Mechanical arm admittance control method, electronic device and computer readable medium

Abstract

Embodiments of the present disclosure disclose a robot arm admittance control method, an electronic device and a computer readable medium. A specific embodiment of the method comprises: obtaining an initial static calibration data information set; performing data preprocessing on the initial static calibration data information set to obtain a static calibration data information set; generating a physical parameter matrix information based on the static calibration data information set; generating an admittance control parameter information based on the static calibration data information set and the physical parameter matrix information; obtaining external force information in real time; in response to determining that the admittance control parameter information and the external force information satisfy a preset threshold condition, determining first preset state information as robot arm state information; in response to determining that the admittance control parameter information and the external force information do not satisfy the preset threshold condition, determining second preset state information as the robot arm state information; and controlling the movement of the robot arm. The embodiment improves the versatility and stability of the robot arm control system.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a mechanical arm admittance control method, an electronic device and a computer readable medium. BACKGROUND

[0002] The mechanical arm admittance control is a control method for controlling the dragging movement of the mechanical arm. The general admittance control method is as follows: obtaining the compensation calibration of the force sensor; setting the admittance control parameters, wherein the used admittance control parameters are usually fixed; and controlling the movement of the mechanical arm by using the set admittance control parameters and the compensation calibration of the force sensor.

[0003] However, when the mechanical arm is controlled by using the above method, the following technical problems often exist:

[0004] The fixed admittance control parameters in the process of admittance control result in low flexibility of the movement of the mechanical arm, and only by manually adjusting the admittance control parameters, the parameters can not be used between different hardware, thereby reducing the versatility and stability of the mechanical arm control system.

[0005] The above information disclosed in the background section is only for the purpose of enhancing the understanding of the background of the present inventive concepts, and therefore, it can include information that does not form the prior art known to those of ordinary skill in the art in the country. SUMMARY

[0006] The summary section is provided to introduce some of the concepts discussed in a simplified form that are further described below in the detailed description. This summary section is not intended to identify key features or essential features of the claimed technology nor is it intended to be used to limit the scope of the claimed technology.

[0007] Some embodiments of the present disclosure provide a mechanical arm admittance control method, an electronic device and a computer readable medium for a mechanical arm, to solve one or more of the technical problems mentioned in the background section.

[0008] In a first aspect, some embodiments of the present disclosure provide a manipulator admittance control method for a manipulator, the method comprising: obtaining an initial static calibration data set; performing data preprocessing on the initial static calibration data set to obtain a static calibration data set; generating a physical parameter matrix based on the static calibration data set; generating an admittance control parameter based on the static calibration data set and the physical parameter matrix; obtaining external force information in real time; determining first preset state information as manipulator state information in response to determining that the admittance control parameter and the external force information satisfy a preset threshold condition; determining second preset state information as manipulator state information in response to determining that the admittance control parameter and the external force information do not satisfy the preset threshold condition; and controlling movement of the manipulator based on the determined manipulator state information, the external force information, and the admittance control parameter.

[0009] In a second aspect, some embodiments of the present disclosure provide an electronic device, comprising: a manipulator; one or more processors; and a storage device having one or more programs stored thereon, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.

[0011] The above embodiments of this disclosure have the following beneficial effects: the robotic arm admittance control method for robotic arms according to some embodiments of this disclosure improves the flexibility of robotic arm movement and the versatility and stability of the robotic arm control system. Specifically, the reason for the low flexibility of robotic arm movement and the low versatility and stability of the robotic arm control system is that the admittance control parameters remain unchanged during the admittance control process, resulting in low flexibility of robotic arm movement. Furthermore, manually adjusting the admittance control parameters may prevent these parameters from being universally applicable across different hardware, thus reducing the versatility and stability of the robotic arm control system. Based on this, the robotic arm admittance control method for robotic arms according to some embodiments of this disclosure first acquires an initial static calibration data information set. This yields initial static calibration data sets for the robotic arm at various robotic arm angles. Then, the initial static calibration data information set is preprocessed to obtain a static calibration data information set. This allows for further data processing to obtain a processed static calibration data information set. Finally, based on the static calibration data information set, physical parameter matrix information is generated. This provides the physical parameter matrix information of the robotic arm. Secondly, based on the aforementioned static calibration data set and physical parameter matrix information, admittance control parameter information is generated. This yields an admittance control information set for controlling the robotic arm's movement. Then, external force information is acquired in real-time. This allows for real-time acquisition of external force information for subsequent admittance control of the robotic arm. Next, in response to determining that the aforementioned admittance control parameter information and the aforementioned external force information satisfy a preset threshold condition, a first preset state information is determined as the robotic arm's state information. This allows for the determination of the robotic arm's state information. Next, in response to determining that the aforementioned admittance control parameter information and the aforementioned external force information do not satisfy the aforementioned preset threshold condition, a second preset state information is determined as the robotic arm's state information. This allows for the determination of the robotic arm's state information. Finally, based on the determined robotic arm state information, the aforementioned external force information, and the aforementioned admittance control parameter information, the robotic arm's movement is controlled. This allows for the control of the robotic arm's movement. Because the admittance control parameters are generated from static calibration data and physical parameter matrix information, the generated admittance control parameters are variable, which can improve the flexibility of the robotic arm's movement. Furthermore, since the robotic arm's movement is controlled by real-time acquired external force information and admittance control parameters, the admittance control parameters will change in real time, thereby improving the versatility and stability of the robotic arm control system. Attached Figure Description

[0012] The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by describing in detail some embodiments thereof with reference to the annexed drawings in which: like reference numerals refer to like elements throughout. The annexed drawings are schematic and are not intended to be drawn to scale.

