Robotic arm cable assembly method and system

Abstract

The application discloses a mechanical arm cable assembly method and system, and relates to the technical field of robot vision perception and control, and comprises the following steps: acquiring point cloud data of a working environment; fitting a cable center line according to the point cloud data and determining a relative pose matrix of a cable terminal and a target slot; determining an initial alignment attitude matrix of a mechanical arm end according to the relative pose matrix, so as to obtain initial joint angles of the mechanical arm and use the initial joint angles as a starting point of an assembly action; after selecting a grabbing point on the cable center line and planning an insertion path, entering an insertion stage from the starting point; according to detection of an insertion force and an insertion direction, performing error calculation and compensation until the insertion force change rate and the end pose error meet a set condition, and then determining that the assembly is completed. The application realizes self-perception, self-judgment and self-adjustment flexible assembly, improves assembly precision and robustness, and is suitable for automatic grabbing, guiding and insertion operation of flexible cables in industrial automation production.

Description

Technical Field

[0001] This invention relates to the field of robot vision perception and control technology, and in particular to a method and system for assembling cables for a robotic arm. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In current industrial robots, cable assembly is one of the most difficult automation processes. This is because cables are highly flexible and their posture is unpredictable, making it difficult for robotic arms to grasp them accurately; the positioning error between terminals and slots is small (the tolerance is only at the millimeter level), often resulting in misaligned insertion or jamming.

[0004] Traditional insertion methods have fixed trajectories and cannot adapt to cable swing or deviation; force control systems lack adaptive adjustment capabilities, and terminals are easily damaged when stiffness is too high, and cannot be inserted when stiffness is too low. Moreover, existing control methods only use visual positioning or force-controlled blind insertion, which lacks information fusion. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a robotic arm cable assembly method and system that integrates vision and force perception in a closed loop to achieve compliant assembly with self-sensing, self-judgment, and self-adjustment, thereby improving assembly accuracy and robustness. This method is applicable to the automatic gripping, guiding, and insertion of flexible cables in industrial automation production.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for assembling cables for a robotic arm, comprising:

[0008] Acquire point cloud data of the operating environment;

[0009] Fit the cable centerline based on the point cloud data, determine the relative pose matrix between the cable terminal and the target slot, determine the initial alignment pose matrix of the robotic arm end based on the relative pose matrix, and solve for the initial joint angle of the robotic arm, which serves as the starting point for the assembly action.

[0010] After selecting the grab point and planning the insertion path on the cable centerline, the insertion stage begins from the starting point.

[0011] Based on the detection of insertion force and insertion direction, error calculation and speed and pose compensation are performed until the rate of change of insertion force and the end pose error meet the set conditions, at which point the assembly is considered complete.

[0012] As an alternative implementation, the relative pose matrix of the cable terminal and the target slot for;

[0013] ;

[0014] in, Let be the rotation matrix of the target slot, representing the orientation of the slot coordinate system in the robot arm's base coordinate system, with the Z-axis direction being the normal direction. That is, insertion direction ; The position vector of the target slot; Let Z be the rotation matrix of the cable terminal, representing the orientation of the cable terminal coordinate system in the robot arm's base coordinate system, with the Z-axis direction being the direction vector of the cable terminal. ; This is the position vector of the cable terminal;

[0015] relative pose matrix Rotating part As the initial alignment attitude matrix.

[0016] As an alternative implementation method, select the grab point The conditions to be met are: ;in, The set of points on the center line of the cable. For local curvature, This is the adjustment coefficient; The location of the target point.

[0017] As an alternative implementation method, a multi-objective trajectory planning model is used to plan the insertion path; multi-objective trajectory planning model for:

[0018] ;

[0019] in, For cable bending energy; For balance parameters; Let be the linear velocity vector of the robotic arm's end effector at time t; Let be the linear acceleration vector of the robotic arm's end effector at time t; T is the total time interval.

[0020] As an alternative implementation, when the insertion force exceeds the insertion force threshold, the system switches to a compliance control mode; the compliance control mode is based on an impedance model. ;in, These represent the virtual mass, damping, and stiffness matrices, respectively. , This is the initial stiffness; For stiffness adjustment gain, For insertion force The rate of change; External forces measured in real time; These are the real-time position, velocity, and acceleration vectors of the robotic arm's end effector; Let be the desired position vector of the robotic arm's end effector;

[0021] At this point, the speed compensation is ;in, The compensated speed of the robotic arm's end effector; For the desired speed; Force control gain matrix; For real-time insertion force; Let be the reference insertion force, and let be the reference insertion force at time t. , For the average insertion force, These are the amplitude and frequency factor, respectively.

