Method, device, electronic device and storage medium for estimating friction force of robot joint

Abstract

The present invention discloses a method, device, electronic device and storage medium for estimating the friction force of a robot joint. The method comprises: obtaining the robot joint position and the robot joint speed of the robot arm; if the robot joint speed of the robot arm is within a preset low-speed range, the robot joint position and the robot joint speed are input into a pre-built low-speed friction force prediction model to obtain the first robot joint friction force; if the robot joint speed of the robot arm is within a preset high-speed range, the robot joint position and the robot joint speed are input into a pre-built high-speed friction force prediction model to obtain the second robot joint friction force, wherein the robot joint speed within the preset low-speed range is less than the robot joint speed within the preset high-speed range. The above technical scheme realizes the separate prediction of the robot joint friction force at different speeds, is more targeted, and improves the estimation accuracy of the robot joint friction force.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, and in particular to a method, device, electronic equipment and storage medium for estimating friction force of a robot arm joint. Background Art

[0002] With the development of robotics technology, laparoscopic surgical robots have become an important innovation in the medical field with their excellent performance and broad application prospects.

[0003] The control performance of laparoscopic surgical robots is significantly affected by friction, especially at low and high speeds. The nonlinear characteristics of friction make it difficult for existing friction models to meet high-precision control requirements.

[0004] In the process of implementing the present invention, the inventors found that there are at least the following technical problems in the prior art: the solution of estimating the friction force of the robot arm joints by using the existing friction force model has the problem of low accuracy in estimating the friction force of the robot arm joints. Summary of the invention

[0005] The present invention provides a method, device, electronic device and storage medium for estimating the friction force of a robot arm joint, so as to improve the estimation accuracy of the friction force of the robot arm joint.

[0006] According to one aspect of the present invention, a method for estimating friction force of a robot arm joint is provided, comprising:

[0007] Get the robot's arm joint position and arm joint speed;

[0008] If the mechanical arm joint speed is within a preset low-speed range, the mechanical arm joint position and the mechanical arm joint speed are input into a pre-built low-speed friction force prediction model to obtain a first mechanical arm joint friction force;

[0009] If the robotic arm joint speed is within a preset high-speed range, the robotic arm joint position and the robotic arm joint speed are input into a pre-constructed high-speed friction force prediction model to obtain a second robotic arm joint friction force, wherein the robotic arm joint speed within the preset low-speed range is less than the robotic arm joint speed within the preset high-speed range.

[0010] According to another aspect of the present invention, there is provided a device for estimating friction force of a robot arm joint, comprising:

[0011] A robot arm joint data acquisition module is used to obtain the robot's robot arm joint position and robot arm joint speed;

[0012] A low-speed friction force prediction module, for inputting the mechanical arm joint position and the mechanical arm joint speed into a pre-built low-speed friction force prediction model to obtain a first mechanical arm joint friction force if the mechanical arm joint speed is within a preset low-speed range;

[0013] A high-speed friction force prediction module is used to input the robot arm joint position and the robot arm joint speed into a pre-constructed high-speed friction force prediction model to obtain a second robot arm joint friction force if the robot arm joint speed is within a preset high-speed range interval, wherein the robot arm joint speed within the preset low-speed range interval is smaller than the robot arm joint speed within the preset high-speed range interval.

[0014] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0015] at least one processor;

[0016] and a memory communicatively coupled to the at least one processor;

[0017] Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for estimating the friction force of a robotic arm joint described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for estimating friction force of a robot arm joint as described in any embodiment of the present invention when executed.

[0019] The technical solution of the embodiment of the present invention obtains the robot's mechanical arm joint position and mechanical arm joint speed. If the mechanical arm joint speed is within a preset low-speed range, the mechanical arm joint position and mechanical arm joint speed are input into a pre-built low-speed friction prediction model to obtain the first mechanical arm joint friction. If the mechanical arm joint speed is within a preset high-speed range, the mechanical arm joint position and mechanical arm joint speed are input into a pre-built high-speed friction prediction model to obtain the second mechanical arm joint friction. The above technical solution realizes the separate prediction of mechanical arm joint friction at different speeds, making the prediction more targeted and improving the estimation accuracy of mechanical arm joint friction.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a method for estimating friction force of a robot arm joint provided according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of a method for estimating friction force of a mechanical arm joint provided according to Embodiment 2 of the present invention;