[0013] Figure 1 a flowchart is shown according to some embodiments of a mechanical arm admittance control method for a mechanical arm in accordance with the present disclosure;

[0014] Figure 2 is a structural schematic diagram of an electronic device suitable for use to implement some embodiments of the present disclosure;

[0015] Figure 3 is a drag schematic diagram of a target person dragging a mechanical arm according to some embodiments of the present disclosure;

[0016] Figure 4 is a flowchart of admittance control of a mechanical arm admittance control method according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described below in greater detail with reference to the drawings. While certain embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be interpreted as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be more thoroughly and completely understood. It should be understood that the drawings of the present disclosure are only for illustrative purposes and should not be construed as limiting the scope of protection of the present disclosure.

[0018] It should also be noted that only parts related to the present application are shown in the drawings for the purpose of description. The embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0019] It should be noted that the terms "first", "second", and the like in the present disclosure are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the terms "one", "multiple" in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that unless otherwise explicitly stated in the context, it should be understood as "one or more".

[0021] The names of the messages or information exchanged between the devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0022] The present disclosure will be described in detail below with reference to the drawings and in conjunction with embodiments.

[0023] Figure 1 A flow 100 of some embodiments of a robot manipulator admittance control method for a robot manipulator according to the present disclosure is shown. The robot manipulator admittance control method comprises the following steps:

[0024] At step 101, a set of initial static calibration data information groups are obtained.

[0025] In some embodiments, a subject (e.g., a computing device) of the robot manipulator admittance control method can obtain a set of initial static calibration data information groups. The robot manipulator can include joint encoders, one or more force sensors, a robot manipulator base, robot manipulator links, and a robot manipulator end effector. The robot manipulator can refer to Figure 3 Each initial static calibration data information group in the set of initial static calibration data information groups can be various data information obtained for characterizing the robot manipulator at different robot manipulator angles. The robot manipulator angle can be angle information of each joint when the robot manipulator end effector and the robot manipulator base have a fixed position in a sensor coordinate system. Each initial static calibration data information in the set of initial static calibration data information can include spatial three-dimensional pose information and force sensor information. The spatial three-dimensional pose information can be digital information for characterizing the three-dimensional spatial pose of each robot manipulator link in the sensor coordinate system. The sensor coordinate system can be a coordinate system established based on the robot manipulator base. The origin, X-axis, Y-axis, and Z-axis of the sensor coordinate system can be pre-set and are not limited herein. The spatial three-dimensional pose information can be obtained by the joint encoders of the robot manipulator. The force sensor information can be digital information obtained by the force sensors included in the robot manipulator end effector. The subject can be a server for managing and scheduling robot manipulator related data information.

[0026] Figure 3 is a drag schematic diagram of a target person dragging a robot manipulator according to some embodiments of the present disclosure. The external force information obtained in real time is external force information generated by the force sensors when the target person drags the robot manipulator end effector. The robot manipulator can include joint encoders, one or more force sensors, a robot manipulator base, and a robot manipulator end effector. Each joint encoder in the joint encoders can be an encoder motor for controlling the rotation of each joint. The one or more force sensors can be force sensors for detecting force. The robot manipulator base can be a base for fixing the robot manipulator. The material of the base can be acrylic or metal. The robot manipulator end effector can be an execution component for physically interacting with the environment.

[0027] In some optional implementations of some embodiments, the execution subject can obtain the initial static calibration data information set by the following steps:

[0028] Firstly, for each of the pre-stored mechanical arm angle information, the following steps are performed:

[0029] Firstly, based on the mechanical arm angle information, the mechanical arm is controlled to rotate to the corresponding angle. Each of the mechanical arm angle information can be a digital signal representing the angle of each joint of the mechanical arm. The mechanical arm angle information can include joint angle information. The joint angle information can be the angle that each joint needs to rotate to. In practice, the execution subject can control each joint of the mechanical arm to rotate to the angle corresponding to the joint angle information included in the mechanical arm angle.

[0030] Secondly, the initial static calibration data information set of the mechanical arm is obtained. Each initial static calibration data information in the initial static calibration data information set includes spatial three-dimensional pose information and force sensor information. In practice, firstly, the execution subject can obtain the spatial three-dimensional pose information from the joint encoders of the mechanical arm. Then, the execution subject can obtain the force sensor information from the force sensor included in the end effector of the mechanical arm. The force sensor information includes three-dimensional force information and three-dimensional torque information. The three-dimensional force information can be used to represent the force in three-dimensional directions obtained by the force sensor. The three-dimensional torque information can be used to represent the torque in three-dimensional directions obtained by the force sensor. The three-dimensional directions can be the X-axis direction, the Y-axis direction and the Z-axis direction in the sensor coordinate system. Then, the execution subject can combine the spatial three-dimensional pose information and the force sensor information into an initial static calibration data information. Finally, the execution subject can determine the obtained multiple initial static calibration data information as an initial static calibration data information set.

[0031] Secondly, the obtained initial static calibration data information set is determined as an initial static calibration data information set.

[0032] Step 102, data preprocessing is performed on the initial static calibration data information set to obtain a static calibration data information set.

[0033] In some embodiments, the execution subject can perform data preprocessing on the initial static calibration data information set to obtain a static calibration data information set. The static calibration data information set can be the initial static calibration data information set after data preprocessing.

[0034] In some optional implementations of some embodiments, the execution subject can perform data preprocessing on the initial static calibration data information set to obtain static calibration data information groups by the following steps:

[0035] Firstly, based on the initial static calibration data information set, the execution subject generates each static average value information. Each static average value information in the static average value information can be the average value of each spatial three-dimensional pose information and each force sensor information included in each initial static calibration data information group in the initial static calibration data information set. Each static average value information in the static average value information includes average spatial three-dimensional pose information and average force sensor information. The average spatial three-dimensional pose information can be the average value of each spatial three-dimensional pose information included in the initial static calibration data information group. The average force sensor information can be the average value of each force sensor information included in the initial static calibration data information group. The average force sensor information includes average three-dimensional force information and average three-dimensional torque information. In practice, firstly, the execution subject can perform the following steps for each initial static calibration data information group in the initial static calibration data information set: determine the spatial three-dimensional pose information included in each initial static calibration data information in the initial static calibration data information group as to-be-averaged spatial three-dimensional pose information to obtain each to-be-averaged spatial three-dimensional pose information. Then, the execution subject can determine the average value of each to-be-averaged spatial three-dimensional pose information as average spatial three-dimensional pose information. Then, the execution subject can determine the force sensor information included in each initial static calibration data information in the initial static calibration data information group as to-be-averaged force sensor information to obtain each to-be-averaged force sensor information. Secondly, the execution subject can determine the average value of each to-be-averaged force sensor information as average force sensor information. Finally, the execution subject can combine the average spatial three-dimensional pose information and the average force sensor information as static average value information.

