Collaborative robot motion control method and device and electronic equipment

Abstract

The invention provides a collaborative robot motion control method and device and electronic equipment, in the process of executing assembly, a contact model between an assembly tool and an assembly body is constructed, a state variable is determined based on the contact model, and a system state equation is established based on the state variable. Extended Kalman filtering processing is performed based on the system state equation to obtain estimated environmental stiffness. And whether the estimated environment rigidity exceeds a preset threshold value or not is detected, and if the estimated environment rigidity exceeds the preset threshold value, the collaborative robot is switched to a torque mode. Damping in the admittance control algorithm is adjusted based on the estimated environment rigidity and the force sensor data, and assembling between the assembling tool and the assembling body is executed based on the adjusted admittance control algorithm. According to the scheme, under the condition that a sensor does not need to be additionally arranged or equipment does not need to be replaced, when the estimated environment rigidity exceeds the preset threshold value, the collaborative robot is switched to the torque mode, the damping parameters are adjusted in a self-adaptive mode, and therefore the oscillation phenomenon is avoided.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, and more specifically, to a method, apparatus, and electronic device for motion control of collaborative robots. Background Technology

[0002] Collaborative robots, with their core advantages of high safety and low barrier to entry, are gradually entering the industrial, educational, and healthcare markets. In the healthcare sector, some patients often require assistance from medical staff during recovery. The long recovery period and the significant manpower and effort required during this process present a challenge. For example, during the recovery process of a leg fracture, nurses need to support the patient's leg for training. Since an adult's leg weighs approximately 15-20 kg, this requires considerable physical strength from nurses during rehabilitation training.

[0003] Collaborative robots not only meet the relevant human-robot collaboration requirements, but also have low inertia, making them suitable for applications such as human massage and limb rehabilitation. Furthermore, by incorporating external six-dimensional force sensors, impedance and admittance control algorithms can be quickly deployed to achieve safe human-robot interaction.

[0004] Taking limb rehabilitation as an example, the first step is to install a protective gear on the patient's limb, and simultaneously install the corresponding protective gear connector at the robot's end flange. Then, the robot is dragged to complete the installation of the protective gear and connector, after which rehabilitation training begins. Currently, the common practice is to use a six-dimensional force sensor and a joint torque sensor, employing admittance control algorithms or impedance control algorithms for position and force control.

[0005] In free space, the admittance control algorithm makes the robot compliant based on the external force applied by the operator, making dragging and teaching relatively easy. However, when using the admittance control algorithm to install protective gear, the robot's protective gear connector and the protective gear form a closed loop. During dragging and teaching, the robot is in position mode. When the sensors are subjected to external force, the admittance control algorithm issues a position command, and the robot moves. When the protective gear connector and the protective gear are connected, the position command issued by the admittance control algorithm makes the robot continue to move forward, but due to the rigidity of the connection between the protective gear, it cannot move forward. At this point, the sensors sense a rebound force, and the admittance control algorithm controls the robot to retract. The operator then tries to control the robot to continue downward, but due to the inherent limitations of the algorithm, the connection is destined to fail. This cycle repeats, causing the robot to oscillate during installation. Moreover, the greater the stiffness of the external environment, the greater the amplitude of the robot's oscillation.

[0006] To address these issues, existing technologies typically employ either adding two six-dimensional force sensors or changing the connector to a soft contact. However, these methods require the introduction of additional sensors or the replacement of equipment, increasing implementation costs and making them unsuitable for various scenarios. Summary of the Invention

[0007] The purpose of this invention is to provide a motion control method, device, and electronic device for collaborative robots to ensure the stability of the assembly process and avoid oscillations.

[0008] In a first aspect, the present invention provides a motion control method for a collaborative robot, wherein a force sensor and an assembly tool are installed at the end effector of the collaborative robot, the method comprising: During the assembly process, a contact model between the assembly tool and the assembly body is constructed. The contact model includes environmental damping, environmental stiffness, force sensor data, and the posture offset of the collaborative robot end effector. The state variables are determined based on the contact model, and the system state equations are established based on the state variables. Based on the system state equations, extended Kalman filtering is performed to obtain the estimated environmental stiffness; Detect whether the estimated environmental stiffness exceeds a preset threshold. If the estimated environmental stiffness exceeds the preset threshold, switch the collaborative robot to torque mode. The damping in the admittance control algorithm is adjusted based on the estimated environmental stiffness and force sensor data, and the assembly between the assembly tool and the assembly body is executed based on the adjusted admittance control algorithm.

