzy distribution graph, and determining a control rule corresponding to the input variable based on the variable value and the control rule adjustment table; obtaining the fuzzy output based on the membership degree and the control rule.

6. The method of claim 1, wherein, The controlling of the target joint based on the rotation increment comprises: determining a current joint angle of the target joint; adding the rotation increment to the current joint angle to obtain a target rotation angle of the target joint, and controlling the target joint to rotate to the target rotation angle.

7. An articulation control device, characterized by comprise: a control data acquisition module configured to acquire control data associated with a rotation increment of a target joint to be controlled in a main operating hand of a robot; an influence parameter obtaining module configured to obtain an influence parameter based on the control data and a fuzzy controller, wherein the influence parameter is used to represent an influence of the control data on the rotation increment; a target joint rotation module configured to obtain the rotation increment based on the influence parameter, and control the target joint to rotate based on the rotation increment; The number of the fuzzy controllers is multiple, and the influence parameter obtaining module comprises: a data group division sub-module configured to divide all the control data into multiple data groups, wherein the number of the data groups is the same as the number of the fuzzy controllers; an influence parameter obtaining module configured to input each of the data groups into a fuzzy controller corresponding to the data group among the multiple fuzzy controllers to obtain an influence parameter; All the influence parameters comprise a proportion parameter, the proportion parameter is obtained based on a proportion data group among the multiple data groups, and the control data in the proportion data group is associated with at least one joint among the target joint, a first joint and a second joint, wherein the first joint is a joint located before the target joint in the main operating hand, and the second joint is a joint located before the first joint in the main operating hand; The proportion parameter comprises a first proportion parameter, a second proportion parameter and a third proportion parameter, the proportion data group comprises a first data group corresponding to the first proportion parameter, a second data group corresponding to the second proportion parameter and a third data group corresponding to the third proportion parameter, and the rotation increment is applied in a current control period. The control data in the first data group comprises a first joint angle of the first joint and a second joint angle of the second joint; The control data in the second data group comprises a joint rotation speed of the first joint and a historical total proportion parameter calculated based on all historical proportion parameters applied in a previous control cycle of the current control cycle; The control data in the third data group comprises a current joint angle of the target joint.

8. A controller characterized by comprising: The controller is configured in a master operating hand of the robot, comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the joint control method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling a processor to execute the joint control method according to any one of claims 1-6 when executed by the processor.

10. A computer program product, characterised in that, comprising: a computer program, which, when executed by a processor, implements the joint control method according to any one of claims 1-6.

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