Robot dual-source joint information impedance control method, system, medium and robot

Abstract

The application belongs to the technical field of robot intelligent control, and provides a robot dual-source joint information impedance control method, system, medium and robot, which is oriented to the motion control of a 3R configuration robot, and comprises the following steps: performing signal filtering and differential processing on original motion state data of each joint of the robot to obtain joint motion information including filtered angle, filtered angular velocity and filtered angular acceleration; based on a kinematics model of the robot, mapping pose and / or velocity information of an end effector to a joint space to obtain mapping angle and mapping angular velocity; respectively fusing the angle and the angular velocity to obtain fused angle and fused angular velocity; and generating a control torque by combining the fused angle, the fused angular velocity and the filtered angular acceleration with preset target impedance parameters and an expected trajectory. According to the application, the angular velocity and the angular acceleration can be obtained without adding speed and acceleration sensors on the motor, so that the size, complexity and cost of the joint control motor are avoided.

Description

Technical Field

[0001] This invention belongs to the field of robot intelligent control technology, and relates to a robot dual-source joint information impedance control method, system, medium and robot. Background Technology

[0002] Impedance control, as a core compliant control strategy, has wide applications in various fields such as industrial automation, medical rehabilitation, and human-robot collaboration, especially in scenarios requiring precise force control and compliant interaction. Impedance control adjusts the robot's equivalent mechanical impedance (such as mass, damping, and stiffness) when subjected to external forces, enabling it to exhibit specific dynamic characteristics when interacting with the environment. This achieves more natural, safe, and physically compliant human-robot / robot-environment interaction. This control method is particularly suitable for applications such as medical surgical robots, rehabilitation assistive devices, and collaborative robots, which require precise force feedback and high motion compliance when in contact with the human body or other soft, fragile objects to ensure the safety and effectiveness of operation.

[0003] From a control principle perspective, impedance control essentially achieves torque control based on the motion information (position, velocity, and acceleration) of the robot joints. Specifically, the controller inputs the desired position, velocity, and acceleration of the joint along with the actual measured values. By solving or approximating the joint's dynamic equations, it calculates the required control torque, thereby driving the robotic arm to produce the desired compliant motion behavior.

[0004] Although impedance control is relatively mature in theory and technology, its performance still faces two major challenges in practical engineering applications, mainly focusing on the accurate acquisition of control variables and the adaptability of impedance models.

[0005] (1) Bottlenecks exist in the accurate acquisition of control quantities: To achieve high-performance impedance control, accurate information on the position, velocity, and acceleration of the motor joints must be obtained. However, in practical robot systems, most joints are only equipped with encoders, which can directly and accurately measure joint angles. Angular velocity and angular acceleration usually cannot be directly measured and must be estimated from the angle sequence read from the encoder through numerical differentiation algorithms. Commonly used numerical differentiation methods include forward differential, backward differential, and center differential. However, these methods generally have inherent defects:

[0006] a. Phase deviation problem: For example, although backward differential is causal, it introduces significant phase lag, which causes delay in velocity and acceleration signals, affecting the dynamic response and stability of the control system.

[0007] b. Noise Amplification Issue: Differential operations are extremely sensitive to signal noise, significantly amplifying the inherent quantization noise and high-frequency interference in encoder measurements. This results in extremely low signal-to-noise ratios for the estimated angular velocity and angular acceleration signals, severely degrading control performance. While theoretically this information can be obtained directly by adding additional velocity and acceleration sensors at the joints, this not only increases the size, weight, and cost of the joints but also introduces additional installation errors and sensor calibration complexity, greatly limiting the application of this solution in compact, low-cost robotic systems.

[0008] (2) Poor environmental adaptability of impedance models: The accuracy and effectiveness of impedance control are highly dependent on the accuracy of its model parameters, namely the target inertia matrix in the impedance equation. Damping matrix and stiffness matrix However, in practical applications, accurately measuring or identifying these dynamic parameters is itself a complex and difficult engineering problem. More importantly, real-world application environments are often unknown, time-varying, or difficult to model accurately. Traditional impedance control often employs fixed... , , Static parameter configurations are less adaptable to environmental changes. When a robot encounters sudden events, such as unexpected contact with a rigid surface, falling from the air to the ground, or suddenly penetrating material and losing contact during drilling operations, fixed impedance parameters cannot quickly adapt to such drastic environmental changes. This can easily lead to excessive impact forces, violent oscillations, or even instability in the system, severely affecting task completion and operational safety.