[0009] In an optional implementation, the step of performing extended Kalman filtering based on the system state equations to obtain estimated environmental stiffness and estimated environmental damping includes: The Jacobian matrix before and after the update is obtained based on the system state equations before and after the update; The updated covariance is obtained from the Jacobian matrix before and after the update; The estimated environmental stiffness and estimated environmental damping are obtained based on the updated covariance.

[0010] In an optional implementation, the step of obtaining the updated covariance based on the Jacobian matrix before and after the update includes: The predicted covariance is obtained based on the Jacobian matrix before the update, the state estimation covariance, and the process noise. The updated covariance is obtained based on the predicted covariance and the updated Jacobian matrix.

[0011] In an optional implementation, the method further includes: Obtain the force sensor data collected by the force sensor, and perform sampling and statistics on the force sensor data; The root mean square error is calculated based on the statistical results. Detect whether the root mean square error changes abruptly. If the root mean square error changes abruptly, then execute the step of switching the collaborative robot to torque mode.

[0012] In an optional implementation, the method further includes: The system detects whether the collaborative robot shakes within a set time period and whether the shaking frequency exceeds a preset frequency. If shaking occurs and the shaking frequency exceeds the preset frequency, then the step of switching the collaborative robot to torque mode is executed.

[0013] In an optional implementation, system model error and measurement noise are introduced into the system state equation.

[0014] In an optional implementation, the method further includes: During the recovery training process, the joint rotation information of the collaborative robot is obtained, and it is determined whether the joint rotation information meets the preset constraints. If the joint rotation information meets the preset constraints, the force sensor collects the interaction force between the collaborative robot and the human body. The Cartesian position command is generated based on the interaction force, and the joint angles of each joint of the collaborative robot are obtained based on the position command. The recovery training process is performed based on the joint angle.

[0015] In an optional implementation, the method further includes: During the recovery training process, the torque values ​​of the force sensor in each direction are monitored; Performance indicators are calculated based on torque values ​​in each direction and using a sliding window statistical algorithm. If the performance index continues to increase within a preset time period, the collaborative robot will be controlled to stop moving.

[0016] Secondly, the present invention provides a motion control device for a collaborative robot, wherein a force sensor and an assembly tool are installed at the end effector of the collaborative robot, and the device includes: A construction module is used to build a contact model between the assembly tool and the assembly body during the assembly process. The contact model includes environmental damping, environmental stiffness, force sensor data, and collaborative robot end-effector posture offset. A module is established to determine state variables based on the contact model and to establish system state equations based on the state variables; The processing module is used to perform extended Kalman filtering based on the system state equation to obtain the estimated environmental stiffness; The detection module is used to detect whether the estimated environmental stiffness exceeds a preset threshold. A switching module is used to switch the collaborative robot to torque mode when the estimated environmental stiffness exceeds a preset threshold. The control module is used to adjust the damping in the admittance control algorithm based on the estimated environmental stiffness and force sensor data, and to perform the assembly between the assembly tool and the assembly body based on the adjusted admittance control algorithm.

[0017] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores computer-readable instructions, which, when executed by the processor, cause the processor to perform the method as described in any of the foregoing embodiments.