[0022] As an alternative implementation, when a deviation in the insertion direction is detected, pose compensation is performed; including:

[0023] Attitude error for: The corresponding attitude correction vector for: ;

[0024] Position error compensation for: ;

[0025] The integrated control input is ;

[0026] in, This is the rotation gain matrix; This represents the current actual posture of the robotic arm's end effector. The desired posture; This is the position scaling gain matrix; For the desired position; This refers to the current actual position of the robotic arm's end effector. It is a 6-dimensional vector containing linear velocity v and angular velocity ω, used to adjust the movement of the robotic arm end effector to align the cable insertion direction with the axis of the target slot.

[0027] In a second aspect, the present invention provides a robotic arm cable assembly system, comprising:

[0028] The acquisition module is configured to acquire point cloud data of the operating environment;

[0029] The attitude recognition module is configured to fit the center line of the cable based on the point cloud data, determine the relative pose matrix between the cable terminal and the target slot, determine the initial alignment attitude matrix of the end effector of the robotic arm based on the relative pose matrix, and thus solve for the initial joint angle of the robotic arm, which serves as the starting point for the assembly action.

[0030] The motion planning module is configured to select a gripping point on the centerline of the cable and plan the insertion path before starting the insertion phase from the starting point.

[0031] The control module is configured to perform error calculation and speed and pose compensation based on the detection of insertion force and insertion direction, until the insertion force change rate and end pose error meet the set conditions to determine that the assembly is complete.

[0032] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0033] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0034] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] To address the problems of uncertain flexible cable posture, small insertion errors, and unsuitable force control in existing cable assembly methods, this invention proposes a robotic arm cable assembly method and system based on visual perception and adaptive control. The method collects color images and depth point cloud data of the working environment, performs coordinate calibration, acquires the cable and insertion target posture, aligns the initial posture, and plans the gripping point and path. Then, it utilizes a force sensor to provide real-time feedback of six-dimensional force / torque signals, enabling dynamic insertion control, posture error correction, and adaptive insertion force adjustment. Finally, assembly completion is determined by force and position thresholds, supporting self-recovery from jamming. This invention integrates a visual and force-sensing closed loop to achieve compliant assembly with self-sensing, self-judgment, and self-adjustment, improving assembly accuracy and robustness. It is suitable for the automatic gripping, guiding, and insertion of flexible cables in industrial automation production.

[0037] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0039] Figure 1 This is a flowchart of the robotic arm cable assembly method provided in Embodiment 1 of the present invention. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0042] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. Furthermore, it should be understood that the terms “comprising” and “including”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0043] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0044] Example 1

[0045] This embodiment provides a method for assembling cables for a robotic arm. Based on visual perception and adaptive control technology, it achieves automatic cable grasping, guiding, and insertion operations by fusing visual and force signals.

[0046] like Figure 1 As shown, it includes:

[0047] Acquire point cloud data of the operating environment;

[0048] Fit the cable centerline based on the point cloud data, determine the relative pose matrix between the cable terminal and the target slot, determine the initial alignment pose matrix of the robotic arm end based on the relative pose matrix, and solve for the initial joint angle of the robotic arm, which serves as the starting point for the assembly action.

[0049] After selecting the grab point and planning the insertion path on the cable centerline, the insertion stage begins from the starting point.

[0050] Based on the detection of insertion force and insertion direction, error calculation and speed and pose compensation are performed until the rate of change of insertion force and the end pose error meet the set conditions, at which point the assembly is considered complete.

[0051] The above methods will be explained in detail below.

[0052] Step 1: Perception of the working environment and coordinate calibration.

[0053] First, color images and point cloud data of the working environment are acquired using a 3D vision sensor installed at or above the end of the robotic arm.

[0054] Then, coordinate system calibration is performed using a calibration plate or calibration ball to obtain the extrinsic transformation matrix from the visual coordinate system to the robotic arm coordinate system. .