[0009] Furthermore, existing technologies largely rely on a single joint encoder information source, lacking the ability to perceive external disturbances in real time. Therefore, there is an urgent need for a novel impedance control scheme that can estimate joint motion states with high accuracy and low noise, while also enhancing the system's adaptability to unknown and abrupt environments, in order to solve the aforementioned bottleneck problems. Summary of the Invention

[0010] To overcome technical problems such as phase deviation, noise amplification, and poor environmental adaptability in existing technologies, this invention discloses a dual-source joint information impedance control method for robots. This method is designed for motion control of 3R configuration robots. By fusing information from joint encoders and end-effector inertial measurement units (IMUs) and combining polynomial filtering and differentiation techniques, it achieves high-precision, robust, and compliant motion control. It can be widely applied in scenarios requiring precise force control and environmental interaction, such as rehabilitation assistance, collaborative assembly, medical surgery, and flexible operations. Specifically, the method includes the following steps:

[0011] S1. By acquiring the original motion state data of each joint of the robot, the original data is processed by signal filtering and differentiation to obtain the first path of joint motion information, which includes the filtering angle, the filtering angular velocity and the filtering angular acceleration.

[0012] S2. Obtain the pose and / or velocity information of the robot's end effector, and map the pose and / or velocity information to the joint space based on the robot's kinematic model to obtain the second path of joint motion information, which includes the mapped angle and the mapped angular velocity.

[0013] S3. The filtered angle and the mapped angle are weighted and fused to obtain the fused angle, and the filtered angular velocity and the mapped angular velocity are weighted and fused to obtain the fused angular velocity;

[0014] S4. The fusion angle, fusion angular velocity and filter angular acceleration are input to the impedance controller, combined with the preset target impedance parameters and desired trajectory, to generate a control torque and drive the robot to perform compliant motion.

[0015] Further, in step S1, signal filtering and differentiation processing is performed on the original data, including:

[0016] The original data is fitted using a polynomial fitting method based on a sliding window to obtain a fitting function. The filtered angular velocity and filtered angular acceleration in the first path of joint motion information are calculated based on the derivative of the fitting function.

[0017] Furthermore, the polynomial fitting uses the least squares method to perform a second or higher-order polynomial fitting on the original data within the sliding window to generate a smooth fitting function. The first and second derivatives of the fitting function are then used to obtain the filtered angular velocity and filtered angular acceleration at the corresponding time points.

[0018] Furthermore, the pose and / or velocity information is acquired by an external end effector sensor mounted on the robot's end effector, wherein the external end effector sensor is an inertial measurement unit (IMU).

[0019] The kinematic model is based on the improved Denavit-Hartenberg parameters. By solving the inverse kinematic equations, the pose information in the pose and / or velocity information is mapped to the mapped angle. The velocity information in the pose and / or velocity information is mapped to the mapped angular velocity through the inverse operation of the Jacobian matrix.

[0020] Furthermore, the 3R robot is configured as a planar robotic arm with three rotary joints, and the Denavit-Hartenberg parameters include the torsion angle between adjacent links, the link offset, and the link length.

[0021] Furthermore, in step S3, the weighted fusion formula for the fusion angle is:

[0022] ;

[0023] in, From a fusion perspective, For the mapping angle, For the filtering angle, The weighting coefficients for the filtering angle. ;

[0024] The weighted fusion formula for fused angular velocities is:

[0025] ;

[0026] in, To combine angular velocities, For mapped angular velocity, For the filtered angular velocity, The weighting coefficients for the filtered angular velocity are... .