[0036] In the second step, based on the initial static calibration data information set, each static standard deviation information is generated. Each static standard deviation information can be the standard deviation of each spatial three-dimensional attitude information and each force sensor information included in each initial static calibration data information set. Each static standard deviation information includes spatial three-dimensional attitude standard deviation information and force sensor standard deviation information. The spatial three-dimensional attitude standard deviation information can be the standard deviation of each spatial three-dimensional attitude information included in the initial static calibration data information set. The force sensor standard deviation information can be the standard deviation of each force sensor information included in the initial static calibration data information set. The generation of the spatial three-dimensional attitude standard deviation information and the force sensor standard deviation information can refer to the generation of the average spatial three-dimensional attitude information and the average force sensor information.

[0037] In the third step, the static average value information and the static standard deviation information are integrated to obtain a static calibration data information set. Each static calibration data information in the static calibration data information set includes static average value information and static standard deviation information. In practice, the execution subject can first combine the static average value information and the static standard deviation information generated from the same initial static calibration data information set into a static calibration data information. Then, the execution subject can determine each static calibration data information as a static calibration data information set.

[0038] In step 103, based on the static calibration data information set, a physical parameter matrix information is generated.

[0039] In some embodiments, the execution subject can generate a physical parameter matrix information based on the static calibration data information set. The physical parameter matrix information can be a matrix used to represent each physical attribute parameter of the robot arm. The physical attribute parameters of the robot arm can include but are not limited to parameters representing three-dimensional force bias, parameters representing three-dimensional torque bias, parameters representing gravity, and parameters representing gravity components. As an example, when the parameters representing three-dimensional force bias are "(Fx0, Fy0, Fz0)", the parameters representing three-dimensional torque bias are "(Mx0, My0, Mz0)", the parameters representing gravity are "mg_z", and the parameters representing gravity components are "(mg_z·r_x, mg_z·r_y, mg_z·r_z)", the physical parameter matrix information can be the transpose of [Fx0, Fy0, Fz0, Mx0, My0, Mz0, mg_z, mg_z·r_x, mg_z·r_y, mg_z·r_z]. The physical parameter matrix information can be a 10*1 matrix.

[0040] In some optional implementations of some embodiments, the execution subject can generate the physical parameter matrix information based on the static calibration data information set by the following steps:

[0041] Firstly, for each static calibration data information in the static calibration data information set, the following steps are performed:

[0042] Firstly, generate an initial force matrix based on the average force sensor information included in the static calibration data information. The initial force matrix can be a matrix composed of the average force sensor information included in the static calibration data information. The initial force matrix can be a 6*1 matrix. In practice, the execution subject can combine the average three-dimensional force information and the average three-dimensional torque information included in the average force sensor information into the initial force matrix. As an example: when the average force sensor information is "(Fx, Fy, Fz), (Mx, My, Mz)", the initial force matrix obtained by combination can be the transpose of [Fx, Fy, Fz, Mx, My, Mz].

[0043] Secondly, obtain the angle rotation matrix of the corresponding mechanical arm angle of the static calibration data information. The angle rotation matrix can be the rotation matrix of the mechanical arm end effector in the world coordinate system. In practice, the execution subject can obtain the angle rotation matrix of the corresponding mechanical arm angle of the static calibration data information from the database. The database can be a database for storing mechanical arm related data.

[0044] Thirdly, generate a rotation parameter matrix based on the angle rotation matrix. The rotation parameter matrix can be a processed angle rotation matrix. The rotation parameter matrix can be a 6*10 matrix. In practice, the execution subject can input the angle rotation matrix into a first preset formula to obtain the rotation parameter matrix. The angle rotation matrix can be R i The i-th angle rotation matrix. r 11i The first row and the first column of the i-th angle rotation matrix. r 13i The first row and the third column of the i-th angle rotation matrix. Other parameters are similar. The first preset formula can be X i The i-th rotation parameter matrix. r 31i The third row and the first column of the i-th angle rotation matrix. -r 32i The negative of the third row and the second column of the i-th angle rotation matrix. Other parameters are similar.

[0045] In a second step, the physical parameter matrix information is generated based on the generated initial force matrix and the generated rotation parameter matrix. The physical parameter matrix information can include force sensor bias information, gravity parameter information, and gravity component information. The physical parameter matrix information can be a 10*1 matrix. The force sensor bias information can be the zero drift of the force sensor when the end effector mass is 0. The gravity parameter information can be the gravity of the end effector of the robot arm. The gravity component information can be the torque of the gravity of the end effector of the robot arm in each sensor direction. In practice, first, the execution subject can combine the rotation parameter matrices into a rotation parameter matrix group. Then, the execution subject can combine the initial force matrices into an initial force matrix group. Then, the execution subject can input the rotation parameter matrix group and the initial force matrix group into a second preset formula to obtain the physical parameter matrix information. The second preset formula can be θ = (A T A) -1 A T B. Wherein θ is the physical parameter matrix information. A is the rotation parameter matrix group. A T is the transpose of the rotation parameter matrix group. (A T A) -1 is the inverse of the matrix. B is the initial force matrix group information. As an example: when the rotation parameter matrices are 6*10 matrices and the initial force matrices are 6*1 matrices, there are n rotation parameter matrices and n initial force matrices. The combined rotation parameter matrix group is a 6n*10 matrix. The combined initial force matrix group information is a 6n*1 matrix. Thus, a 10*1 physical parameter matrix information is obtained after the calculation of the second preset formula.