[0018] This invention provides a motion control method, device, and electronic device for collaborative robots. During assembly, a contact model between the assembly tool and the assembly body is constructed. State variables are determined based on the contact model, and a system state equation is established based on these state variables. Extended Kalman filtering is performed based on the system state equation to obtain an estimated environmental stiffness. It is then checked whether the estimated environmental stiffness exceeds a preset threshold. If it does, the collaborative robot is switched to torque mode. The damping in the admittance control algorithm is adjusted based on the estimated environmental stiffness and force sensor data. The assembly between the assembly tool and the assembly body is then performed based on the adjusted admittance control algorithm. This solution avoids oscillations by switching the collaborative robot to torque mode and adaptively adjusting the damping parameters when the estimated environmental stiffness exceeds a preset threshold, without requiring additional sensors or equipment replacement. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a collaborative robot motion control method provided in an embodiment of the present invention; Figure 2 A schematic diagram showing the installation of the robot's end effector and sensors; Figure 3 for Figure 1 A flowchart of the sub-steps included in S13; Figure 4 A flowchart of RMS detection in the collaborative robot motion control method provided in this embodiment of the invention; Figure 5 This is a flowchart of zero-crossing detection in the collaborative robot motion control method provided in an embodiment of the present invention; Figure 6 A flowchart illustrating the recovery of training process control in the collaborative robot motion control method provided in this embodiment of the invention; Figure 7 A schematic diagram of a collaborative robot with multiple axes; Figure 8 This is a functional block diagram of the collaborative robot motion control device provided in an embodiment of the present invention; Figure 9 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0022] Please see Figure 1 The following is a flowchart of a collaborative robot motion control method provided in an embodiment of the present invention. The detailed steps of the collaborative robot motion control method are described below.

[0023] S11, During the assembly process, a contact model between the assembly tool and the assembly body is constructed. The contact model includes environmental damping, environmental stiffness, force sensor data, and the posture offset of the collaborative robot end effector. S12, determine the state variables based on the contact model, and establish the system state equations based on the state variables; S13, Perform extended Kalman filtering based on the system state equation to obtain the estimated environmental stiffness; S14, Detect whether the estimated environmental stiffness exceeds a preset threshold. If the estimated environmental stiffness exceeds the preset threshold, execute the following step S15. S15, switch the collaborative robot to torque mode; S16, Based on the estimated environmental stiffness and force sensor data, the damping in the admittance control algorithm is adjusted, and the assembly between the assembly tool and the assembly body is executed based on the adjusted admittance control algorithm.

[0024] In this embodiment, the collaborative robot can be a six-axis collaborative robot, with a force sensor and assembly tools installed at its end effector. For example... Figure 2 As shown, the collaborative robot is first enabled, and a six-dimensional force sensor is installed on the robot's end flange. Furthermore, the orientation of the six-dimensional force sensor is kept opposite to the orientation of the end flange. Based on this, real-time force sensor data detected by the six-dimensional force sensor can be obtained. .

[0025] During assembly, the collaborative robot, driven by a human, moves its assembly tool toward the assembly and into contact with it. Force sensors collect force data in real time during this contact process. The contact model between the assembly tool at the end effector of the collaborative robot and the assembly can be constructed as follows:

[0026] In the formula: For environmental damping; Environmental stiffness is the variable used for estimation. Force sensor data; This refers to the end-effector posture offset of the collaborative robot.

[0027] In addition, the system state equations are established, taking into account system model errors in the process. and measuring noise The state variables associated with the contact model can be determined, for Based on the state variables, system model error, and measurement noise, the final system state equation is as follows:

[0028] Based on this, extended Kalman filtering is performed on the system state equations to obtain estimated environmental stiffness and estimated environmental damping. For details, please refer to [link to relevant documentation]. Figure 3 This can be achieved in the following ways: S131, Based on the system state equations before and after the update, obtain the Jacobian matrix before and after the update; S132, obtain the updated covariance based on the Jacobian matrix before and after the update; S133, based on the updated covariance, the estimated environmental stiffness and estimated environmental damping are obtained.

[0029] The determination of the aforementioned updated covariance can be achieved in the following way: The predicted covariance is obtained based on the unupdated Jacobian matrix, the state estimation covariance, and the process noise; the updated covariance is obtained based on the predicted covariance and the updated Jacobian matrix.

[0030] Specifically, the implementation process of extended Kalman filtering mainly involves the following steps: Based on the established system state equations, The nonlinear discretization process is performed, and the result is characterized as follows:

[0031] In the formula: For the system period.

[0032] Accordingly, Predicted estimates It can be represented as follows:

[0033] The Jacobian matrix before the update is constructed as follows:

[0034] Then, the predicted covariance is constructed based on the unupdated Jacobian matrix, the state estimation covariance, and the process noise. As shown below:

[0035] in, P This represents the state estimate covariance. F This represents the Jacobian matrix before the update. Q w This indicates process noise.