[0055] Subsequently, all point cloud data were converted to the robotic arm coordinate system to ensure that the visual perception results were aligned with the robotic arm's operating space.

[0056] Among them, the three-dimensional vision sensor is of the structured light, laser scanning or stereo vision type, which can output dense spatial point clouds.

[0057] Step 2: Acquire the spatial attitude of the cable and the insertion target.

[0058] Fit the cable centerline based on point cloud data. :

[0059] ;

[0060] in, For cable length, Let S be the coordinates of point S on the center line of the cable.

[0061] The position vector of the cable terminal is directional vector of cable terminals Calculated from the tangent line of the curve: .

[0062] The normal direction of the target slot (such as a terminal block) is denoted as Insertion direction set to .

[0063] The relative pose matrix between the cable terminal and the target slot is obtained through attitude transformation:

[0064] ;

[0065] in, Let be the rotation matrix of the target slot, a 3x3 matrix, describing the orientation of the slot coordinate system in the robot arm's base coordinate system, with its Z-axis direction being the normal direction. That is, insertion direction ; The position vector of the target slot is a 3x1 vector, representing the coordinates of the slot's center point in the robot arm's base coordinate system; Let be the rotation matrix of the cable terminal, a 3x3 matrix, describing the orientation of the cable terminal's coordinate system in the robot arm's base coordinate system. Its Z-axis direction is the orientation vector of the cable terminal. ; This is the position vector of the cable terminal, i.e. , representing the coordinates of the cable terminal in the robot arm's base coordinate system.

[0066] This matrix describes the spatial deviation of the cable terminal relative to the target slot, providing a basis for subsequent alignment and trajectory planning.

[0067] Step 3: Initial attitude alignment and trajectory start point planning.

[0068] Based on the relative pose matrix obtained in step 2 Calculate the initial alignment attitude matrix of the robotic arm end effector to ensure that the direction of the cable terminal and the direction of the insertion hole remain collinear.

[0069] Specifically, the rotation part of the initial alignment attitude matrix is: It is the relative pose matrix. The rotating part (3x3 matrix) is obtained by multiplying the rotation matrices of the cable terminals and the target slot, which ensures that the attitude of the robotic arm end effector is aligned with the cable terminals.

[0070] Subsequently, the initial joint angles of the robotic arm were obtained by solving the inverse kinematics using a numerical iterative method based on the Jacobian pseudo-inverse. This serves as the starting point for the assembly process.

[0071] Step 4: Cable grab point and insertion path planning.

[0072] First, at the center line of the cable Select grab point The selection principle is to balance the proximity to the endpoint with the cable bending stability, and to meet the optimization conditions:

[0073] ;

[0074] in, The set of points on the center line of the cable. For local curvature, This is the adjustment coefficient; The location of the target point.

[0075] This optimization ensures that the gripping position is not in a high-bend area, thereby reducing the risk of cable swaying.

[0076] Then, a multi-objective trajectory planning model is adopted. :

[0077] ;

[0078] in, The cable bending energy term is calculated by integrating along the cable centerline, which is proportional to the square of the curvature. ,in It is the curvature along the arc length s of the cable; For balance parameters; Let be the linear velocity vector of the robotic arm's end effector at time t; Let be the linear acceleration vector of the robotic arm's end effector at time t; T is the total time interval.

[0079] To achieve the above optimization goals, this invention employs a parametric programming method, using a fifth-order polynomial to parametrically model the displacement trajectory of the robotic arm's end effector.

[0080] Specifically, trajectory It is independently represented by a fifth-degree polynomial in each of its dimensions:

[0081] ;

[0082] The advantage lies in the fact that the fifth-order polynomial contains six coefficients, which are sufficient to precisely satisfy the six boundary conditions, including the position, velocity, and acceleration of the trajectory's starting and ending points. This ensures the smoothness of the trajectory, consistent with the optimization objective of minimizing acceleration. This is achieved through parameterization... Substitute into the multi-objective trajectory planning model The functional optimization problem can be transformed into solving the problem of polynomial coefficients. The parameter optimization problem is easy to solve.