[0024] Figure 3 is a flow chart of a method for estimating friction force of a robot arm joint provided according to Embodiment 3 of the present invention;

[0025] Figure 4 is a schematic diagram of a speed-mechanical arm joint friction force variation curve provided according to an embodiment of the present invention;

[0026] Figure 5 is a schematic diagram of the structure of a mechanical arm joint friction force estimation device provided according to a fourth embodiment of the present invention;

[0027] Figure 6 It is a schematic diagram of the structure of an electronic device for implementing the method for estimating the friction force of a robot arm joint according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0030] Embodiment 1

[0031] Figure 1 This is a flow chart of a method for estimating the friction force of a mechanical arm joint provided in the first embodiment of the present invention. This embodiment can be applied to the case of estimating the friction force of a mechanical arm joint of a laparoscopic surgical robot. The method can be executed by a mechanical arm joint friction force estimation device. The mechanical arm joint friction force estimation device can be implemented in the form of hardware and / or software. The mechanical arm joint friction force estimation device can be configured in an electronic device such as a controller of a laparoscopic surgical robot. Figure 1 As shown, the method includes:

[0032] S110, obtaining the robot's mechanical arm joint positions and mechanical arm joint velocities.

[0033] In an embodiment of the present invention, the robot may be a medical robot with a robotic arm, such as a laparoscopic surgery robot or an ear surgery robot, etc., which is not specifically limited herein. The robotic arm joint position refers to the position of the robot joint. For example, the robotic arm joint position may be 10° or 45°, etc. The robotic arm joint speed refers to the movement speed of the robot joint. For example, the robotic arm joint speed may be 20° / s or 180° / s, etc.

[0034] For example, the position of the robot's mechanical arm joints can be acquired in real time by a displacement sensor, and the speed of the robot's mechanical arm joints can be acquired in real time by a speed sensor.

[0035] S120: If the robot arm joint speed is within a preset low-speed range, the robot arm joint position and the robot arm joint speed are input into a pre-built low-speed friction force prediction model to obtain a first robot arm joint friction force.

[0036] In the embodiment of the present invention, the preset low speed range interval refers to the speed range corresponding to the low speed motion state of the robot arm joint. For example, the preset low speed range interval can be ,in, Indicates the reverse rotation threshold speed of the robot joint. Represents the forward rotation threshold speed of the robot joint. The low-speed friction prediction model refers to a friction model that can predict the friction of the robot joint at low speed. For example, the low-speed friction prediction model can be constructed based on the n-th order Fourier series and polynomial. The first robot joint friction refers to the robot joint friction predicted by the low-speed friction prediction model.

[0037] For example, if In Inside, and Substitution The friction force of the first manipulator joint is obtained, where represents the joint position of the robot arm, represents the robot joint speed, Represents the low-speed friction prediction model.

[0038] S130. If the robot arm joint speed is within a preset high-speed range, the robot arm joint position and the robot arm joint speed are input into a pre-constructed high-speed friction force prediction model to obtain a second robot arm joint friction force, wherein the robot arm joint speed within the preset low-speed range is less than the robot arm joint speed within the preset high-speed range.

[0039] In the embodiment of the present invention, the preset high speed range interval refers to the speed range corresponding to the high speed motion state of the robot arm joint. or ,in, Indicates the maximum forward rotation speed of the robot joint. Represents the maximum speed of reverse rotation of the robot joint. The high-speed friction prediction model refers to a friction model that can predict the friction of the robot joint under high-speed conditions. For example, the high-speed friction prediction model can be constructed based on the n-order viscous friction model. The second robot joint friction refers to the robot joint friction predicted by the high-speed friction prediction model.

[0040] For example, if In Inside, and Substitution The friction force of the second manipulator joint is obtained, where represents the joint position of the robot arm, represents the robot joint speed, Represents the high-speed friction prediction model.

[0041] For example, the controller of the endoscopic surgery robot can be used to control the mechanical arm joint to rotate m circles at a preset speed (e.g., 10° / s) in both positive and negative directions, and the positive and negative joint torques corresponding to the mechanical arm joint positions can be collected. Then, the collected positive and negative joint torques corresponding to the mechanical arm joint positions are interpolated to obtain the forward joint interpolation torque. and inverse joint interpolation torque , where p=1,…,n, so that there are corresponding joint torques at positions that are integer multiples of the step length (for example, 0.01°), and then the discrete data set related to the gravity torque and the joint position of the manipulator is calculated. The discrete data set is:

[0042] ;

[0043] Furthermore, a theoretical gravity torque model of the manipulator can be established based on the dynamic equations, and the discrete data set Correct the gravity moment model parameter x to obtain the actual gravity moment model , where x represents the centroid position.