[0046] In step 104, the admittance control parameter information is generated based on the static calibration data information group and the physical parameter matrix information.

[0047] In some embodiments, the execution subject can generate the admittance control parameter information based on the static calibration data information group and the physical parameter matrix information. The admittance control parameter information can be the relevant parameter information for the robot arm admittance control.

[0048] In the process of solving the above technical problems by adopting the technical solutions, the following problems often occur:

[0049] The calibration process and the admittance control process are two different modes of the robot arm state. The admittance control parameters in the admittance control process are irrelevant to the parameters obtained in the calibration process, resulting in low accuracy and universality of the robot arm control.

[0050] In view of the above technical problems, the following solutions are adopted:

[0051] In some optional implementations of some embodiments, the above-mentioned execution subject can generate the admittance control parameter information based on the above-mentioned static calibration data information set and the above-mentioned physical parameter matrix information by the following steps:

[0052] Firstly, the execution subject can generate a force matrix information set based on the above-mentioned physical parameter matrix information and the above-mentioned static calibration data information set. Each force matrix information in the force matrix information set can be analog data information for characterizing a force sensor. In practice, first, the execution subject can execute the following steps on each static calibration data information in the static calibration data information set: input the physical property matrix information corresponding to the static calibration data information and the physical parameter matrix information into a third preset formula to obtain each force matrix information. Then, the execution subject can determine the force matrix information as the force matrix information set. The third preset formula can be D' = C · θ. D' is a force matrix information. C is the physical property matrix information corresponding to a static calibration data information. θ is the physical parameter matrix information.

[0053] Secondly, the execution subject can generate a force deviation information set based on the above-mentioned force matrix information set and the above-mentioned static calibration data information set. Each force deviation information in the force deviation information set can be the difference between the initial force matrix corresponding to the static calibration data information and the force matrix information corresponding to the static calibration data information. In practice, first, the execution subject can execute the following steps on each static calibration data information in the static calibration data information set: the execution subject can determine the difference between the force matrix information corresponding to the static calibration data information and the initial force matrix corresponding to the static calibration data information as the force deviation information. Then, the execution subject can determine each force deviation information as the force deviation information set.

[0054] In the third step, the drag state switching information set is generated based on the force deviation information set. Each piece of the drag state switching information in the drag state switching information set can be threshold information representing the switching between the mechanical arm drag state and the static state in a predetermined direction. The predetermined direction can be the X-axis direction, the Y-axis direction or the Z-axis direction in the sensor coordinate system. In practice, the execution subject can determine the force deviation corresponding to the X-axis direction with the largest absolute value in each piece of the force deviation information in the force deviation information set as the X-axis maximum force deviation. The execution subject can determine the force deviation corresponding to the Y-axis direction with the largest absolute value in each piece of the force deviation information in the force deviation information set as the Y-axis maximum force deviation. The execution subject can determine the force deviation corresponding to the Z-axis direction with the largest absolute value in each piece of the force deviation information in the force deviation information set as the Z-axis maximum force deviation. Then, the execution subject can determine the torque deviation corresponding to the X-axis direction with the largest absolute value in each piece of the force deviation information in the force deviation information set as the X-axis maximum torque deviation. The execution subject can determine the torque deviation corresponding to the Y-axis direction with the largest absolute value in each piece of the force deviation information in the force deviation information set as the Y-axis maximum torque deviation. The execution subject can determine the torque deviation corresponding to the Z-axis direction with the largest absolute value in each piece of the force deviation information in the force deviation information set as the Z-axis maximum torque deviation. Then, the execution subject can combine the X-axis maximum force deviation, the Y-axis maximum force deviation, the Z-axis maximum force deviation, the X-axis maximum torque deviation, the Y-axis maximum torque deviation and the Z-axis maximum torque deviation into the maximum deviation information. Next, the execution subject can input each piece of information in the maximum deviation information into the fourth preset formula to obtain the drag state switching information corresponding to each piece of information. The fourth preset formula can be Fe θ = p·Fe max . Fe θ is the drag state switching information corresponding to Fe max . p is a preset conservative coefficient. Here, the specific value of the conservative coefficient is not limited. Fe max is any one of the X-axis maximum force deviation, the Y-axis maximum force deviation, the Z-axis maximum force deviation, the X-axis maximum torque deviation, the Y-axis maximum torque deviation and the Z-axis maximum torque deviation.

[0055] In the fourth step, the static average value information included in each piece of the static calibration data information in the static calibration data information set is determined as each piece of the to-be-processed average value information.

[0056] In the fifth step, the average force sensor information included in each piece of the to-be-processed average value information is determined as each piece of the initial force sensor information.

[0057] In the sixth step, the end mass parameter information is generated based on the initial force sensor information and the physical parameter matrix information. The end mass parameter information can be analog mass information representing the end effector of the robot arm. In practice, the execution subject can input the initial force sensor information and the physical parameter matrix information into a robot simulation analysis software to obtain the end mass parameter information. The robot simulation analysis software can be Deneb, MATLAB / Simulink, or ADAMS. In practice, the execution subject can determine the quotient of the gravity parameter information included in the physical parameter matrix information and the gravitational acceleration as the end mass parameter information.

[0058] In the seventh step, the maximum damping information is generated based on the end mass parameter information. The maximum damping information can be parameter information representing the maximum damping. In practice, the execution subject can input the end mass parameter information into a fifth preset formula to obtain the maximum damping information. The fifth preset formula can be D max Ds is the maximum damping information. Ds max M is a preset maximum damping value when M is 1 kg. The preset maximum damping value can be a preset value, which is not limited herein. M is the end mass parameter information.

[0059] In the eighth step, the minimum damping information is generated based on the end mass parameter information. The minimum damping information can be parameter information representing the minimum damping. In practice, the execution subject can input the end mass parameter information into a sixth preset formula to obtain the minimum damping information. The sixth preset formula can be D min Ds is the minimum damping information. Ds min M is a preset minimum damping value when M is 1 kg. The preset minimum damping value can be a preset value, which is not limited herein. M is the end mass parameter information.