[0083] Multi-objective trajectory planning model as the basis for optimization The coefficients of the fifth-degree polynomial that satisfy the above boundary conditions can be solved. To achieve this. Specifically:

[0084] Parameterization Substitute into the model This complex functional optimization problem is thus transformed into a more easily solvable parametric optimization problem with polynomial coefficients as variables. By adjusting the coefficients, it is possible to find the objective function among all possible fifth-degree polynomial curves that satisfy the boundary conditions. The optimal (or near-optimal) curve.

[0085] Step 5: Dynamic insertion control and force feedback.

[0086] When the robotic arm enters the insertion stage, the force sensor at the end of the robotic arm begins to collect six-dimensional force / torque signals:

[0087] ;

[0088] in, These are the force components measured by the sensors along the X, Y, and Z axes of the robotic arm's end-effector coordinate system. These are the torque components measured by the sensors in the X, Y, and Z axes of the coordinate system at the end of the robotic arm.

[0089] Set insertion force threshold ,when Switch to compliant control mode at this time.

[0090] Compliance control is based on an impedance model:

[0091] ;

[0092] in, These represent the virtual mass, damping, and stiffness matrices, respectively. External forces measured in real time; These are the real-time position, velocity, and acceleration vectors of the robotic arm's end effector; Let be the desired position vector of the robotic arm's end effector.

[0093] Speed ​​compensation is calculated based on force error to ensure smooth changes in insertion force and prevent terminal jamming; when based on the insertion force threshold... After determining that contact has occurred and switching to compliant control mode, speed compensation based on force feedback is initiated in this mode.

[0094] The speed compensation formula is as follows:

[0095] ;

[0096] in, The compensated end-effector command speed; For the desired speed; Force control gain matrix; For reference insertion force; This refers to the insertion force measured in real time.

[0097] Step 6: Adaptive insertion force adjustment.

[0098] Based on the force feedback in step 5, adaptive insertion force adjustment is initiated to prevent rigid collisions and handle minor alignment errors.

[0099] Design a dynamic insertion force reference model:

[0100] ;

[0101] in, For the average insertion force, These are amplitude and frequency factor, respectively. The reference insertion force is used as the input for the velocity compensation formula in step 5.

[0102] The average insertion force is dynamically adjusted, and the insertion force reference curve is adjusted in real time. If the insertion depth continues to increase and the force remains stable, the force is increased slowly. To accelerate insertion; if persistent jamming is detected ( continuously greater than within the set time period If so, then appropriately reduce This prevents excessive compression, ensures a smooth contact transition, and reduces the probability of terminal damage.

[0103] At the same time, according to the rate of change of force The virtual stiffness is adjusted to automatically reduce stiffness when a sudden increase in contact force is detected, thus achieving a compliant response;

[0104] ;

[0105] in, This is the initial stiffness; This is the stiffness adjustment gain.

[0106] When a sudden increase in contact force is detected ( When the value is >0, the stiffness is automatically reduced. This enables a smoother response and buffers collisions.

[0107] The new stiffness calculated here Directly update the impedance model in step 5 matrix.

[0108] Step 7: Attitude error correction and dynamic compensation.

[0109] If, after performing the adaptive insertion force adjustment in step 6, the terminal posture is determined to have deviated by force sensor signals (such as non-axial torque) or visual information, and the insertion force continues to be greater than the maximum threshold and the lateral torque is significant, then it is determined to be a stuck state caused by posture deviation, and the posture error is automatically calculated and compensated.

[0110] The normal direction of the target slot has been defined in step 2. This direction is the correct insertion direction and remains unchanged throughout the assembly process. Simultaneously, in step 2, the initial direction vector of the cable terminal is obtained by fitting the cable centerline. Under ideal alignment conditions, Should be with They are in the same direction.

[0111] Attitude error Using quaternion form: ;

[0112] Corresponding attitude correction vector for: ;

[0113] Meanwhile, position error compensation amount for: ;

[0114] The integrated control input is: ;

[0115] in, Here is the rotation gain matrix. It is a quaternion logarithmic mapping function; This represents the current actual posture of the robotic arm's end effector. The desired posture; This is the position scaling gain matrix; The desired location determined by the planned path; This refers to the current actual position of the robotic arm's end effector. It is a 6-dimensional vector containing linear velocity v and angular velocity ω, used to adjust the motion of the robotic arm end effector, thereby correcting the pose error in real time and achieving precise alignment between the cable insertion direction and the target slot axis.

[0116] Step 8: Instrumentation completion determination and release.