[0044] Furthermore, the robot arm joint can be statically positioned to a preset position (such as joint zero position), and a gradually increasing robot arm joint torque can be applied in the opposite direction of the gravity torque to observe the robot arm joint acceleration. When the robot arm joint acceleration is greater than the acceleration threshold, the robot arm joint torque at this time is recorded. Joint position with robot arm , then the maximum static friction force is , control the motor torque according to the robot arm joint position , continue to observe the robot arm joint acceleration. When the robot arm joint acceleration tends to 0 and stabilizes, record the robot arm joint speed at this time, and the robot arm joint speed at this time can be used as the robot arm joint threshold speed.

[0045] The technical solution of the embodiment of the present invention obtains the robot's mechanical arm joint position and mechanical arm joint speed. If the mechanical arm joint speed is within a preset low-speed range, the mechanical arm joint position and mechanical arm joint speed are input into a pre-built low-speed friction prediction model to obtain the first mechanical arm joint friction. If the mechanical arm joint speed is within a preset high-speed range, the mechanical arm joint position and mechanical arm joint speed are input into a pre-built high-speed friction prediction model to obtain the second mechanical arm joint friction. The above technical solution realizes the separate prediction of mechanical arm joint friction at different speeds, making the prediction more targeted and improving the estimation accuracy of mechanical arm joint friction.

[0046] Embodiment 2

[0047] Figure 2 A flowchart of a method for estimating friction force of a robotic arm joint provided in the second embodiment of the present invention, the method of this embodiment can be combined with the various optional schemes in the method for estimating friction force of a robotic arm joint provided in the above embodiments. The method for estimating friction force of a robotic arm joint provided in this embodiment is further optimized. Optionally, before obtaining the robotic arm joint position and the robotic arm joint speed of the robot, it also includes: obtaining the joint position, joint speed and motor torque of the robotic arm joint under uniform motion at multiple speeds within a preset low-speed range; determining a low-speed friction prediction model based on the joint position, joint speed and motor torque of the robotic arm joint under uniform motion at multiple speeds within a preset low-speed range.

[0048] like Figure 2 As shown, the method includes:

[0049] S210, obtaining joint positions, joint speeds, and motor torques of the robot arm joints when the joints move at multiple uniform speeds within a preset low-speed range.

[0050] S220, determining a low-speed friction force prediction model based on the joint positions, joint speeds, and motor torques of the robotic arm joints under uniform motion at multiple speeds within a preset low-speed range.

[0051] S230, obtaining the robot's mechanical arm joint positions and mechanical arm joint velocities.

[0052] S240: If the robot arm joint speed is within a preset low-speed range, the robot arm joint position and the robot arm joint speed are input into a pre-built low-speed friction force prediction model to obtain a first robot arm joint friction force.

[0053] S250. If the robotic arm joint speed is within a preset high-speed range, the robotic arm joint position and the robotic arm joint speed are input into a pre-constructed high-speed friction force prediction model to obtain a second robotic arm joint friction force, wherein the robotic arm joint speed within the preset low-speed range is less than the robotic arm joint speed within the preset high-speed range.

[0054] In the embodiment of the present invention, it can be Within the range, the joints of the robotic arm are controlled to move at different speeds at a uniform speed, and the joint positions, joint speeds, and motor torques at each low speed are recorded. Furthermore, the model parameters of the low-speed friction prediction model can be solved according to the joint positions, joint speeds, and motor torques at each low speed, so that the low-speed friction prediction model can be constructed according to the model parameters of the low-speed friction prediction model.

[0055] In some optional embodiments, a low-speed friction prediction model is determined based on the joint position, joint speed and motor torque of the robotic arm joint under uniform motion at multiple speeds within a preset low-speed range, including: obtaining an initial low-speed friction prediction model; constructing a low-speed friction target function based on the initial low-speed friction prediction model; solving the low-speed friction target function based on the joint position, joint speed and motor torque of the robotic arm joint under uniform motion at multiple speeds within a preset low-speed range to obtain model parameters of the low-speed friction prediction model; and constructing a low-speed friction prediction model based on the model parameters of the low-speed friction prediction model.