[0060] In the ninth step, the admittance control parameter information is obtained by integrating the force matrix information set, the force deviation information set, the drag state switching information set, the end mass parameter information, the maximum damping information, and the minimum damping information. The admittance control parameter information can be parameter information for admittance control of the robot arm. In practice, the execution subject can combine the force matrix information set, the force deviation information set, the drag state switching information set, the end mass parameter information, the maximum damping information, and the minimum damping information into the admittance control parameter information.

[0061] The technical scheme and related content above are an inventive point of an embodiment of the present disclosure, and solve the problem that the calibration process and the admittance control process are two different mechanical arm states, and the parameters obtained in the calibration process are irrelevant to the admittance control parameters in the admittance control process, resulting in low accuracy and universality of the mechanical arm control. Factors that result in low accuracy and universality of the mechanical arm control are often as follows: some parameters in the original calibration process and the admittance control process are not associated, and the control process is controlled only by manually adjusting the admittance control parameters, resulting in low accuracy and universality of the mechanical arm control. If the above factors are solved, the accuracy and universality of the mechanical arm control can be improved. To achieve this effect, the present disclosure first generates a force matrix information set based on the physical parameter matrix information and the static calibration data information set. In this way, the force matrix information set corresponding to the static calibration data information set can be obtained. Then, a force deviation information set is generated based on the force matrix information set and the static calibration data information set. In this way, the difference between the force matrix information set and the static calibration data information set can be obtained. Then, a dragging state switching information set is generated based on the force deviation information set. In this way, the threshold information when the mechanical arm switches states can be obtained. Secondly, the static average value information included in each static calibration data information in the static calibration data information set is determined as each to-be-processed average value information. In this way, each to-be-processed average value information can be obtained. Then, the average force sensor information included in each to-be-processed average value information is determined as each initial force sensor information. In this way, each initial force sensor information can be obtained. Then, an end mass parameter information is generated based on each initial force sensor information and the physical parameter matrix information. In this way, the mass information of the end effector can be obtained. Secondly, a maximum damping information is generated based on the end mass parameter information. In this way, the maximum damping information of the mechanical arm can be obtained. Then, a minimum damping information is generated based on the end mass parameter information. In this way, the minimum damping information of the mechanical arm can be obtained. Finally, the force matrix information set, the force deviation information set, the dragging state switching information set, the end mass parameter information, the maximum damping information, and the minimum damping information are integrated to obtain admittance control parameter information. In this way, the admittance control parameter information can be obtained. Because the admittance control parameter information is generated based on the information in the static calibration data information set obtained through the calibration process, the parameters obtained through the calibration process are associated with the admittance control parameters. Because the mechanical arm is controlled based on the associated admittance control parameter information, the accuracy and universality of the mechanical arm control can be improved.

[0062] In step 105, the external force information is acquired in real time.

[0063] In some embodiments, the execution subject can obtain the external force information in real time. The external force information can be digital information obtained by a force sensor included in the end effector of the robot arm. The external force information includes three-dimensional external force information and three-dimensional external torque information. In practice, the execution subject can obtain the external force information in real time through the force sensor of the robot arm.

[0064] In step 106, in response to determining that the admittance control parameter information and the external force information satisfy the preset threshold condition, the first preset state information is determined as the robot arm state information.

[0065] In some embodiments, the execution subject can determine the first preset state information as the robot arm state information in response to determining that the admittance control parameter information and the external force information satisfy the preset threshold condition. The preset threshold condition can be that the three-dimensional external force information and the three-dimensional external torque information included in the external force information are both less than the corresponding element of the drag state switching information in the drag state switching information group included in the admittance control parameter information. The first preset state information can be text information used to represent the motion state of the robot arm. The first preset state information can be “robot arm static state”. The robot arm state information can be text information used to represent the motion state of the robot arm.

[0066] In step 107, in response to determining that the admittance control parameter information and the external force information do not satisfy the preset threshold condition, the second preset state information is determined as the robot arm state information.

[0067] In some embodiments, the execution subject can determine the second preset state information as the robot arm state information in response to determining that the admittance control parameter information and the external force information do not satisfy the preset threshold condition. The second preset state information can be text information used to represent the motion state of the robot arm. The second preset state information can be “robot arm drag state”.

[0068] In step 108, the robot arm motion is controlled based on the determined robot arm state information, external force information, and admittance control parameter information.

[0069] In some embodiments, the execution subject can control the robot arm motion based on the determined robot arm state information, external force information, and admittance control parameter information.

[0070] In some optional implementations of some embodiments, the execution subject can control the robot arm motion based on the determined robot arm state information, external force information, and admittance control parameter information by the following steps:

[0071] First, in response to determining that the robot arm state information satisfies the preset drag condition, the following steps are performed:

[0072] The first sub-step involves generating first damping parameter information based on the aforementioned external force information and admittance control parameter information. The aforementioned preset dragging condition can be that the robotic arm state information is in a "robotic arm dragging state". The aforementioned first damping parameter information can be damping parameters used for the real-time movement of the robotic arm. In practice, the aforementioned execution entity can input the dragging state switching information group, maximum damping information, and minimum damping information included in the aforementioned external force information and admittance control parameter information into the seventh preset formula to obtain the first damping parameter information. The aforementioned seventh preset formula can be... d(F ext ) is F ext The first damping parameter information. D min This provides information on the minimum damping parameters. (D) max This provides information on the maximum damping parameters. `min()` is the minimum value function. F ext Information about external forces. F max The maximum force information for the robotic arm is preset. This preset maximum force information can be pre-set and is not limited here. θ This refers to the drag-and-drop state switching information in the drag-and-drop state switching information group.

