WO2021238049A1 - Method, apparatus and control device for multi-load self-adaptive gravity compensation of manipulator - Google Patents

Abstract

Disclosed is a method for the multi-load self-adaptive gravity compensation of a manipulator, the method comprising the following steps: S1.1, building a kinematics model for a manipulator; S1.2, reconstructing a gravity item of a dynamics model; S1.3, sampling in a no-load static position; S1.4, sampling in a static position after tools are installed; S1.5, respectively calculating the value of a parameter to be calibrated of each tool; S1.6, respectively calculating the mass and the centroid of each tool; S2.1, calculating a force which is applied to a flange by the tool which is currently installed; and S2.2, compensating for the gravity of the tool. By means of the method, operation steps are reduced, and the efficiency and the gravity compensation performance are improved.

Description

Multi-load adaptive gravity compensation method, device and control equipment of mechanical arm

Cross-references to related applications

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on May 28, 2020, with the application number 202010466099.X, titled "A Multi-load Adaptive Gravity Compensation Method for a Robotic Arm", the entire content of which is approved The reference is incorporated in this application.

Technical field

This application relates to the technical field of robots, and specifically to a method, device and control equipment for gravity compensation of a mechanical arm.

Background technique

With the gradual development of the robotic arm manufacturing industry and the sensor industry, robotic arms no longer only serve the production line, but also slowly enter various areas of life. The traditional industrial robot arm needs to set a safety range, and personnel are strictly prohibited from entering its work area during operation to prevent injuries. However, most of the application scenarios in life will have a lot of inconveniences when setting the safety range, and the efficiency is not high when man-machine cooperative operation is performed. In order to make the working space of man and machine no longer separate, so as to achieve truly high-efficiency and high-precision man-machine collaborative operation, people have designed a collaborative robotic arm. The collaborative robot arm has the ability to sense contact force and can react to the physical contact between the human body and the robot arm, thus allowing the operator and the robot arm to share the working space. The emergence of collaborative robotic arms has greatly expanded the applications of robotic arms in home care, education and entertainment, health care, high-end manufacturing and other industries. The features of high efficiency, high precision, and high stability of robotic arms are used to improve all aspects of life.

Zero-force control technology means that during the process of dragging and teaching, the mechanical arm can move well in accordance with the external force, as if it is not affected by the gravity of the mechanical arm. This technology reduces the labor intensity of dragging and teaching, and increases the fluency of people in controlling the robotic arm. In order to enable the robotic arm to achieve zero-force control even when the end tool is clamped, it is necessary to calibrate the parameters of the robotic arm body and tools separately, and use reverse engineering methods to accurately calculate the masses and tools of each segment of the robotic arm. Centroid. The technology of the robot body parameter calibration is in the literature Identifying the dynamic model used by the KUKA LWR: A reverse engineering approach (C.Gaz, F.Flacco) and Gravity compensation of KUKA LBR IIWA Through Fast Robot Interface. (C.Hou , Y. Zhao), but there are few calibration data for the end tool parameters. In some complex applications, the robotic arm even needs to change the end tool to complete the work. In this case, how the robotic arm can adaptively compensate for the gravity of the tool, so as to ensure that different end tools can obtain zero-force control, has become the key to whether the collaborative operation is smooth.

At present, the gravity compensation scheme of a robotic arm generally requires the use of a weighing instrument to measure the mass of the end tool, and then use the suspension method or the support method to measure the center of mass of the tool. Then import the measured data into the control system of the robotic arm, and let the control system perform gravity compensation according to the parameters of the tool, so that the robotic arm can achieve zero-force control. However, when measuring the quality and center of mass of the tool, the tool is separated from the system. The measured parameters are easy to ignore the influence of the installation process on the quality and center of mass. Gravity compensation can only be performed on the parameters of one tool at a time, and the tool must be stopped when switching tools. When the program requires multiple tools to switch frequently, the efficiency is low.