[0056] In the embodiment of the present invention, the initial low-speed friction force prediction model refers to a low-speed friction force prediction model whose model parameters have not been identified.

[0057] For example, in Within the range, the robot arm joints are controlled to move at different speeds, and the joint positions, joint speeds, and motor torques at each low speed are recorded. Further, the initial low-speed friction prediction model can be , the low-speed friction objective function can be expressed as follows:

[0058] ;

[0059] in, represents the motor torque, represents the gravity torque model; further, based on the joint position, joint speed and motor torque of the robot joint under multiple uniform speed motion within a preset low-speed range, the low-speed friction objective function of the intelligent algorithm can be solved to minimize f, thereby obtaining the model parameters of the low-speed friction prediction model, that is, obtaining the low-speed friction prediction model.

[0060] The intelligent algorithm may be a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, a regression algorithm or a least squares algorithm, etc., which is not specifically limited here.

[0061] Exemplarily, the low-speed friction prediction model is:

[0062] ;

[0063] in, represents the joint position of the robot arm, represents the robot joint speed, represents the threshold velocity of the robot joint, represents the maximum static friction force, n represents the order of the Fourier series; , , , , , , , , and These are the model parameters of the low-speed friction prediction model.

[0064] It should be noted that when the robot joint speed is 0 or When , the low-speed friction prediction model output is equal to the maximum static friction. In addition, , , , , , , , , and It has no specific physical meaning and is only a model parameter of the low-speed friction prediction model.

[0065] The technical solution of the embodiment of the present invention obtains the joint position, joint speed and motor torque of the robot arm joint under multiple uniform speed motion within a preset low-speed range, and then determines the low-speed friction force prediction model based on the joint position, joint speed and motor torque of the robot arm joint under multiple uniform speed motion within the preset low-speed range, thereby realizing the construction of the low-speed friction force prediction model, making subsequent predictions more targeted and improving the estimation accuracy of the robot arm joint friction.

[0066] Embodiment 3

[0067] Figure 3 A flowchart of a method for estimating friction force of a robotic arm joint provided in Embodiment 3 of the present invention, the method of this embodiment can be combined with the various optional schemes in the method for estimating friction force of a robotic arm joint provided in the above embodiments. The method for estimating friction force of a robotic arm joint provided in this embodiment is further optimized. Optionally, before obtaining the robotic arm joint position and robotic arm joint speed of the robot, it also includes: obtaining the joint position, joint speed and motor torque of the robotic arm joint under uniform motion at multiple speeds within a preset high-speed range; determining a high-speed friction prediction model based on the joint position, joint speed and motor torque of the robotic arm joint under uniform motion at multiple speeds within a preset high-speed range.

[0068] like Figure 3 As shown, the method includes:

[0069] S310, obtaining joint positions, joint speeds, and motor torques of the robot arm joints when the joints move at multiple uniform speeds within a preset high-speed range.

[0070] S320, determining a high-speed friction force prediction model based on the joint positions, joint speeds, and motor torques of the robotic arm joints under uniform motion at multiple speeds within a preset high-speed range.

[0071] S330, obtaining the robot's mechanical arm joint positions and mechanical arm joint velocities.

[0072] S340: If the robot arm joint speed is within a preset low-speed range, the robot arm joint position and the robot arm joint speed are input into a pre-built low-speed friction force prediction model to obtain a first robot arm joint friction force.

[0073] S350. If the robot arm joint speed is within a preset high-speed range, the robot arm joint position and the robot arm joint speed are input into a pre-constructed high-speed friction force prediction model to obtain a second robot arm joint friction force, wherein the robot arm joint speed within the preset low-speed range is less than the robot arm joint speed within the preset high-speed range.

[0074] In the embodiment of the present invention, it can be and Within the range, the joints of the robotic arm are controlled to move at different speeds at a uniform speed, and the joint positions, joint speeds, and motor torques at each high speed are recorded. Furthermore, the model parameters of the high-speed friction prediction model can be solved according to the joint positions, joint speeds, and motor torques at each high speed, so that the high-speed friction prediction model can be constructed according to the model parameters of the high-speed friction prediction model.