[0036] Based on this, a nonlinear measurement model is used to update the system state equations. The updated system state equations (state variables) are characterized as follows:

[0037] Based on this, the updated Jacobian matrix is ​​obtained. As shown below:

[0038] Based on the predicted covariance and the updated Jacobian matrix, the Kalman gain is obtained as shown below:

[0039] in, R v The covariance matrix represents the noise in the measurement data.

[0040] Finally, the updated covariance is obtained based on the Kalman gain, the updated Jacobian matrix, and the predicted covariance:

[0041] By integrating the above calculation formulas, the environmental stiffness can be determined. and environmental damping , used as an estimate of environmental stiffness and environmental damping.

[0042] When using admittance control algorithms for assembly operations, the six-dimensional force sensor can detect drastic changes in force when encountering hard objects, thereby identifying changes in environmental stiffness. Specifically, the estimated environmental stiffness can be compared with a preset threshold. If the estimated environmental stiffness exceeds the preset threshold, it indicates that the system may be oscillating. In this case, the collaborative robot is switched to torque mode.

[0043] Based on this, the damping in the admittance control algorithm is adjusted according to the estimated environmental stiffness and force sensor data. The correlation between the damping adjustment and the estimated environmental stiffness and force sensor data can be characterized as follows:

[0044] In the formula: For initial damping; This is the stiffness adjustment factor; This is the force adjustment coefficient.

[0045] By switching the collaborative robot to torque mode and employing adaptive damping adjustment, vibration can be further mitigated. Specifically, since the impact force during installation is instantaneous, the damping change cannot be sustained. However, to complete the operation, the operator continuously drags the robot during installation, resulting in continuous impact forces. Therefore, to maintain system stability, adaptive damping can be added.

[0046] Considering the potential errors in environmental stiffness estimation and the redundancy in algorithm design, this embodiment also introduces other switching triggering mechanisms. For details, please refer to [link to relevant documentation]. Figure 4 The collaborative robot motion control method provided in this embodiment may further include the following steps: S21, Obtain the force sensor data collected by the force sensor, and perform sampling and statistics on the force sensor data; S22, the root mean square error is calculated based on statistical results; S23, detect whether the root mean square error has a sudden change. If the root mean square error has a sudden change, then execute the step of switching the collaborative robot to torque mode.

[0047] In this embodiment, RMS (Root Mean Square) detection of the data is introduced based on the admittance control algorithm. A sliding window method is used to sample and statistically analyze the force sensor data acquired by the force sensor. Specifically, at each sampling time, N sampling points are collected to obtain statistical data, which is represented as follows:

[0048] The root mean square error (RMSE) is calculated for the force sensor data corresponding to N sampling points within the sliding window, resulting in the following RMSE:

[0049] If the root mean square error (RMSE) changes abruptly, for example, if the RMSE exceeds a certain threshold and persists for a period of time, it can be determined that a sudden change has occurred in the force sensor data. Since the admittance control algorithm actually uses the filtered force sensor data, it can switch to torque mode in advance to avoid command position bounce caused by sudden changes in force sensor data.

[0050] Building upon this, zero-crossing detection is also introduced in this embodiment. Please refer to [link / reference]. Figure 5 The collaborative robot motion control method provided in this embodiment may further include the following steps: S31, detect whether the collaborative robot shakes within a set time period and whether the shaking frequency exceeds a preset frequency; S32, if there is shaking and the shaking frequency exceeds the preset frequency, then execute the step of switching the collaborative robot to torque mode.

[0051] Since admittance control algorithms can cause oscillations when operating in a closed-loop manner with the environment, if the aforementioned estimation of environmental stiffness and RMS detection fail, it is necessary to promptly identify the oscillation signal and switch the collaborative robot to torque mode. Therefore, in this embodiment, zero-crossing detection is used to identify whether the collaborative robot exhibits lateral swaying at low speeds.

[0052] Since shaking frequencies exceeding a certain range will be noticeable to the human body, this embodiment, in addition to determining the presence of shaking within a set time period based on zero-crossing detection, can also calculate the shaking frequency and detect whether it exceeds a preset frequency. If it exceeds the preset frequency, the collaborative robot needs to be switched to torque mode.

[0053] Specifically, the product of the velocities at two consecutive moments can be calculated. If the product is less than 0, it indicates that the velocities have reversed, meaning a zero-crossing phenomenon has occurred. In this case, the number of zero-crossings can be incremented by 1, which can be represented as follows:

[0054] in, These represent the velocity values ​​at times k and k-1, respectively. t This indicates the cumulative number of times the ...