[0027] Furthermore, in step S4, the formula for the control torque is:

[0028] ;

[0029] in, To control the torque, , , These are the target inertia matrix, damping matrix, and stiffness matrix, which are the target impedance parameters. , , These are the filtering angular acceleration, the fusion angular velocity, and the fusion angle, respectively. , , , , and , respectively, represent the target acceleration, target velocity, and target angle in the desired trajectory sent by the host computer.

[0030] This invention also provides a robot dual-source joint information impedance control system, including a sensor module, a filtering and differentiation module, a kinematic mapping module, a fusion module, an impedance controller, and an execution drive module.

[0031] The sensor module includes joint sensors and an end effector external sensor. The joint sensors are used to acquire raw motion state data of each joint, and the end effector external sensor is used to acquire pose and / or velocity information of the robot end effector.

[0032] The filtering and differentiation module is used to perform signal filtering and differentiation processing on the original data to obtain the first path of joint motion information, which includes the filtering angle, filtering angular velocity and filtering angular acceleration.

[0033] The kinematic mapping module is used to map the pose and / or velocity information to the joint space based on the robot's kinematic model to obtain a second path of joint motion information, which includes the mapping angle and the mapping angular velocity.

[0034] The fusion module is used to perform weighted fusion of the filtered angle and the mapped angle to obtain a fused angle, and to perform weighted fusion of the filtered angular velocity and the mapped angular velocity to obtain a fused angular velocity;

[0035] The impedance controller is used to generate a control torque based on the fusion angle, the fusion angular velocity, and the filtering angular acceleration, combined with preset target impedance parameters and desired trajectory.

[0036] The execution drive module is used to drive the robot to perform compliant motion according to the control torque.

[0037] This invention also provides a robot, including the aforementioned control system, for application scenarios involving physical interaction with unknown or real-time changing environments. The robot is a 3R configuration robot, including but not limited to rehabilitation assistive robots, collaborative assembly robots, medical surgical robots, and flexible grinding robots.

[0038] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned robot dual-source joint information impedance control methods to overcome technical problems such as phase deviation, noise amplification, and poor environmental adaptability in the prior art.

[0039] This invention also provides a computer-readable storage medium storing a computer program that executes any of the above-described robot dual-source joint information impedance control methods, in order to overcome technical problems such as phase deviation, noise amplification, and poor environmental adaptability in the prior art.

[0040] Compared with the prior art, the beneficial effects that the robot dual-source joint information impedance control method of the present invention can achieve include at least the following:

[0041] 1. A sliding window-based polynomial fitting differentiator is used to synchronously realize angle filtering and angular velocity / acceleration calculation from encoder data, eliminating the phase lag of traditional numerical differentiation, suppressing noise amplification, and obtaining accurate first-path joint motion information as "joint source" information, which greatly improves the accuracy of motion state estimation.

[0042] 2. By installing an IMU on the robot's end effector, changes in end-effector pose can be sensed in real time. It can also sense disturbances such as collisions and sudden load changes. The information is quickly fed back to the joint controller through kinematic mapping, forming a second path of joint motion information as an "end-effector source" to compensate for encoder information delay and significantly improve the system's adaptability to unknown / sudden environments.

[0043] 3. The angular velocity and acceleration information of the "joint source" and the "end source" are weighted and fused to obtain the fused angle and fused angular velocity, respectively. The angular acceleration is not fused. Under the condition of balancing control stability and adaptability to complex environments, the computational burden is reduced, and a balance between stability and robustness is achieved.

[0044] 4. The method of the present invention is applicable to robots with various 3R configurations and similar configurations, and can be extended to systems with more degrees of freedom. It has broad application prospects in scenarios with high safety requirements such as rehabilitation, collaboration, and medical treatment.