Summary of the invention

One of the objectives of the present application includes providing a multi-load adaptive gravity compensation method, device, control device, and readable storage medium of a mechanical arm, reducing operation steps, and improving efficiency and performance of gravity compensation.

In order to achieve the above objectives, the technical solutions adopted in this application are as follows:

In the first aspect, an embodiment of the present application provides a multi-load adaptive gravity compensation method for a robotic arm, which includes the following steps:

S1.1, build the kinematics model of the robotic arm;

S1.2, reconstruct the gravity term of the dynamic model;

S1.3, sampling at no-load static position;

S1.4, perform static position sampling after installing each tool;

S1.5, respectively calculate the parameter values to be calibrated for each tool;

S1.6, respectively calculate the quality and centroid of each tool;

S2.1, calculate the force exerted by the currently installed tool on the flange;

S2.2, compensation of tool gravity.

In a possible implementation, in step S1.1, the standard D-H method is used to construct the robotic arm joint coordinate system.

In a possible implementation, in step S1.3, the robotic arm runs to any non-singular position in the workspace under no load, and the joint position and torque readings are sampled.

In a possible implementation manner, in step S1.4, each tool is installed at the end of the robotic arm in stages, and step S1.3 is repeated to perform static position sampling.

In a possible implementation manner, in step S1.4, for each tool, after the tool is installed at the end of the robotic arm, the current effective working space of the robotic arm corresponding to the tool is determined according to the size of the tool, and then Step S1.3 is repeated to perform static position sampling of the tool based on the effective working space.

In a possible implementation, in step S1.5, the sampling data obtained in S1.3 and S1.4 are grouped according to tools, and then substituted into the gravity term in step S1.2 in turn.

In a possible implementation, in step S1.6, for each tool, the parameter value calibrated when the tool is carried is compared with the parameter value calibrated without load, and based on the comparison result, the quality of the tool, the The relationship between the center of mass of the tool, the mass of the end arm in the robotic arm and the center of mass of the end arm is calculated, and the mass and center of mass of the tool are calculated.

In the second aspect, an embodiment of the present application provides a multi-load adaptive gravity compensation device for a mechanical arm, the device including:

The model building module is configured to build a kinematic model of the robotic arm;

The gravity reconstruction module is configured to reconstruct the gravity term of the dynamic model;

Position sampling module, configured for no-load static position sampling;

The position sampling module is also configured to perform static position sampling after installing each tool;

The parameter calibration module is configured to calculate the parameter values to be calibrated for each tool;

The mass parameter calculation module is configured to calculate the mass and centroid of each tool separately;

The external force calculation module is configured to calculate the force exerted by the currently installed tool on the flange;

The gravity compensation module is configured to compensate the gravity of the tool.

In a possible implementation manner, the model building module uses the standard D-H method to construct the robotic arm joint coordinate system during the process of building the kinematic model of the robotic arm.

In a possible implementation manner, the position sampling module controls the robot arm to run to any non-singular position in the working space when the robot arm is unloaded, and samples the joint position and torque readings.

In a possible implementation manner, after the position sampling module installs each tool on the end of the robotic arm in stages, for each tool, according to the size of the tool, determine the current effective working space of the robotic arm corresponding to the tool, and then Based on the effective working space, the robot arm is repeatedly controlled to run to any non-singular position in the corresponding effective working space, and the joint position and torque readings are sampled.

In a possible implementation manner, the parameter calibration module groups the sampling data obtained by the position sampling module according to tools, and sequentially substitutes them into the gravity terms obtained by the gravity reconstruction module for calculation to obtain each The parameter value of the tool to be calibrated.

In a possible implementation manner, in the process of calculating the mass and center of mass of each tool, the quality parameter calculation module performs the calibration of the parameter value when the tool is carried with the parameter value of the no-load calibration for each tool. Compare and calculate the quality and center of mass of the tool based on the comparison result and the relationship between the quality of the tool, the center of mass of the tool, the mass of the end arm in the robotic arm, and the center of mass of the end arm.