[0075] In some optional embodiments, a high-speed friction prediction model is determined based on the joint position, joint speed and motor torque of the robotic arm joint under uniform motion at multiple speeds within a preset high-speed range, including: obtaining an initial high-speed friction prediction model; constructing a high-speed friction target function based on the initial high-speed friction prediction model; solving the high-speed friction target function based on the joint position, joint speed and motor torque of the robotic arm joint under uniform motion at multiple speeds within a preset high-speed range to obtain model parameters of the high-speed friction prediction model; and constructing a high-speed friction prediction model based on the model parameters of the high-speed friction prediction model.

[0076] In the embodiment of the present invention, the initial high-speed friction force prediction model refers to a high-speed friction force prediction model whose model parameters have not been identified.

[0077] For example, in and Within the range, the robot arm joints are controlled to move at different speeds, and the joint positions, joint speeds, and motor torques at each high speed are recorded. Furthermore, the initial high-speed friction force prediction model can be , the high-speed friction objective function can be expressed as follows:

[0078] ;

[0079] in, represents the motor torque, represents the gravity torque model; further, based on the joint position, joint speed and motor torque of the robot joint under multiple uniform speed motion within a preset high-speed range, the high-speed friction objective function of the intelligent algorithm can be solved to minimize f, thereby obtaining the model parameters of the high-speed friction prediction model, that is, obtaining the high-speed friction prediction model.

[0080] The intelligent algorithm may be a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, a regression algorithm or a least squares algorithm, etc., which is not specifically limited here.

[0081] Exemplarily, the high-speed friction prediction model is:

[0082] ;

[0083] ;

[0084] in, represents the joint position of the robot arm, represents the robot joint speed, represents the threshold velocity of the robot joint, represents the maximum static friction force, n represents the order of the Fourier series; , , , , and These are the model parameters of the high-speed friction prediction model.

[0085] It should be noted that It is used to compensate for the maximum static friction differences at different robot joint positions. , , , , and It has no specific physical meaning and is only a model parameter of the high-speed friction prediction model.

[0086] For example, Figure 4It is a schematic diagram of a speed-mechanical arm joint friction force change curve provided by an embodiment of the present invention. Figure 4 The horizontal axis represents the mechanical arm joint speed, and the vertical axis represents the mechanical arm joint friction. Specifically, the friction prediction model of this embodiment can be expressed by the following formula:

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] in, represents the joint position of the robot arm, represents the robot joint speed, represents the threshold velocity of the robot joint, represents the maximum static friction force, n represents the order of the Fourier series; , , , , , , , , and are the model parameters of the low-speed friction prediction model. , , , , and These are the model parameters of the high-speed friction prediction model.

[0092] The technical solution of the embodiment of the present invention obtains the joint position, joint speed and motor torque of the robot arm joint under multiple uniform speed motion within a preset high-speed range, and then determines the high-speed friction force prediction model based on the joint position, joint speed and motor torque of the robot arm joint under multiple uniform speed motion within the preset high-speed range, thereby realizing the construction of a high-speed friction force prediction model, making subsequent predictions more targeted and improving the estimation accuracy of the friction of the robot arm joints.

[0093] Embodiment 4

[0094] Figure 5 This is a schematic diagram of the structure of a mechanical arm joint friction force estimation device provided by the fourth embodiment of the present invention. Figure 5 As shown, the device comprises:

[0095] A robot arm joint data acquisition module 410 is used to acquire the robot's robot arm joint positions and robot arm joint velocities;

[0096] A low-speed friction force prediction module 420 is used to input the mechanical arm joint position and the mechanical arm joint speed into a pre-built low-speed friction force prediction model to obtain a first mechanical arm joint friction force if the mechanical arm joint speed is within a preset low-speed range;

[0097] The high-speed friction force prediction module 430 is used to input the robot arm joint position and the robot arm joint speed into a pre-built high-speed friction force prediction model to obtain a second robot arm joint friction force if the robot arm joint speed is within a preset high-speed range interval, wherein the robot arm joint speed within the preset low-speed range interval is smaller than the robot arm joint speed within the preset high-speed range interval.