[0045] In summary, the method of the present invention can obtain accurate angular velocity and angular acceleration without installing speed and acceleration sensors on the motor, thus avoiding increasing the size, complexity, and cost of the joint control motor. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart of the robot dual-source joint information impedance control method of the present invention;

[0048] Figure 2 This is a schematic diagram of the principle of the robot dual-source joint information impedance control method of the present invention;

[0049] Figure 3 The process involves signal filtering and differentiation of the acquired raw motion data;

[0050] Figure 4 This is a schematic diagram of the configuration of a 3R robot;

[0051] Figure 5 This is an architecture diagram of the robot dual-source joint information impedance control system of the present invention;

[0052] Among them, 501 is the sensor module; 502 is the filtering and differentiation module; 503 is the kinematic mapping module; 504 is the fusion module; 505 is the impedance controller; and 506 is the execution drive module. Detailed Implementation

[0053] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0054] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features of the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] This invention discloses a dual-source joint information impedance control method for robots. This method is designed for motion control of 3R configuration robots. By fusing information from joint encoders and the end-effector inertial measurement unit (IMU), and combining polynomial filtering and differentiation techniques, it achieves high-precision, robust, and compliant motion control. (See also...) Figure 1 and Figure 2 As shown, the method includes the following steps:

[0056] S1. By acquiring the original motion state data of each joint of the robot, the original data is processed by signal filtering and differentiation to obtain the first path of joint motion information, which includes the filtering angle, the filtering angular velocity and the filtering angular acceleration.

[0057] S2. Obtain the pose and / or velocity information of the robot's end effector, and map the pose and / or velocity information to the joint space based on the robot's kinematic model to obtain the second path of joint motion information, which includes the mapped angle and the mapped angular velocity.

[0058] S3. The filtered angle and the mapped angle are weighted and fused to obtain the fused angle, and the filtered angular velocity and the mapped angular velocity are weighted and fused to obtain the fused angular velocity;

[0059] S4. The fusion angle, fusion angular velocity and filter angular acceleration are input to the impedance controller, combined with the preset target impedance parameters and desired trajectory, to generate a control torque and drive the robot to perform compliant motion.

[0060] Further, in step S1, signal filtering and differentiation processing is performed on the original data, including:

[0061] The original data is fitted using a polynomial fitting method based on a sliding window to obtain a fitting function. The filtered angular velocity and filtered angular acceleration in the first path of joint motion information are calculated based on the derivative of the fitting function.

[0062] Furthermore, the polynomial fitting uses the least squares method to perform a second or higher-order polynomial fitting on the original data within the sliding window to generate a smooth fitting function. The first and second derivatives of the fitting function are then used to obtain the filtered angular velocity and filtered angular acceleration at the corresponding time points.

[0063] When implementing, see Figure 3 The following diagram illustrates the processing of the original data within the filtering window using a quadratic fitting filtering method:

[0064] 1. Time series are represented as: , representing discrete time points; the corresponding angle sequence is represented as The angle measurement value corresponding to each time point.

[0065] 2. Fit the time-angle data within the sliding window. For example, in implementation, choose a sliding window length of 4. Polynomial fitting was performed on the four data points.

[0066] Taking a quadratic polynomial as an example, the equation of the fitting function is:

[0067] The coefficients of a polynomial can be determined using the least squares method. The value of , where, Let be the filtering angle at time t.

[0068] 3. Perform filtering and differentiation operations, and use the fitting results. Replace the original center point This generates a smoothed filtered angle sequence. Taking the derivative of the above fitting function yields the angular velocity equation:

[0069] Take the center point value at Generate a filtered angular velocity sequence, where, Let t be the filtered angular velocity at time t.

[0070] Based on the above process, the filtered angle after filtering at the joint source end can be obtained. and filtered angular velocity Repeat the same operation to check the filtered angular velocity. The filtered angular acceleration can be obtained through processing. .

[0071] Furthermore, such as Figure 4 As shown, the 3R robot is configured as a planar manipulator with three rotary joints. An external sensor, such as an inertial measurement unit (IMU), is installed on the robot's end effector to collect its pose and / or velocity information. By performing inverse kinematics equations and inverse Jacobian matrices on this pose and / or velocity information, respectively, mapping angles and angular velocities obtained from the end effector source to the joints can be obtained.