[0055] The frequency of the vibration is obtained by counting the number of times the device crosses zero within a set time period and dividing the count by the duration of the set time period. ,in, T This indicates the set time period. T For example, the value can be 2s.

[0056] In torque mode, collaborative robots often require a body weight of 40-60 kg, as the patient's limb may exceed 15 kg. This makes drag-and-teach operations with collaborative robots still quite difficult for human operators. In this embodiment, an impedance-driven drag-and-teach algorithm based on a six-dimensional force sensor is introduced. The motor output torque in the algorithm is shown below:

[0057] In the formula: This refers to the output torque of the motor. It is a Jacobian matrix; Coriolis force; For gravity; At the same time, it compensates for dragging force, improving the dragging experience, and The larger the size, the easier it is to drag; Here are the damping matrices in the joint space, all of which are positive numbers; Joint position; This refers to the joint velocity.

[0058] When a collaborative robot generates joint speed, the output torque of the motor decreases, thereby reducing the teaching speed of the collaborative robot and limiting the dragging speed.

[0059] In collaborative robot motion control, the drag-and-drop assembly process can be executed in the above manner. After assembly is completed, the collaborative robot can be controlled to perform a recovery training process. Based on the above admittance control algorithm, in order to provide operators with a better human-computer interaction experience, the collaborative robot motion control algorithm provided in this embodiment also includes the following steps, which should be referred to in conjunction with the above steps. Figure 6 : S41, during the recovery training process, the joint rotation information of the collaborative robot is obtained; S42, determine whether the joint rotation information meets the preset constraint limit. If the joint rotation information meets the preset constraint limit, execute the following step S43. If the joint rotation information does not meet the preset constraint limit, execute the following step S46.

[0060] S43, The force sensor is used to collect the interaction force between the collaborative robot and the human body; S44, generate a Cartesian position command based on the interaction force, and obtain the joint angles of each joint of the collaborative robot based on the position command; S45, Perform the recovery training process based on the joint angle; S46 indicates that the human body is in danger and movement should be stopped.

[0061] Human joints, such as the knee and elbow joints, can only rotate, and only along an axis. Therefore, during rehabilitation training, since patients cannot resist the robot, it is necessary to avoid situations that exceed the constraints.

[0062] A simplified model of a collaborative robot, such as Figure 7 As shown in the figure, the arrows in the figure only indicate the direction of rotation, not the positive direction. Figure 7 The axes from left to right represent the 1st, 2nd, 3rd, 4th, 5th, and 6th axes of the collaborative robot. Among them, the rotation direction of the 2nd, 3rd, and 4th axes of the collaborative robot is consistent with the rotation direction of the human knee joint. The 1st and 5th axes can move slightly. The left and right rotation of the knee joint caused by the 6th axis does not conform to the movement of human joints and needs to be restricted.

[0063] Based on this, in this embodiment, the rotation information applied to the human body is obtained by acquiring the joint rotation information of the collaborative robot. The system checks whether the joint rotation information meets preset constraints. If it does not, it indicates that the rotation applied to the human body by the collaborative robot does not conform to the human joint movement, and control to stop is required to avoid danger. If the preset constraints are met, subsequent operations can be performed.

[0064] In this way, the collaborative robot responds only in the direction of movement allowed by the human joints during rehabilitation training, thereby avoiding the patient's resistance to the robot due to misoperation or external force.

[0065] Under the condition of meeting the preset constraints, the interaction force between the collaborative robot and the human body is collected in real time by the force sensor. The interaction force includes force components in three directions and torque components in three directions, wherein the output torque in the rz direction is 0.

[0066] Based on the interaction forces, Cartesian position commands are generated. These commands are then used to calculate the joint angles of each joint of the collaborative robot using inverse kinematics. While ensuring that the joint angles of specific joints, such as the aforementioned 6-axis joints, remain unchanged, a recovery training process is executed.