[0073] The second sub-step generates first acceleration information based on the aforementioned external force information and the aforementioned first damping parameter information. This first acceleration information can be a Cartesian acceleration characterizing the movement of the robotic arm's end effector. In practice, the actuator can input the aforementioned external force information, the end-effector mass parameter information included in the aforementioned admittance control parameter information, the aforementioned first damping parameter information, and the velocity information from the previous robotic arm cycle into the eighth preset formula to obtain the first acceleration information. The aforementioned robotic arm cycle can be a time period characterizing the robotic arm control frequency. The specific value of the aforementioned robotic arm cycle can be preset and is not limited here. The aforementioned velocity information can be a Cartesian velocity characterizing the movement of the robotic arm's end effector. The aforementioned eighth preset formula can be F... ext =Mx″+D(F ext )x′。 F ext This represents external force information. M represents end-effector mass parameter information. x″ represents the first acceleration information. D(F) ext The external force information is F. ext The first damping parameter information at time. X′ is the velocity information.

[0074] The third sub-step involves generating first velocity information based on the aforementioned first acceleration information. This first velocity information can be a Cartesian velocity characterizing the movement of the robotic arm's end effector. In practice, the actuator can integrate the first acceleration information over one robotic arm cycle to obtain the first velocity information.

[0075] A fourth sub-step, generating each first joint desired speed based on the first speed information. Wherein, the each first joint desired speed can be used to represent the speed of each joint encoder of the robot arm. In practice, the execution subject can input the first speed information into the robot arm simulation analysis software to obtain each first joint desired speed.

[0076] A fifth sub-step, controlling the movement of the robot arm based on the each first joint desired speed. In practice, the execution subject can control each joint of the robot arm to reach each first joint desired speed to control the movement of the robot arm. Wherein, the dragging gesture of the target person dragging the robot arm can refer to Figure 3 . The control flow of the movement of the robot arm can refer to Figure 4 Admittance control flowchart.

[0077] Figure 3 is a dragging gesture of a target person dragging a robot arm according to some embodiments of the present disclosure. Wherein, the external force information obtained in real time is the external force information generated by the force sensor through the target person dragging the robot arm end effector. The robot arm includes each joint encoder, one or more force sensors, a robot arm base, and a robot arm end effector. Wherein, each joint encoder in the each joint encoder can be an encoder motor used to control the rotation of each joint. The one or more force sensors can be force sensors used to detect force. The robot arm base can be a base used to fix the robot arm. The material of the base can be acrylic or metal. The robot arm end effector can be an execution component used to physically interact with the environment.

[0078] Figure 4 is an admittance control flowchart of a mechanical part admittance control method according to some embodiments of the present disclosure. The control flowchart between the robot arm static calibration and the admittance control is shown. Wherein, the static calibration system in the calibration flowchart can be a system for obtaining an initial static calibration data information set. The static calibration system corresponds to Figure 4 “static calibration” in . The control parameters generated by the static calibration system can be sent to the admittance controller. The control parameters can be numerical values used for the control of the admittance controller. The compensation parameters generated by the static calibration system can be sent to the end compensation system. The compensation parameters can be numerical values used for the end compensation system. The end compensation system corresponds to Figure 4 “end compensation” in . In the control flowchart, the target person can drag the robot arm to generate external force, and the dragging of the robot arm will also generate a change in the position of the robot arm end effector included in the robot arm, thereby feeding back to the target person. The target person corresponds to Figure 4"human" in the above description. The robot arm can apply the external force to the force sensor in real time, and the force sensor can generate an external force reading. The external force reading can be a value representing the external force applied to the force sensor. The force sensor can send the generated external force reading to the end compensation system. The robot arm can send real-time speed information of the robot arm to the admittance controller. The end compensation system can receive the external force reading sent by the force sensor and the compensation parameter generated by the static calibration system, and generate a compensated external force. The compensated external force can be a value representing the external force reading after being processed by the end compensation system. The end compensation system can then send the compensated external force to the parameter adaptive system and the admittance controller. The parameter adaptive system corresponds to Figure 4 "parameter adaptive" in the above description. The parameter adaptive system can receive the compensated external force, generate state information and damping information. The state information can be information representing the state of the robot arm. The damping information can be a value representing the damping of the robot arm. The parameter adaptive system can send the state information and the damping information to the admittance controller. The admittance controller can receive the control parameter sent by the static calibration system, the real-time speed information sent by the robot arm, the compensated external force sent by the end compensation system, and the state information and the damping information sent by the parameter adaptive system, and generate desired speed information. The desired speed information can be a value representing the desired speed. The admittance controller can then send the desired speed information to the speed control loop. The speed control loop can receive the desired speed information and generate a control instruction. The control instruction can then control the movement of the robot arm.

[0079] In the process of using the technical solutions to solve the above technical problems, the following problems often occur:

[0080] In the admittance control process, the robot arm enters a stationary state, and the stationary state of the robot arm is still controlled by the joint encoders of the robot arm to maintain the stationary state of the robot arm, resulting in a large power consumption of the joint encoders when the robot arm is in the stationary state.

[0081] In the face of the above technical problems, the following solutions are adopted:

[0082] In some optional implementations of some embodiments, the execution subject can control the movement of the robot arm based on the determined robot arm state information, the external force information, and the admittance control parameter information by the following steps:

[0083] First, in response to determining that the robot arm state information satisfies the preset stationary condition, the following steps are performed:

[0084] A first sub-step, determining preset external force information as the external force information. The preset stationary condition can be that the mechanical arm state information is a "mechanical arm stationary state". The preset external force information can be preset external force information. The specific value of the preset external force information can be preset, which is not limited herein. The preset external force information can be "0".

[0085] A second sub-step, generating second damping parameter information based on the external force information and the admittance control parameter information. The second damping parameter information can be a damping parameter for real-time movement of the mechanical arm. The method of generating the second damping parameter information can refer to the specific implementation of generating the first damping parameter information, which will not be repeated herein.