In a third aspect, an embodiment of the present application provides a control device for a robotic arm, including a memory and a processor, the memory stores a computer program that can run on the processor, and the processor executes the computer During the program, the aforementioned multi-load adaptive gravity compensation method of the robotic arm is realized.

In a fourth aspect, an embodiment of the present application provides a readable storage medium on which a computer program is stored. When the computer program is run by a processor, it executes the aforementioned multi-load adaptive gravity compensation method for a robotic arm.

One of the beneficial effects of the embodiments of the present application includes: constructing a robot arm joint coordinate system through the D-H method, and then constructing the center of mass position of each segment of the robot arm based on the joint coordinate system. In the original gravity term, split the terms related to the joint position and the terms related to the mass center of mass. During the splitting process, the parameters to be calibrated need to be appropriately combined, and then the split terms are put into two matrices. Multiplying it still satisfies the original gravity term. Then sample the static position of the robotic arm in the no-load state, then install each tool on the end of the robotic arm, and then sample the static position separately. The different tools of the sampled data are grouped and substituted into the gravity term, and the combined parameter values can be solved by SVD decomposition. Finally, the combined object parameter separation method can be used to extract the quality and centroid of the tool from the combined parameters. Based on the calibrated parameters and real-time joint position feedback under no-load conditions, the force applied by the currently installed end tool to the flange can be calculated. According to the measured external force on the flange, the system can know which tool is currently installed on the flange, so that the calibrated parameter value can be used to directly measure the external force after compensating the tool gravity, or apply the obtained mass and center of mass Into the configuration of the robotic arm. This method simplifies the operation steps in actual application by pre-calculating and acquiring tool parameters, and greatly enhances the fluency of collaborative operation. In addition, through the use of joint position and torque sensors to calibrate the parameters of the tool, the calibrated tool parameters are more in line with the kinematics and dynamics of the robotic arm, thereby improving the performance of zero-force control.

Description of the drawings

Fig. 1 is a flowchart of a multi-load adaptive gravity compensation method for a mechanical arm provided by an embodiment of the present application.

2 is a schematic diagram of the tool and the end of the robot arm of a multi-load adaptive gravity compensation method for a robot arm provided by an embodiment of the present application.

FIG. 3 is a schematic diagram of a multi-tool installation of a robotic arm in a multi-load adaptive gravity compensation method for a robotic arm provided by an embodiment of the present application;

FIG. 4 is a schematic diagram of the composition of a multi-load adaptive gravity compensation device of a mechanical arm provided by an embodiment of the present application.

Detailed ways

The application will be further elaborated and illustrated below in conjunction with specific embodiments and the accompanying drawings of the specification:

Please refer to Figure 1 to Figure 3. In the complete dynamics equation of the mechanical arm, there are inertia terms, centrifugal force and Coriolis force terms, gravity terms and friction terms. The inertia term is related to the joint acceleration, the centrifugal force is related to the Coriolis term and the joint speed, and the friction term is also related to the joint speed. The joint acceleration and joint speed are both 0 when the robot arm is at rest. Therefore, for the static position Research can only be carried out on the gravity term. The construction of dynamic equations with gravity terms requires the joint position and moment of the robotic arm as input, so there are certain requirements for the hardware of the robotic arm. Taking KUKA LBR Med 7 R800 as an example, the robotic arm is a seven-axis collaborative robotic arm, and each joint is equipped with high-precision position and torque sensors, which meets the configuration requirements for the robotic arm hardware in this application. Take KUKA LBR Med 7 R800 seven-axis collaborative robot arm as an example to explain the actual operation.

S1.1: Build a kinematic model of the robotic arm.

In a possible implementation of this embodiment, when building the kinematics model of the robotic arm, the joint coordinate system of the robotic arm adopts the classic DH method (A Kinematic Notation for Lower-Pair Mechanisms Based on Matrices, J. Denavit). , RS Hartenberg). For KUKA LBR Med 7 R800, the D-H parameter table is shown below.