[0098] The technical solution of the embodiment of the present invention obtains the robot's mechanical arm joint position and mechanical arm joint speed. If the mechanical arm joint speed is within a preset low-speed range, the mechanical arm joint position and mechanical arm joint speed are input into a pre-built low-speed friction prediction model to obtain the first mechanical arm joint friction. If the mechanical arm joint speed is within a preset high-speed range, the mechanical arm joint position and mechanical arm joint speed are input into a pre-built high-speed friction prediction model to obtain the second mechanical arm joint friction. The above technical solution realizes the separate prediction of mechanical arm joint friction at different speeds, making the prediction more targeted and improving the estimation accuracy of mechanical arm joint friction.

[0099] In some optional embodiments, the robot arm joint friction force estimation device further includes:

[0100] A low-speed related data acquisition module is used to obtain the joint position, joint speed and motor torque of the robot arm joint under multiple uniform speed motions within a preset low-speed range;

[0101] The low-speed friction force prediction model determination module is used to determine the low-speed friction force prediction model based on the joint position, joint speed and motor torque of the robotic arm joint under uniform motion at multiple speeds within a preset low-speed range.

[0102] In some optional implementations, the low-speed friction prediction model determination module is specifically used to:

[0103] Obtaining an initial low-speed friction prediction model;

[0104] Constructing a low-speed friction objective function based on the initial low-speed friction prediction model;

[0105] Based on the joint positions, joint speeds and motor torques of the robot arm joints under uniform motion at multiple speeds within a preset low-speed range, the low-speed friction objective function is solved to obtain model parameters of the low-speed friction prediction model;

[0106] A low-speed friction force prediction model is constructed based on the model parameters of the low-speed friction force prediction model.

[0107] In some optional implementations, the low-speed friction prediction model is:

[0108] ;

[0109] in, represents the joint position of the robot arm, represents the robot joint speed, represents the threshold velocity of the robot joint, represents the maximum static friction force, n represents the order of the Fourier series; , , , , , , , , and These are the model parameters of the low-speed friction prediction model.

[0110] In some optional embodiments, the robot arm joint friction force estimation device further includes:

[0111] A high-speed related data acquisition module is used to obtain the joint position, joint speed and motor torque of the robot arm joint under multiple uniform speed motions within a preset high-speed range;

[0112] The high-speed friction force prediction model determination module is used to determine the high-speed friction force prediction model based on the joint position, joint speed and motor torque of the robotic arm joint under multiple uniform speed motions within a preset high-speed range.

[0113] In some optional implementations, the high-speed friction force prediction model determination module is specifically used to:

[0114] Obtaining an initial high-speed friction prediction model;

[0115] Constructing a high-speed friction force objective function based on the initial high-speed friction force prediction model;

[0116] Based on the joint positions, joint velocities and motor torques of the robot arm joints under uniform motion at multiple speeds within a preset high-speed range, the high-speed friction objective function is solved to obtain model parameters of the high-speed friction prediction model;

[0117] A high-speed friction force prediction model is constructed based on the model parameters of the high-speed friction force prediction model.

[0118] In some optional implementations, the high-speed friction force prediction model is:

[0119] ;

[0120] ;

[0121] in, represents the joint position of the robot arm, represents the robot joint speed, represents the threshold velocity of the robot joint, represents the maximum static friction force, n represents the order of the Fourier series; , , , , and These are the model parameters of the high-speed friction prediction model.

[0122] The robot arm joint friction force estimation device provided in the embodiment of the present invention can execute the robot arm joint friction force estimation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0123] Embodiment 5

[0124] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment 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 assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0125] like Figure 6As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The I / O interface 15 is also connected to the bus 14.

[0126] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0127] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for estimating friction force of a mechanical arm joint, which includes:

[0128] Get the robot's arm joint position and arm joint speed;

[0129] If the mechanical arm joint speed is within a preset low-speed range, the mechanical arm joint position and the mechanical arm joint speed are input into a pre-built low-speed friction force prediction model to obtain a first mechanical arm joint friction force;

[0130] If the robotic arm joint speed is within a preset high-speed range, the robotic arm joint position and the robotic arm joint speed are input into a pre-constructed high-speed friction force prediction model to obtain a second robotic arm joint friction force, wherein the robotic arm joint speed within the preset low-speed range is less than the robotic arm joint speed within the preset high-speed range.

[0131] In some embodiments, the method for estimating the friction force of a mechanical arm joint may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for estimating the friction force of a mechanical arm joint described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for estimating the friction force of a mechanical arm joint by any other appropriate means (e.g., by means of firmware).