[0086] A third sub-step, generating second acceleration information based on the external force information and the second damping parameter information. The second acceleration information can be a Cartesian acceleration for representing movement of the end effector of the mechanical arm. The method of generating the second acceleration information can refer to the specific implementation of generating the first acceleration information, which will not be repeated herein.

[0087] A fourth sub-step, generating second speed information based on the second acceleration information. The second speed information can be a Cartesian speed for representing movement of the end effector of the mechanical arm. In practice, the execution subject can integrate the second acceleration information according to one mechanical arm cycle to obtain the second speed information.

[0088] A fifth sub-step, generating each second joint expected speed based on the second speed information. The each second joint expected speed can be a speed for representing each joint encoder of the mechanical arm. In practice, the execution subject can input the second speed information to the mechanical arm simulation analysis software to obtain each second joint expected speed.

[0089] A sixth sub-step, controlling the mechanical arm to stop movement based on the each second joint expected speed. In practice, first, the execution subject can control each joint of the mechanical arm to reach each second joint expected speed and then control the mechanical arm to stop movement, and then, in response to the mechanical arm stopping movement satisfying a preset stop time condition, control the auxiliary stationary device to keep the mechanical arm stationary. The preset stop time condition can be that the time of the mechanical arm stopping movement is greater than a preset stop time. The preset stop time can be a time preset by a target person. For example, the preset stop time can be "1 minute". The auxiliary stationary device can be a physical device for assisting the mechanical arm to be stationary. The auxiliary stationary device can be a locking structure for locking movement of each joint encoder.

[0090] The above technical solutions and related contents are an inventive point of the embodiments of the present disclosure, which solves the problem that in the admittance control process, the mechanical arm is still controlled by the joint encoders of the mechanical arm to maintain the stationary state of the mechanical arm after the mechanical arm enters the stationary state, resulting in a large power consumption of the joint encoders when the mechanical arm maintains the stationary state. The factors that cause the joint encoders to consume a large amount of power when the mechanical arm maintains the stationary state are often as follows: the joint encoders still need to continuously work when the mechanical arm maintains the stationary state. If the above factors are solved, the power consumption of the joint encoders when the mechanical arm maintains the stationary state can be reduced. To achieve this effect, the present disclosure first, in response to determining that the mechanical arm state information meets the preset stationary condition, performs the following steps: determining the preset external force information as the external force information. In this way, the preset external force information can be used as the external force information. Then, based on the external force information and the admittance control parameter information, the second damping parameter information is generated. Then, based on the external force information and the second damping parameter information, the second acceleration information is generated. Secondly, based on the second acceleration information, the second speed information is generated. Then, based on the second speed information, the second joint expected speed is generated. In this way, the second joint expected speed for controlling the mechanical arm to stop moving can be generated by the external force information determined by the preset external force information. Finally, based on the second joint expected speed, the mechanical arm is controlled to stop moving. In this way, the mechanical arm can be controlled to stop moving. Because the second joint expected speed is generated by the preset external force information, the mechanical arm can be controlled to stop moving. Because the auxiliary stationary device is used when the mechanical arm is controlled to stop moving, the power consumption required for the joint encoders of the mechanical arm to maintain work is reduced, and thus the power consumption of the joint encoders of the mechanical arm when the mechanical arm maintains the stationary state can be reduced.

[0091] The above various embodiments of the present disclosure have the following beneficial effects: through the manipulator admittance control method for a manipulator of some embodiments of the present disclosure, the flexibility of the manipulator movement and the versatility and stability of the manipulator control system are improved. Specifically, the reason why the flexibility of the manipulator movement and the versatility and stability of the manipulator control system are low is that the admittance control parameters are fixed in the process of admittance control, which leads to low flexibility of the manipulator movement, and the admittance control parameters can not be used between different hardware through manual adjustment, which leads to low versatility and stability of the manipulator control system. Based on this, the manipulator admittance control method for a manipulator of some embodiments of the present disclosure first acquires an initial static calibration data information set. In this way, the initial static calibration data set of the manipulator at each manipulator angle can be obtained. Then, the initial static calibration data information set is preprocessed to obtain a static calibration data information set. In this way, the initial static calibration data information set can be processed to obtain the processed static calibration data information set. Then, based on the static calibration data information set, a physical parameter matrix information is generated. In this way, the physical parameter matrix information of the manipulator can be obtained. Secondly, based on the static calibration data information set and the physical parameter matrix information, an admittance control parameter information is generated. In this way, the admittance control information set for controlling the movement of the manipulator can be obtained. Then, the external force information is acquired in real time. In this way, the external force information can be acquired in real time for subsequent admittance control of the manipulator. Then, in response to determining that the admittance control parameter information and the external force information satisfy a preset threshold condition, a first preset state information is determined as the manipulator state information. In this way, the state information of the manipulator can be determined. Secondly, in response to determining that the admittance control parameter information and the external force information do not satisfy the preset threshold condition, a second preset state information is determined as the manipulator state information. In this way, the state information of the manipulator can be determined. Finally, based on the determined manipulator state information, the external force information and the admittance control parameter information, the movement of the manipulator is controlled. In this way, the movement of the manipulator can be controlled. Because the admittance control parameter information is generated through the static calibration data information set and the physical parameter matrix information, the generated admittance control parameter information is variable, so the flexibility of the manipulator movement can be improved, and because the movement of the manipulator is controlled through the real-time acquired external force information and the admittance control parameter information, the admittance control parameter will change in real time, thereby improving the versatility and stability of the manipulator control system.

[0092] Reference will now be made to Figure 2 which shows a structural schematic diagram of an electronic device 200 suitable for implementing some embodiments of the present disclosure. Figure 2 The electronic device shown is only an example and should not impose any limitation on the functions and use range of the embodiments of the present disclosure.

[0093] like Figure 2 As shown, electronic device 200 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 201, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 202 or a program loaded from storage device 208 into random access memory (RAM) 203. The RAM 203 also stores various programs and data required for the operation of electronic device 200. The processing device 201, ROM 202, and RAM 203 are interconnected via bus 204. Input / output (I / O) interface 205 is also connected to bus 204.