[0072] In practice, the kinematic model is established based on the improved Denavit-Hartenberg parameters, which include the torsion angle between adjacent links, the link offset, and the link length. The values ​​of these parameters are shown in Table 1 below:

[0073] Table 1: Denavit-Hartenberg (DH) Parameters

[0074]

[0075] in, These are the four parameters of the DH transformation matrix, which describes the positional relationship between the two coordinate systems of the robotic arm. These are the lengths of links 2 and 3 on the robotic arm, respectively. These refer to the angles of rotation of each joint.

[0076] The above DH parameters can be substituted into the rotation matrix solving equation to obtain the coordinate transformation matrices at each level. The transformation matrix represents the transformation from the i-th coordinate system to the (i+1)-th coordinate system.

[0077] ;

[0078] By substituting the parameters from the DH parameter table, we can obtain the following: Figure 4 The transformation matrices shown are for coordinate system 0 to coordinate system 1, coordinate system 1 to coordinate system 2, and coordinate system 2 to coordinate system 3. , , The coordinates in coordinate system 0, coordinate system 1, coordinate system 2, and coordinate system 3 are represented as (x0, y0, z0), (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3), respectively.

[0079] ;

[0080] ;

[0081] ;

[0082] in, , , They are respectively , , , , , express , .

[0083] The coordinate transformation matrix from the base to the end is:

[0084] ;

[0085] The following can be obtained by substituting the three coordinate transformation matrices and multiplying them together:

[0086] ;

[0087] in, express , express .

[0088] in, The first three elements of the fourth column represent the coordinates of the end position in the base coordinate system, i.e.

[0089] ;

[0090] in, , , This represents the pose coordinates of the robot's end effector, which are obtained from measurements by the end-effector IMU.

[0091] By reversibly solving the system of equations, the angle mapping angles of the three joints are respectively... , , .

[0092] In the process of determining joint velocities from the velocity of the robot's end effector, the inverse operation of the Jacobian matrix is ​​often used, derived from the velocity mapping formula:

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] in, , , , These are the velocity components of the terminal velocity along the x-axis, y-axis, and z-axis, respectively. This represents the angular velocity vector mapped from the end-effector velocity to the joint. , , These represent the angular velocities of the three joints, respectively. The Jacobian matrix describes the mapping relationship between end velocity and joint angular velocity.

[0098] The mapping from end-effector velocity to joint velocity can then be expressed as:

[0099] .

[0100] Furthermore, in step S3, the weighted fusion formula for the fusion angle is:

[0101] ;

[0102] in, From a fusion perspective, For the mapping angle, For the filtering angle, The weighting coefficients for the filtering angle. ;

[0103] The weighted fusion formula for fused angular velocities is:

[0104] ;

[0105] in, To combine angular velocities, For mapped angular velocity, For the filtered angular velocity, The weighting coefficients for the filtered angular velocity are... .

[0106] During the fusion process, adjustments can be made. , Different adjustment effects can be achieved. The larger the coefficient, the more stable the control of the robotic arm, but the lower its ability to cope with complex environments. The smaller the coefficient, the lower the control stability, but the higher its ability to cope with complex environments.

[0107] Furthermore, in step S4, the formula for the control torque is:

[0108] ;

[0109] in, To control the torque, , , These are the target inertia matrix, damping matrix, and stiffness matrix, which are the target impedance parameters. , , These are the filtering angular acceleration, the fusion angular velocity, and the fusion angle, respectively. , , , , and , respectively, represent the target acceleration, target velocity, and target angle in the desired trajectory sent by the host computer. The calculated output torque... Used for robot motion control. It is worth noting that for acceleration... Since the mapping of end-effector acceleration to joint acceleration is quite complex, introducing it would place a significant burden on the system's computing power. Therefore, the control only uses the position and velocity information of the end-effector source mapped to the joint.

[0110] Based on the same inventive concept, this invention also provides a robot dual-source joint information impedance control system, as described in the following embodiments. Since the principle of the robot dual-source joint information impedance control system in solving the problem is similar to the robot dual-source joint information impedance control method disclosed in the above embodiments, the implementation of the robot dual-source joint information impedance control system can refer to the implementation of the robot dual-source joint information impedance control method disclosed in the above embodiments, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0111] Figure 5 This is a structural block diagram of a robot dual-source joint information impedance control system disclosed in an embodiment of the present invention, as shown below. Figure 5 As shown, the system includes a sensor module 501, a filtering and differentiation module 502, a kinematic mapping module 503, a fusion module 504, an impedance controller 505, and an execution drive module 506. The structure is described below.