[0132] Various implementations 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), system-on-chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0133] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0134] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0135] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0136] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0137] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0138] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0139] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating friction force of a robot arm joint, characterized in that: include: Get the robot's arm joint position and arm joint speed; If the mechanical arm joint speed is within a preset low-speed range, the mechanical arm joint position and the mechanical arm joint speed are input into a pre-built low-speed friction force prediction model to obtain a first mechanical arm joint friction force; If the mechanical arm joint speed is within a preset high speed range, the mechanical arm joint position and the mechanical arm joint speed are input into a pre-constructed high speed friction force prediction model to obtain a second mechanical arm joint friction force, wherein the mechanical arm joint speed within the preset low speed range is less than the mechanical arm joint speed within the preset high speed range; The low-speed friction prediction model is: ； in, represents the joint position of the robot arm, represents the robot joint speed, represents the threshold velocity of the robot joint, represents the maximum static friction force, n represents the order of the Fourier series; , , , , , , , , and These are the model parameters of the low-speed friction prediction model; Before obtaining the robot's mechanical arm joint position and mechanical arm joint speed, the method further includes: Obtain the joint position, joint speed and motor torque of the robot arm joint under multiple uniform speed motions within a preset high speed range; Determine a high-speed friction force prediction model based on the joint positions, joint velocities, and motor torques of the robotic arm joints at multiple uniform speeds within a preset high-speed range; The high-speed friction prediction model is determined based on the joint position, joint speed and motor torque of the mechanical arm joint under uniform motion at multiple speeds within a preset high-speed range, including: Obtaining an initial high-speed friction prediction model; Constructing a high-speed friction force objective function based on the initial high-speed friction force prediction model; Based on the joint positions, joint velocities and motor torques of the robot arm joints under uniform motion at multiple speeds within a preset high-speed range, the high-speed friction objective function is solved to obtain model parameters of the high-speed friction prediction model; Constructing a high-speed friction force prediction model based on the model parameters of the high-speed friction force prediction model; The high-speed friction prediction model is: ； ； in, represents the joint position of the robot arm, represents the robot joint speed, represents the threshold velocity of the robot joint, represents the maximum static friction force, n represents the order of the Fourier series; , , , , and These are the model parameters of the high-speed friction prediction model.

2. The method according to claim 1, characterized in that Before obtaining the robot's mechanical arm joint position and mechanical arm joint speed, the method further includes: Obtain the joint position, joint speed and motor torque of the robot arm joint under multiple uniform speed motions within a preset low-speed range; A low-speed friction prediction model is determined based on the joint positions, joint speeds and motor torques of the robotic arm joints under uniform motion at multiple speeds within a preset low-speed range.

3. The method according to claim 2, characterized in that The method for determining a low-speed friction prediction model based on the joint positions, joint speeds, and motor torques of the mechanical arm joints at multiple uniform speeds within a preset low-speed range includes: Obtaining an initial low-speed friction prediction model; Constructing a low-speed friction objective function based on the initial low-speed friction prediction model; Based on the joint positions, joint speeds and motor torques of the robot arm joints under uniform motion at multiple speeds within a preset low-speed range, the low-speed friction objective function is solved to obtain model parameters of the low-speed friction prediction model; A low-speed friction force prediction model is constructed based on the model parameters of the low-speed friction force prediction model.

4. A device for estimating friction force of a robot arm joint, characterized in that: The method for estimating the friction force of a mechanical arm joint according to any one of claims 1 to 3 comprises: A robot arm joint data acquisition module is used to obtain the robot's robot arm joint position and robot arm joint speed; A low-speed friction force prediction module, for inputting the mechanical arm joint position and the mechanical arm joint speed into a pre-built low-speed friction force prediction model to obtain a first mechanical arm joint friction force if the mechanical arm joint speed is within a preset low-speed range; A high-speed friction force prediction module is used to input the robot arm joint position and the robot arm joint speed into a pre-constructed high-speed friction force prediction model to obtain a second robot arm joint friction force if the robot arm joint speed is within a preset high-speed range interval, wherein the robot arm joint speed within a preset low-speed range interval is less than the robot arm joint speed within a preset high-speed range interval.

5. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to 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 so that the at least one processor can execute the robot arm joint friction force estimation method described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for estimating the friction force of a robot arm joint according to any one of claims 1 to 3 when executed.

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