[0094] Typically, the following devices can be connected to I / O interface 205: input devices 206 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 207 including, for example, robotic arms, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 208 including, for example, magnetic tapes, hard disks, etc.; and communication devices 209. Communication device 209 allows electronic device 200 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 2 An electronic device 200 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 2 Each box shown can represent a device or multiple devices as needed.

[0095] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 209, or installed from storage device 208, or installed from ROM 202. When the computer program is executed by processing device 201, it performs the functions defined above in the methods of some embodiments of this disclosure.

[0096] Note that the computer readable medium in some embodiments of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination thereof. The computer readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program used by an instruction execution system, apparatus or device, or that can be used by or in connection with an instruction execution system, apparatus or device. In some embodiments of the present disclosure, the computer readable signal medium can include a computer readable program code propagated in or on a carrier medium, in which the computer readable program code is embodied. Such propagated computer readable program code can take many forms, including but not limited to, an electromagnetic signal, an optical signal or any suitable combination of the foregoing. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. Program code embodied on a computer readable medium can be transmitted using any suitable medium, including but not limited to, wire, cable, wireless, RF, infrared or any suitable combination of the foregoing.

[0097] In some embodiments, the client, server, or both can communicate using any current known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current known or future developed networks.

[0098] The computer readable medium can be included in the electronic device; or can exist independently of the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: acquire an initial static calibration data information set; perform data preprocessing on the initial static calibration data information set to obtain a static calibration data information set; generate a physical parameter matrix information based on the static calibration data information set; generate admittance control parameter information based on the static calibration data information set and the physical parameter matrix information; acquire external force information in real time; determine first preset state information as the mechanical arm state information in response to determining that the admittance control parameter information and the external force information satisfy a preset threshold condition; determine second preset state information as the mechanical arm state information in response to determining that the admittance control parameter information and the external force information do not satisfy the preset threshold condition; and control movement of the mechanical arm based on the determined mechanical arm state information, the external force information, and the admittance control parameter information.

[0099] Computer program code for carrying out operations of some embodiments of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0100] The computer program product of the first aspect can further include one or more of the following features. The computer program product can include a computer readable medium. The computer readable medium can include a computer readable storage medium or a computer readable signal medium. The computer readable storage medium can include tangible storage medium. The computer readable signal medium can include a propagated data signal with computer readable program code embodied therein. The computer readable program code can be downloaded into a working memory of a computer from the computer readable signal medium or from the computer readable storage medium. The computer readable program code can cause the computer to perform the steps of the first aspect. The computer readable program code can be executed by one or more processors associated with the computer.

[0101] The functions described above can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, non-limiting examples of hardware logic components that can be used include field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SOCs), complex programmable logic devices (CPLDs), etc.

[0102] The above description is merely exemplary of the disclosure and the application of the principles thereof. It is not intended to limit the scope of the disclosure to the precise forms disclosed. The disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the application as defined by the claims. The disclosure can also cover combinations of the features set out above.

Claims

1. A method for controlling the admittance of a robotic arm, comprising: Obtain the initial static calibration data set, including: For each robotic arm angle in the pre-stored robotic arm angle information, perform the following steps: Based on the robotic arm angle information, control the robotic arm to rotate to the corresponding angle; Acquire the initial static calibration data information group of the robotic arm, wherein each initial static calibration data information in the initial static calibration data information group includes spatial three-dimensional posture information and force sensor information; Each of the obtained initial static calibration data information groups is defined as the initial static calibration data information group set; The initial static calibration data information set is preprocessed to obtain the static calibration data information set; Based on the static calibration data information group, physical parameter matrix information is generated; Based on the static calibration data information group and the physical parameter matrix information, admittance control parameter information is generated; Real-time acquisition of external force information; In response to determining that the admittance control parameter information and the external force information meet a preset threshold condition, the first preset state information is determined as the robotic arm state information; In response to determining that the admittance control parameter information and the external force information do not meet the preset threshold condition, the second preset state information is determined as the robotic arm state information; The movement of the robotic arm is controlled based on the determined state information of the robotic arm, the external force information, and the admittance control parameter information.

2. The method according to claim 1, wherein, The step of preprocessing the initial static calibration data information set to obtain the static calibration data information set includes: Based on the initial static calibration data set, various static average values ​​are generated, wherein each static average value includes average spatial three-dimensional attitude information and average force sensor information. Based on the initial static calibration data set, various static standard deviation information is generated, wherein each static standard deviation information includes spatial three-dimensional attitude standard deviation information and force sensor standard deviation information. The static average information and the static standard deviation information are integrated and processed to obtain a static calibration data information group, wherein each static calibration data information in the static calibration data information group includes static average information and static standard deviation information.

3. The method according to claim 2, wherein, The generation of physical parameter matrix information based on the static calibration data information group includes: For each static calibration data information in the static calibration data information group, perform the following steps: Based on the average force sensor information included in the static calibration data, an initial force matrix is ​​generated; Obtain the angle rotation matrix corresponding to the robot arm angles described in the static calibration data; Based on the aforementioned angle rotation matrix, a rotation parameter matrix is ​​generated; Based on the generated initial force matrices and the generated rotation parameter matrices, physical parameter matrix information is generated, wherein the physical parameter matrix information includes force sensor bias information, gravity parameter information, and gravity component information.

4. The method according to claim 1, wherein, The step of controlling the movement of the robotic arm based on the determined robotic arm state information, the external force information, and the admittance control parameter information includes: In response to determining that the robotic arm's state information meets the preset dragging conditions, the following steps are performed: Based on the external force information and the admittance control parameter information, the first damping parameter information is generated; Based on the external force information and the first damping parameter information, first acceleration information is generated; Based on the first acceleration information, first velocity information is generated; Based on the first velocity information, the expected velocity of each first joint is generated; The movement of the robotic arm is controlled based on the desired speed of each of the first joints.

5. An electronic device, comprising: robotic arm; One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 4.

6. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

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