[0067] To avoid the impact of sudden changes in force sensor data during the above process, this embodiment can also monitor the torque values ​​of the force sensor in various directions during the recovery training process. Based on the torque values ​​in each direction, a performance index, such as the RMS performance index, is calculated using a sliding window statistical algorithm. If the performance index continues to increase within a preset time period, the collaborative robot is controlled to stop moving, thereby preventing dangerous situations from occurring.

[0068] The collaborative robot motion control method provided in this embodiment combines admittance control algorithms with the collaborative robot's torque mode during assembly. It utilizes an extended Kalman filter algorithm to estimate the environmental stiffness during contact and sets adaptive damping based on force changes and environmental stiffness. Furthermore, it introduces RMS detection and zero-crossing detection algorithms to identify data changes from the six-dimensional force sensor and the robot's actual operating state. When abnormal RMS data or robot oscillation is detected, the robot is promptly switched to torque mode to prevent abnormal situations.

[0069] During collaborative robot-assisted recovery training, external constraints on human joints were introduced. Combined with the robot's configuration, this ensured that the angles of specific joints remained constant. Simultaneously, torque values ​​in three directions were monitored in real-time by a six-dimensional force sensor, and a sliding window statistical algorithm was used to calculate the RMS data. If the RMS continuously increased, the robot's movement was stopped immediately to avoid dangerous situations.

[0070] The collaborative robot motion control method provided in this embodiment can ensure smooth assembly without the need for additional sensors or equipment replacement, avoid system oscillation, and improve the human-computer interaction experience during recovery training.

[0071] Based on the same inventive concept, please refer to Figure 8 This invention also provides a functional module diagram of a robotic arm control system based on an adaptive force field. This embodiment divides the robotic arm control system based on the adaptive force field into functional modules according to the above method embodiments. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware or as software functional modules. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0072] For example, when dividing functional modules according to their respective functions, Figure 8 The collaborative robot motion control device shown is only a schematic diagram. The collaborative robot motion control device may include a construction module, an establishment module, a processing module, a detection module, a switching module, and a control module. The functions of each module of the collaborative robot motion control device will be described in detail below.

[0073] A construction module is used to build a contact model between the assembly tool and the assembly body during the assembly process. The contact model includes environmental damping, environmental stiffness, force sensor data, and collaborative robot end-effector posture offset. A module is established to determine state variables based on the contact model and to establish system state equations based on the state variables; The processing module is used to perform extended Kalman filtering based on the system state equation to obtain the estimated environmental stiffness; The detection module is used to detect whether the estimated environmental stiffness exceeds a preset threshold. A switching module is used to switch the collaborative robot to torque mode when the estimated environmental stiffness exceeds a preset threshold. The control module is used to adjust the damping in the admittance control algorithm based on the estimated environmental stiffness and force sensor data, and to perform the assembly between the assembly tool and the assembly body based on the adjusted admittance control algorithm.

[0074] The collaborative robot motion control device provided in this embodiment can be used to execute the collaborative robot motion control method under any of the above embodiments. For details not covered in this embodiment, please refer to the corresponding descriptions in the above embodiments. This embodiment will not be elaborated here.

[0075] Please see Figure 9 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. The electronic device can be a control device on a collaborative robot, or a computer device, server, etc., communicating with the collaborative robot. The electronic device includes a memory, a processor, and a communication module. The memory, processor, and communication module are electrically connected directly or indirectly to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0076] The memory is used to store computer programs or data. Memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.

[0077] The processor is used to read / write data or programs stored in the memory and to execute the collaborative robot motion control method provided in any embodiment of the present invention.

[0078] The communication module is used to establish communication connections between electronic devices and other communication terminals via a network, and to send and receive data via the network.

[0079] It should be understood that, Figure 9 The structure shown is only a schematic diagram of an electronic device; the electronic device may also include components that are larger than those shown. Figure 9 The more or fewer components shown, or having the same Figure 9 The different configurations shown.

[0080] Furthermore, embodiments of the present invention also provide a computer-readable storage medium storing machine-executable instructions, which, when executed, implement the collaborative robot motion control method provided in the above embodiments.