Table 1: KUKA LBR Med 7 R800 D-H parameter table

Joint number α _i a _i (mm) d _i (mm) θ _i 1 -π/2 0 340 θ ₁ 2 π/2 0 0 θ ₂ 3 -π/2 0 400 θ ₃ 4 π/2 0 0 θ ₄ 5 -π/2 0 400 θ ₅ 6 π/2 0 0 θ ₆ 7 0 0 126 θ ₇

Where α _i represents link angle, a _i represents the length of the link, d _i represents link offset, θ _i denotes an angle joint. After the joint coordinate system is established, a centroid coordinate system is established for the centroid position of each segment of the manipulator according to certain rules. When establishing the center of mass coordinate system, it is only necessary to ensure that the origin of the coordinate system is at the center of mass of each arm, and the rotation angle can be consistent with the rotation angle of the i+1th joint.

S1.2: Reconstruct the gravity term of the dynamic model.

In this embodiment, for the stationary manipulator, the gravitational term is equal to the joint torque of the manipulator, and the formula is expressed as:

G(θ,m,c)=τ

It can be seen from the formula that the gravitational term is related to the joint angle θ _i , the mass _mi and the center of mass c _i . Among them, the mass _mi and the center of mass c _i are directly related to the calibration of the tool and need to be extracted. Therefore, the gravity term G must be split as follows:

Y(θ)·A(m,c)=τ

It is easy to find that the mass _mi and the center of mass c _i are coupled in the gravitational term and cannot be separated separately. Therefore, in order to form the matrix A of the parameters to be calibrated, it is necessary to make the calibrated parameters become combined _mi and c _i by combining the parameters reasonably while satisfying the establishment of the equation. When combining parameters, the number of parameters in A should be reduced as much as possible, so as to avoid the solution of the linear equations from falling into the local optimum, so as to obtain a better calibration effect.

S1.3: No-load static position sampling.

In this embodiment, confirm that the robotic arm is in a no-load state, move the robotic arm to a non-singular position in the working space, and collect the positions and moments of the joints after each joint is stationary. Repeat the sampling steps to make the sampling points spread as much as possible throughout the working space. During the sampling process, there may be a small number of sampling points that are very close to the singular position. The lack of freedom of the manipulator at the singular point position will cause the torque feedback to be inaccurate, so these sampling points should be excluded from the sampling set.

Please refer to Figure 3, S1.4: Perform static position sampling after installing each tool.

In this embodiment, each tool is installed in sequence to the end of the robotic arm, and step S1.3 is repeated to perform a round of motion sampling for each tool, and the data set after the singular position is eliminated is saved. Because the tool itself has a certain size, there will be certain restrictions on the range of motion of the robotic arm, and accidents may occur during the movement of the robotic arm body or peripheral obstacles, so it is effective in the process of sampling the static position of the tool's mechanical energy The working space is reconfirmed, and a reasonable movement trajectory is designed so that the sampling points can fill the working space as much as possible while preventing collisions. Therefore, in a possible implementation of this embodiment, for each tool, after the tool is installed at the end of the robotic arm, the current effective working space of the robotic arm corresponding to the tool is determined according to the size of the tool. Then repeat the step S1.3 based on the effective working space to sample the static position of the tool.

S1.5: Calculate the parameter values to be calibrated for each tool respectively.

In this embodiment, to perform step S1.5, all collected data needs to be grouped according to tools, and each group of data is stacked into the equation set obtained in S1.2, as shown below:

中，而关节力矩堆叠到

由于堆叠矩阵

和

都已被确认，待标定参数矩阵A(m,c)可通过SVD分解求线性方程组的方式得到。矩阵

可被分解为以下形式： Among them, the joint positions in the data set will be stacked into a matrix

While the joint moments are stacked to

Due to stacked matrix

with

It has been confirmed that the parameter matrix A(m,c) to be calibrated can be obtained by SVD decomposition to obtain a linear equation system. matrix

It can be broken down into the following forms:

令X＝V ^TA，

则有新表达式 Among them, the left singular matrix U and the right singular matrix V are both orthogonal matrices, so for the overdetermined equation

Let X=V ^T A,

There are new expressions

ΣX=B

的奇异值σ _i且σ ₁≥σ ₂…≥σ _n>0，因此可以求出X。最后根据A＝VX即可求出矩阵A中的待标定参数。 In the above expression, Σ is a diagonal matrix, and all diagonal elements are matrices

The singular value of σ _i and σ ₁ ≥σ ₂ …≥σ _n >0, so X can be found. Finally, the parameters to be calibrated in matrix A can be obtained according to A=VX.

S1.6: Calculate the mass and centroid of each tool separately.

In this embodiment, the process of performing step S1.5 requires the tool as a rigid body to be calibrated after being installed on the end of the robotic arm. Therefore, the mass and center of mass of the rigid body in the last segment of the calibrated parameters are actually the last segment of the robotic arm and the tool. Combine the following parameters. As a result, the quality and center of mass of the tool can be compared with the parameters calibrated with the tool and the parameters calibrated without load, and combined to characterize the relationship between the quality and center of mass of the tool and the mass and center of mass of the end arm of the robotic arm The centroid formula of the multi-body system of the relationship can be determined. Therefore, in a possible implementation of this embodiment, for each tool, the parameter value calibrated when the tool is carried is compared with the parameter value calibrated without load, and based on the comparison result and the quality of the tool, The relationship between the center of mass of the tool, the mass of the end arm in the robotic arm and the center of mass of the end arm is calculated to obtain the mass and center of mass of the tool.

Among them, taking the end of KUKA LBR Med 7 R800 as an example, please refer to Figure 2. The centroid formula of the multi-body system corresponding to the robotic arm system after clamping the tool has the following physical properties:

Where c _c is the center of mass of the tool and the end arm, m _t and c _t are the mass and center of mass of the tool, respectively, m ₇ and c ₇ are the mass and center of mass of the end arm, respectively. Combining the above formula with the calibrated parameter expression, the mass m _t and the centroid c _{t of} the tool can be obtained.

At the same time, this application designs a system for automatically selecting tool parameters for gravity compensation. The system uses the joint torque and the output of the position sensor as the input of the system, and calculates the force applied by the current tool at the end of the robotic arm inside the system to determine the type of tool clamped, and then applies the parameters calculated in S1 to complete gravity compensation. The detailed implementation steps are explained below.

S2.1: Calculate the force exerted by the currently installed tool on the flange (end of the robotic arm).

In this embodiment, the parameters calibrated in the no-load state can be used to calculate the joint torque τ _{robot at} the current position caused by the manipulator body. The real-time measured joint torque τ _measure is subtracted from τ _{robot , and the joint torque τ ext} caused by the external force is obtained. Using the Jacobian matrix again, the external force can be mapped from the joint space to the working space, and the external force on the end of the robotic arm (flange) in the working space can be calculated.

S2.2: Compensation of tool gravity.

In this embodiment, the difference between the tools will be reflected in the value of the external force. For example, if the quality difference between tools is large, the value in the XYZ direction of the external force can be used as the basis for distinguishing the tools; if the quality difference of the tools is small and the difference in the center of mass is large, the torque in the direction of the external force ABC can be considered as the distinction. According to: For tools with little difference in quality and center of mass, it can be considered as a tool and the same set of calibrated parameters can be applied to obtain a better compensation effect. For a robotic arm that allows input of tool parameters for automatic gravity compensation, the mass and center of mass of the tool calculated in step S1.6 can be directly written into the configuration of the robotic arm, and the built-in program of the robotic arm can calculate the external force applied on the tool ; For the mechanical arm without gravity compensation function, the parameters calibrated in step S1.5 can be directly used to calculate the external force received by the current tool. Therefore, the external force felt by the robotic arm is the external force after compensating for the gravity of the tool, and the control strategy that uses the external force as an input will also ignore the influence of the tool, that is, achieve zero-force control.