[0117] Real-time monitoring of the insertion force change rate and end-effector pose error; automatic determination of assembly completion when the following conditions are met:

[0118] ;

[0119] in, These are the force threshold and the position threshold, respectively.

[0120] If the condition continues for a period of time If the set value is exceeded, a release action is executed, and the robotic arm releases the cable.

[0121] If the insertion force continues to exceed the maximum threshold of the safety protection And the attitude error did not decrease. Much larger If the condition is detected as stuck, the machine will automatically perform a buffer retraction action of retracting 3-5 mm and recalculate the insertion path to achieve self-recovery.

[0122] Step 9: Control system and communication mechanism.

[0123] The control system is based on the ROS (Robot Operating System, an open-source robot software framework) communication architecture. Its modules include vision processing nodes, force feedback nodes, motion planning nodes, and controller nodes. The main control loop frequency is set to 500 Hz to achieve high-frequency closed-loop updates. The controller integrates vision, force feedback, and error correction results to generate end-effector velocity and attitude commands in real time, enabling high-precision assembly.

[0124] Based on the above method, a robotic arm cable assembly system based on visual perception and adaptive control is designed. The system includes: a 3D vision sensor, a robotic arm body, a force sensor, a motion planning module, an adaptive control module, and a robotic arm controller.

[0125] The 3D vision sensor is installed at or above the end of the robotic arm to collect color images and depth point cloud data of the working environment. It supports structured light, laser scanning or stereo vision types to realize the attitude recognition of cable endpoints and insertion targets.

[0126] The robotic arm itself is a multi-degree-of-freedom industrial robot arm used to perform cable gripping, guiding and insertion actions, and supports inverse kinematics solving and end-effector posture control.

[0127] Force sensors are integrated into the end effector of the robotic arm to acquire six-dimensional force / torque signals in real time, providing force feedback to support compliant control.

[0128] The motion planning module is based on a multi-objective optimization algorithm to calculate the grab point and insertion path, ensuring a smooth path and minimal cable deformation.

[0129] The adaptive control module adopts an impedance control model and dynamically adjusts the virtual stiffness and insertion force according to the force signal to achieve attitude error correction and self-recovery from lag.

[0130] The robotic arm controller integrates the ROS communication architecture to coordinate the fusion of signals from various modules and generate real-time control commands.

[0131] This embodiment proposes a comprehensive assembly method integrating visual perception, force feedback, and adaptive control, enabling robots to possess self-sensing, self-judgment, and self-adjustment capabilities during the assembly process. It includes three closed loops: a perception closed loop (vision), where a 3D vision sensor acquires the real-time posture of the cable terminals and slots, establishing a precise spatial relative pose model and forming a dynamic assembly reference coordinate system; a motion closed loop (trajectory planning), which utilizes multi-objective functions for path optimization, ensuring both minimal insertion time and minimum cable deformation energy, achieving smooth, non-pulling trajectory planning; and a force control closed loop (adaptive control), where the robotic arm adjusts the insertion force and virtual stiffness in real-time based on force sensor feedback during insertion, automatically retracting and correcting for jamming, achieving compliant assembly. Thus, by integrating visual and force feedback closed loops, compliant assembly with self-sensing, self-judgment, and self-adjustment is achieved, improving assembly accuracy and robustness, and is suitable for the automatic gripping, guiding, and insertion of flexible cables in industrial automation production.

[0132] Example 2

[0133] This embodiment provides a robotic arm cable assembly system, including:

[0134] The acquisition module is configured to acquire point cloud data of the operating environment;

[0135] The attitude recognition module is configured to fit the center line of the cable based on the point cloud data, determine the relative pose matrix between the cable terminal and the target slot, determine the initial alignment attitude matrix of the end effector of the robotic arm based on the relative pose matrix, and thus solve for the initial joint angle of the robotic arm, which serves as the starting point for the assembly action.

[0136] The motion planning module is configured to select a gripping point on the centerline of the cable and plan the insertion path before starting the insertion phase from the starting point.

[0137] The control module is configured to perform error calculation and speed and pose compensation based on the detection of insertion force and insertion direction, until the insertion force change rate and end pose error meet the set conditions to determine that the assembly is complete.