[0112] The sensor module 501 includes joint sensors and an end effector external sensor. The joint sensors are used to acquire raw motion state data of each joint, and the end effector external sensor is used to acquire pose and / or velocity information of the robot end effector.

[0113] The filtering and differentiation module 502 is used to perform signal filtering and differentiation processing on the original data to obtain the first path of joint motion information, which includes the filtering angle, filtering angular velocity and filtering angular acceleration.

[0114] The kinematic mapping module 503 is used to map the pose and / or velocity information to the joint space based on the robot's kinematic model to obtain a second path of joint motion information, which includes the mapping angle and the mapping angular velocity.

[0115] The fusion module 504 is used to perform weighted fusion of the filtered angle and the mapped angle to obtain a fused angle, and to perform weighted fusion of the filtered angular velocity and the mapped angular velocity to obtain a fused angular velocity;

[0116] The impedance controller 505 is used to generate a control torque based on the fusion angle, the fusion angular velocity, and the filtering angular acceleration, combined with preset target impedance parameters and desired trajectory;

[0117] The execution drive module 506 is used to drive the robot to perform compliant motion according to the control torque.

[0118] 1. A sliding window-based polynomial fitting differentiator is used to synchronously realize angle filtering and angular velocity / acceleration calculation from encoder data, eliminating the phase lag of traditional numerical differentiation, suppressing noise amplification, and obtaining accurate first-path joint motion information as "joint source" information, which greatly improves the accuracy of motion state estimation.

[0119] 2. By installing an IMU on the robot's end effector, changes in end-effector pose can be sensed in real time. It can also sense disturbances such as collisions and sudden load changes. The information is quickly fed back to the joint controller through kinematic mapping, forming a second path of joint motion information as an "end-effector source" to compensate for encoder information delay and significantly improve the system's adaptability to unknown / sudden environments.

[0120] 3. The angular velocity and acceleration information of the "joint source" and the "end source" are weighted and fused to obtain the fused angle and fused angular velocity, respectively. The angular acceleration is not fused. Under the condition of balancing control stability and adaptability to complex environments, the computational burden is reduced, and a balance between stability and robustness is achieved.

[0121] 4. The method of the present invention is applicable to robots with various 3R configurations and similar configurations, and can be extended to systems with more degrees of freedom. It has broad application prospects in scenarios with high safety requirements such as rehabilitation, collaboration, and medical treatment.

[0122] In summary, the method of the present invention can obtain accurate angular velocity and angular acceleration without installing speed and acceleration sensors on the motor, thus avoiding increasing the size, complexity, and cost of the joint control motor.

[0123] This invention also provides a robot, including the aforementioned control system, for application scenarios involving physical interaction with unknown or real-time changing environments. The robot is a 3R configuration robot, including but not limited to rehabilitation assistive robots, collaborative assembly robots, medical surgical robots, and flexible grinding robots.

[0124] In this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-described robot dual-source joint information impedance control methods.

[0125] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0126] In this embodiment, a computer-readable storage medium is provided, which stores a computer program that executes any of the above-described robot dual-source joint information impedance control methods.