In addition, please refer to FIG. 4, the present application also provides a multi-load adaptive gravity compensation device 100 of a mechanical arm, so as to realize the functions of the above-mentioned mechanical arm through various functional realization modules included in the multi-load adaptive gravity compensation device 100. The multi-load adaptive gravity compensation method is executed. The multi-load adaptive gravity compensation device 100 includes a model building module 110, a gravity reconstruction module 120, a position sampling module 130, a parameter calibration module 140, a mass parameter calculation module 150, an external force calculation module 160, and a gravity compensation module 170.

The model building module 110 is configured to build a kinematic model of the robotic arm.

The gravity reconstruction module 120 is configured to reconstruct the gravity term of the dynamic model.

The position sampling module 130 is configured to perform no-load static position sampling.

The position sampling module 130 is also configured to perform static position sampling after each tool is installed.

The parameter calibration module 140 is configured to respectively calculate the parameter values to be calibrated for each tool.

The mass parameter calculation module 150 is configured to calculate the mass and center of mass of each tool respectively.

The external force calculation module 160 is configured to calculate the force exerted on the flange by the currently installed tool.

The gravity compensation module 170 is configured to compensate the gravity of the tool.

In a possible implementation of this embodiment, the model building module 110 uses the standard D-H method to construct the robotic arm joint coordinate system during the process of building the kinematic model of the robotic arm.

In a possible implementation of this embodiment, the position sampling module 130 controls the robot arm to run to any non-singular position in the working space when the robot arm is unloaded, and samples joint positions and torque readings.

In a possible implementation of this embodiment, after the position sampling module 130 installs each tool on the end of the robotic arm in stages, for each tool, it determines the current corresponding to the robotic arm according to the size of the tool. Effective working space, and then based on the effective working space, repeatedly control the manipulator to run to any non-singular position in the corresponding effective working space, and sample the joint position and torque readings.

In a possible implementation of this embodiment, the parameter calibration module 140 groups the sampling data obtained by the position sampling module according to tools, and sequentially substitutes them into the gravity term obtained by the gravity reconstruction module. Perform calculations to obtain the parameter values to be calibrated for each tool.

It should be noted that the basic principles and technical effects of the multi-load adaptive gravity compensation device 100 provided by the embodiment of the present application are the same as those of the aforementioned multi-load adaptive gravity compensation method. For a brief description, part of this embodiment is not For what is mentioned, please refer to the above description of the multi-load adaptive gravity compensation method.

In addition, the present application also provides a control device of a mechanical arm, the control device including a memory and a processor. The memory may include one or more computer program products, and the computer program product may include various forms of readable storage media, such as volatile memory and/or nonvolatile memory. The volatile memory may include random access memory and/or cache memory, for example. The non-volatile memory may include, for example, a read-only memory, a hard disk, a flash memory, and the like. One or more computer programs can be stored on the readable storage medium, and the processor can run the computer programs to realize the functions represented by the above-mentioned multi-load adaptive gravity compensation method of the robotic arm and/or other desired Function. Various application programs and various data, such as various data used and/or generated by the application program, can also be stored in the readable storage medium.

The processor may be implemented in the form of at least one of a digital signal processor, a field programmable gate array, and a programmable logic array. The processor may be a central processing unit or have data processing capabilities and/or instruction execution One or a combination of several processing units in other forms of capabilities, and can control other components in the control device to perform desired functions. The processor can correspondingly execute the computer program stored in the memory to realize the function represented by the computer program.

In a possible implementation of this embodiment, the above-mentioned multi-load adaptive gravity compensation device 100 of the robotic arm can be stored in the memory of the control device in the form of software or firmware, and the processor of the control device The software function modules and computer programs included in the multi-load adaptive gravity compensation device 100 in the memory are executed to realize the functions corresponding to the above-mentioned multi-load adaptive gravity compensation method of the robotic arm.