[0138] It should be noted that the above modules correspond to the steps described in Embodiment 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0139] In further embodiments, the following is also provided:

[0140] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0141] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0142] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0143] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0144] The method in Example 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0145] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0146] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0147] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0148] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0149] Those skilled in the art will recognize that the units and algorithm steps described in connection with the various examples of this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0150] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for assembling cables for a robotic arm, characterized in that, include: Acquire point cloud data of the operating environment; Fit the cable centerline based on the point cloud data, determine the relative pose matrix between the cable terminal and the target slot, determine the initial alignment pose matrix of the robotic arm end based on the relative pose matrix, and solve for the initial joint angle of the robotic arm, which serves as the starting point for the assembly action. After selecting the gripping point and planning the insertion path on the cable centerline, the insertion stage begins from the starting point; the selected gripping point... The conditions to be met are: ;in, The set of points on the center line of the cable. For local curvature, This is the adjustment coefficient; The location of the target point; Based on the detection of insertion force and insertion direction, error calculation and speed and pose compensation are performed until the insertion force change rate and end pose error meet the set conditions and the assembly is judged to be complete. When the insertion force exceeds the insertion force threshold, the system switches to compliant control mode; the compliant control mode is based on an impedance model. ;in, These represent the virtual mass, damping, and stiffness matrices, respectively. , This is the initial stiffness; For stiffness adjustment gain, For insertion force The rate of change; External forces measured in real time; These are the real-time position, velocity, and acceleration vectors of the robotic arm's end effector; Let be the desired position vector of the robotic arm's end effector; At this point, the speed compensation is ;in, The compensated speed of the robotic arm's end effector; For the desired speed; Force control gain matrix; For real-time insertion force; Let be the reference insertion force, and let be the reference insertion force at time t. , For the average insertion force, These are amplitude and frequency factor, respectively. When a deviation in insertion direction is detected, pose compensation is performed; including: Attitude error for: The corresponding attitude correction vector for: ; Position error compensation for: ; The integrated control input is ; in, This is the rotation gain matrix; This represents the current actual posture of the robotic arm's end effector. The desired posture; This is the position scaling gain matrix; For the desired position; This refers to the current actual position of the robotic arm's end effector. It is a 6-dimensional vector containing linear velocity v and angular velocity ω, used to adjust the movement of the robotic arm end effector to align the cable insertion direction with the axis of the target slot.

2. The robotic arm cable assembly method as described in claim 1, characterized in that, Relative pose matrix of cable terminals and target slots for; ； in, Let be the rotation matrix of the target slot, representing the orientation of the slot coordinate system in the robot arm's base coordinate system, with the Z-axis direction being the normal direction. That is, insertion direction ; The position vector of the target slot; Let Z be the rotation matrix of the cable terminal, representing the orientation of the cable terminal coordinate system in the robot arm's base coordinate system, with the Z-axis direction being the direction vector of the cable terminal. ; This is the position vector of the cable terminal; relative pose matrix Rotating part As the initial alignment attitude matrix.

3. The robotic arm cable assembly method as described in claim 1, characterized in that, A multi-objective trajectory planning model is used to plan the insertion path; multi-objective trajectory planning model for: ； in, For cable bending energy; For balance parameters; Let be the linear velocity vector of the robotic arm's end effector at time t; Let be the linear acceleration vector of the robotic arm's end effector at time t; T is the total time interval.

4. A robotic arm cable assembly system, characterized in that, A method for performing a robotic arm cable assembly as described in any one of claims 1-3 includes: The acquisition module is configured to acquire point cloud data of the operating environment; The attitude recognition module is configured to fit the center line of the cable based on the point cloud data, determine the relative pose matrix between the cable terminal and the target slot, determine the initial alignment attitude matrix of the end effector of the robotic arm based on the relative pose matrix, and thus solve for the initial joint angle of the robotic arm, which serves as the starting point for the assembly action. The motion planning module is configured to select a gripping point on the cable centerline and plan the insertion path before starting the insertion phase from the initial point; the selected gripping point... The conditions to be met are: ;in, The set of points on the center line of the cable. For local curvature, This is the adjustment coefficient; The location of the target point; The control module is configured to perform error calculation and speed and pose compensation based on the detection of insertion force and insertion direction, until the insertion force change rate and end pose error meet the set conditions to determine that the assembly is complete.

5. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-3.

7. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-3.

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