[0127] Specifically, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0128] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0129] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A robot dual-source joint information impedance control method, characterized by, Motion control for a 3R configuration robot, the method comprising: By acquiring the raw motion state data of each joint of the robot, the raw data is processed by signal filtering and differentiation to obtain the first path of joint motion information, which includes the filtering angle, the filtering angular velocity and the filtering angular acceleration. Acquire the pose and / or velocity information of the robot's end effector, and map the pose and / or velocity information to the joint space based on the robot's kinematic model to obtain a second path of joint motion information, which includes the mapped angle and the mapped angular velocity. The filtered angle and the mapped angle are weighted and fused to obtain the fused angle, and the filtered angular velocity and the mapped angular velocity are weighted and fused to obtain the fused angular velocity; The fusion angle, the fusion angular velocity, and the filter angular acceleration are input to the impedance controller, which, in conjunction with the preset target impedance parameters and the desired trajectory, generates a control torque and drives the robot to perform compliant motion. The weighted fusion formula from the fusion perspective is: ； wherein, is a fusion angle, is a mapping angle, is a filter angle, is a weight coefficient of the filter angle, ; The weighted fusion formula for fused angular velocities is: ； wherein, is a fusion angular velocity, is a mapping angular velocity, is a filtered angular velocity, is a weight coefficient of the filtered angular velocity, ; The formula for the control torque is: ； wherein, is a control torque, , , are a target inertia matrix, a target damping matrix, a target stiffness matrix in the target impedance parameters, respectively, , , are a filtered angular acceleration, a fused angular velocity and a fused angle, respectively, , , are a target acceleration, a target velocity and a target angle in the desired trajectory issued by the host computer, respectively.

2. The robot dual-source joint information impedance control method according to claim 1, characterized in that, The original data undergoes signal filtering and differentiation processing, including: The original data is fitted using a polynomial fitting method based on a sliding window to obtain a fitting function. The filtered angular velocity and filtered angular acceleration in the first path of joint motion information are calculated based on the derivative of the fitting function.

3. The robot dual-source joint information impedance control method according to claim 2, characterized in that, Polynomial fitting uses the least squares method to perform second-order or higher-order polynomial fitting on the original data within the sliding window to generate a smooth fitting function. The first and second derivatives of the fitting function are then used to obtain the filtered angular velocity and filtered angular acceleration at the corresponding time points.

4. The robot dual-source joint information impedance control method according to claim 1, characterized in that, The pose and / or velocity information is acquired by an external end-effector sensor mounted on the robot's end effector, wherein the external end-effector sensor is an inertial measurement unit (IMU). The kinematic model is based on the improved Denavit-Hartenberg parameters. By solving the inverse kinematic equations, the pose information in the pose and / or velocity information is mapped to the mapped angle. The velocity information in the pose and / or velocity information is mapped to the mapped angular velocity through the inverse operation of the Jacobian matrix.

5. The robot dual-source joint information impedance control method according to claim 4, characterized in that, The 3R configuration robot is a planar robotic arm with three rotary joints. The Denavit-Hartenberg parameters include the torsion angle between adjacent links, the link offset, and the link length.

6. A robot dual-source joint information impedance control system, characterized in that, The control system is used to implement the method as described in any one of claims 1 to 5, and the control system includes: The sensor module includes joint sensors and an end-effector sensor. The joint sensors are used to acquire raw motion state data of each joint, and the end-effector sensor is used to acquire pose and / or velocity information of the robot's end effector. The filtering and differentiation module is used to perform signal filtering and differentiation processing on the original data to obtain the first path of joint motion information, which includes the filtering angle, filtering angular velocity and filtering angular acceleration. The kinematic mapping module is used to map the pose and / or velocity information to the joint space based on the robot's kinematic model to obtain a second path of joint motion information, which includes the mapping angle and the mapping angular velocity. The fusion module is used to perform weighted fusion of the filtered angle and the mapped angle to obtain a fused angle, and to perform weighted fusion of the filtered angular velocity and the mapped angular velocity to obtain a fused angular velocity; An impedance controller is used to generate a control torque based on the fusion angle, the fusion angular velocity, and the filtering angular acceleration, combined with preset target impedance parameters and desired trajectory. The execution drive module is used to drive the robot to perform compliant motion according to the control torque.

7. A robot, characterized in that, The system includes the control system as described in claim 6, for application scenarios involving physical interaction with unknown or real-time changing environments. The robot is a 3R configuration robot, including but not limited to rehabilitation assistive robots, collaborative assembly robots, medical surgical robots, and flexible grinding robots.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the robot dual-source joint information impedance control method as described in any one of claims 1 to 5.

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