[0081] Specifically, the computer-readable storage medium can be a general-purpose storage medium, such as a removable disk or hard disk. When the computer program on the computer-readable storage medium is executed, it can perform the aforementioned collaborative robot motion control method. The processes involved in the execution of the executable instructions on the computer-readable storage medium can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0082] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0083] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] Furthermore, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0085] It should be noted that if the functionality is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] The above are merely embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A motion control method for a collaborative robot, characterized in that, The collaborative robot has a force sensor and assembly tool installed at its end effector, and the method includes: During the assembly process, a contact model between the assembly tool and the assembly body is constructed. The contact model includes environmental damping, environmental stiffness, force sensor data, and the posture offset of the collaborative robot end effector. The state variables are determined based on the contact model, and the system state equations are established based on the state variables. Based on the system state equations, extended Kalman filtering is performed to obtain the estimated environmental stiffness; Detect whether the estimated environmental stiffness exceeds a preset threshold. If the estimated environmental stiffness exceeds the preset threshold, switch the collaborative robot to torque mode. The damping in the admittance control algorithm is adjusted based on the estimated environmental stiffness and force sensor data, and the assembly between the assembly tool and the assembly body is executed based on the adjusted admittance control algorithm.

2. The collaborative robot motion control method according to claim 1, characterized in that, The step of performing extended Kalman filtering based on the system state equation to obtain estimated environmental stiffness and estimated environmental damping includes: The Jacobian matrix before and after the update is obtained based on the system state equations before and after the update; The updated covariance is obtained from the Jacobian matrix before and after the update; The estimated environmental stiffness and estimated environmental damping are obtained based on the updated covariance.

3. The collaborative robot motion control method according to claim 2, characterized in that, The step of obtaining the updated covariance based on the Jacobian matrix before and after the update includes: The predicted covariance is obtained based on the Jacobian matrix before the update, the state estimation covariance, and the process noise. The updated covariance is obtained based on the predicted covariance and the updated Jacobian matrix.

4. The collaborative robot motion control method according to claim 1, characterized in that, The method further includes: Obtain the force sensor data collected by the force sensor, and perform sampling and statistics on the force sensor data; The root mean square error is calculated based on the statistical results. Detect whether the root mean square error changes abruptly. If the root mean square error changes abruptly, then execute the step of switching the collaborative robot to torque mode.

5. The collaborative robot motion control method according to claim 1, characterized in that, The method further includes: The system detects whether the collaborative robot shakes within a set time period and whether the shaking frequency exceeds a preset frequency. If shaking occurs and the shaking frequency exceeds the preset frequency, then the step of switching the collaborative robot to torque mode is executed.

6. The collaborative robot motion control method according to claim 1, characterized in that, The system state equations incorporate system model errors and measurement noise.

7. The collaborative robot motion control method according to any one of claims 1-6, characterized in that, The method further includes: During the recovery training process, the joint rotation information of the collaborative robot is obtained, and it is determined whether the joint rotation information meets the preset constraints. If the joint rotation information meets the preset constraints, the force sensor collects the interaction force between the collaborative robot and the human body. The Cartesian position command is generated based on the interaction force, and the joint angles of each joint of the collaborative robot are obtained based on the position command. The recovery training process is performed based on the joint angle.

8. The collaborative robot motion control method according to claim 7, characterized in that, The method further includes: During the recovery training process, the torque values ​​of the force sensor in each direction are monitored; Performance indicators are calculated based on torque values ​​in each direction and using a sliding window statistical algorithm. If the performance index continues to increase within a preset time period, the collaborative robot will be controlled to stop moving.

9. A motion control device for a collaborative robot, characterized in that, The collaborative robot has a force sensor and assembly tools installed at its end effector, and the device includes: A construction module is used to build a contact model between the assembly tool and the assembly body during the assembly process. The contact model includes environmental damping, environmental stiffness, force sensor data, and collaborative robot end-effector posture offset. A module is established to determine state variables based on the contact model and to establish system state equations based on the state variables; The processing module is used to perform extended Kalman filtering based on the system state equation to obtain the estimated environmental stiffness; The detection module is used to detect whether the estimated environmental stiffness exceeds a preset threshold. A switching module is used to switch the collaborative robot to torque mode when the estimated environmental stiffness exceeds a preset threshold. The control module is used to adjust the damping in the admittance control algorithm based on the estimated environmental stiffness and force sensor data, and to perform the assembly between the assembly tool and the assembly body based on the adjusted admittance control algorithm.

10. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, cause the processor to perform the method as described in any one of claims 1 to 8.

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