To sum up, the above scheme uses the D-H method to construct the robotic arm joint coordinate system, and then builds the center of mass position of each segment of the robotic arm based on the joint coordinate system. In the original gravity term, split the terms related to the joint position and the terms related to the mass center of mass. During the splitting process, the parameters to be calibrated need to be appropriately combined, and then the split terms are put into two matrices. Multiplying it still satisfies the original gravity term. Then sample the static position of the robotic arm in the no-load state, then install each tool on the end of the robotic arm, and then sample the static position separately. The different tools of the sampled data are grouped and substituted into the gravity term, and the combined parameter values can be solved by SVD decomposition. Finally, the combined object parameter separation method can be used to extract the quality and centroid of the tool from the combined parameters. Based on the calibrated parameters and real-time joint position feedback under no-load conditions, the force applied by the currently installed end tool to the flange can be calculated. According to the measured external force on the flange, the system can know which tool is currently installed on the flange, so that the calibrated parameter value can be used to directly measure the external force after compensating the tool gravity, or apply the obtained mass and center of mass Into the configuration of the robotic arm. This method simplifies the operation steps in actual application by pre-calculating and acquiring tool parameters, and greatly enhances the fluency of collaborative operation. In addition, through the use of joint position and torque sensors to calibrate the parameters of the tool, the calibrated tool parameters are more in line with the kinematics and dynamics of the robotic arm, thereby improving the performance of zero-force control.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, not to limit the scope of protection of this application. Although this application has been described in detail with reference to alternative implementations, those of ordinary skill in the art should It is understood that the technical solution of the present application can be modified or equivalently replaced without departing from the essence and scope of the technical solution of the present application.

Industrial applicability

The embodiment of the application provides a multi-load adaptive gravity compensation method, device, control device, and readable storage medium for a robotic arm. The robotic arm joint coordinate system is constructed by the DH method, and the coordinate system of each segment of the robotic arm is determined based on the joint coordinate system. The position of the center of mass is used to establish the system. In the original gravity term, split the terms related to the joint position and the terms related to the mass center of mass. During the splitting process, the parameters to be calibrated need to be appropriately combined, and then the split terms are put into two matrices. Multiplying it still satisfies the original gravity term. Then sample the static position of the robotic arm in the no-load state, then install each tool on the end of the robotic arm, and then sample the static position separately. The different tools of the sampled data are grouped and substituted into the gravity term, and the combined parameter values can be solved by SVD decomposition. Finally, the combined object parameter separation method can be used to extract the quality and centroid of the tool from the combined parameters. Based on the calibrated parameters and real-time joint position feedback under no-load conditions, the force applied by the currently installed end tool to the flange can be calculated. According to the measured external force on the flange, the system can know which tool is currently installed on the flange, so that the calibrated parameter value can be used to directly measure the external force after compensating the tool gravity, or apply the obtained mass and center of mass Into the configuration of the robotic arm. This method simplifies the operation steps in actual application by pre-calculating and acquiring tool parameters, and greatly enhances the fluency of collaborative operation. In addition, through the use of joint position and torque sensors to calibrate the parameters of the tool, the calibrated tool parameters are more in line with the kinematics and dynamics of the robotic arm, thereby improving the performance of zero-force control.

Claims (15)

A multi-load adaptive gravity compensation method for a mechanical arm is characterized in that it includes the following steps: S1.1, build the kinematics model of the robotic arm; S1.2, reconstruct the gravity term of the dynamic model; S1.3, sampling at no-load static position; S1.4, perform static position sampling after installing each tool; S1.5, respectively calculate the parameter values to be calibrated for each tool; S1.6, respectively calculate the quality and centroid of each tool; S2.1, calculate the force exerted by the currently installed tool on the flange; S2.2, compensation of tool gravity. The multi-load adaptive gravity compensation method of the robotic arm according to claim 1, wherein in step S1.1, the standard D-H method is used to construct the robotic arm joint coordinate system. The multi-load adaptive gravity compensation method of the robotic arm according to claim 1 or 2, characterized in that, in step S1.3, the robotic arm runs to any non-singular position in the workspace under the condition of no load, and the joints are sampled Position and torque readings. The multi-load adaptive gravity compensation method of the robotic arm according to claim 3, wherein in step S1.4, each tool is installed at the end of the robotic arm in stages, and step S1.3 is repeated to perform static position sampling. The multi-load adaptive gravity compensation method of the robotic arm according to claim 4, wherein, in step S1.4, for each tool, after the tool is installed at the end of the robotic arm, the size of the tool is determined The current effective working space of the robotic arm corresponding to the tool, and then repeating step S1.3 based on the effective working space to sample the static position of the tool. The multi-load adaptive gravity compensation method for a robotic arm according to any one of claims 1-5, wherein in step S1.5, the sampled data obtained in S1.3 and S1.4 are grouped according to tools , And substitute them into the gravity item in step S1.2 in turn. The multi-load adaptive gravity compensation method of a mechanical arm according to any one of claims 1-6, wherein, in step S1.6, for each tool, the parameter value calibrated when the tool is carried is compared with the The parameter values of the load calibration are compared, and the quality of the tool is calculated according to the comparison result and the relationship between the quality of the tool, the center of mass of the tool, the mass of the end arm in the robotic arm, and the center of mass of the end arm. Centroid. A multi-load adaptive gravity compensation device for a mechanical arm, characterized in that the device includes: The model building module is configured to build a kinematic model of the robotic arm; The gravity reconstruction module is configured to reconstruct the gravity term of the dynamic model; Position sampling module, configured for no-load static position sampling; The position sampling module is also configured to perform static position sampling after installing each tool; The parameter calibration module is configured to calculate the parameter values to be calibrated for each tool; The mass parameter calculation module is configured to calculate the mass and centroid of each tool separately; The external force calculation module is configured to calculate the force exerted by the currently installed tool on the flange; The gravity compensation module is configured to compensate the gravity of the tool. 8. The multi-load adaptive gravity compensation device of the robotic arm according to claim 8, wherein the model building module uses the standard D-H method to construct the robotic arm joint coordinate system during the process of building the kinematic model of the robotic arm. The multi-load adaptive gravity compensation device of the mechanical arm according to claim 8 or 9, wherein the position sampling module controls the mechanical arm to run to any non-singular position in the working space when the mechanical arm is unloaded, Sample joint position and torque readings. The multi-load adaptive gravity compensation device of the mechanical arm according to claim 10, wherein the position sampling module determines the size of each tool according to the size of the tool after each tool is installed at the end of the mechanical arm in stages. The current effective working space of the robotic arm corresponding to the tool, and then based on the effective working space, the robotic arm is repeatedly controlled to run to any non-singular position in the corresponding effective working space, and the joint position and torque readings are sampled. The multi-load adaptive gravity compensation device for a mechanical arm according to any one of claims 8-11, wherein the parameter calibration module groups the sampling data obtained by the position sampling module according to tools, and Substituting into the gravity term obtained by the gravity reconstruction module in turn for calculation, and obtaining the parameter values to be calibrated for each tool. The multi-load adaptive gravity compensation device of the robotic arm according to any one of claims 8-12, wherein the mass parameter calculation module is for each tool in the process of calculating the mass and center of mass of each tool. Tool, compare the calibrated parameter value when carrying the tool with the parameter value calibrated without load, and based on the comparison result and the quality of the tool, the center of mass of the tool, the mass of the end arm of the robotic arm, and the center of mass of the end arm Calculate the quality and center of mass of the tool. A control device for a robotic arm, comprising a memory and a processor, and a computer program that can be run on the processor is stored in the memory, wherein the processor implements the claims when the computer program is executed by the processor. The multi-load adaptive gravity compensation method of the mechanical arm described in any one of 1-7. A readable storage medium with a computer program stored thereon, wherein the computer program executes the multi-load adaptive gravity compensation of the robotic arm according to any one of claims 1-7 when the computer program is run by a processor method.

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