Self-adaptive man-machine cooperation assembly system for heavy-load industrial robot large-component mechanism and control method of self-adaptive man-machine cooperation assembly system

Abstract

The invention relates to the technical field of heavy-load industrial robot assembly, solves the technical problems that a traditional heavy-load robot is limited in fixed station space, rigid contact is prone to damaging components and control delay causes vibration, and particularly relates to a heavy-load industrial robot large-component mechanism self-adaptive man-machine cooperation assembly system and a control method of the heavy-load industrial robot large-component mechanism self-adaptive man-machine cooperation assembly system. After the AGV trolley moves to the assembly part placing position and the robot body moves to drive the end effector to reach the designated position, the adsorption mechanism adsorbs the assembly part through the suction cup assembly, then the tightening mechanism tightens the assembly part, and finally the robot body moves to enable the assembly part to be away from the assembly part placing position. And fixation of the assembly part is completed. The working space is expanded through the AGV cooperative motion platform, the contact stress is reduced in combination with the self-adaptive variable-rigidity end effector, the intelligent variable-admittance algorithm is designed to suppress dynamic disturbance, and efficient and safe assembly of large-size weak-rigidity components is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of heavy-load industrial robot assembly, and in particular to a heavy-load industrial robot large component mechanism adaptive human-machine collaborative assembly system and a control method thereof. Background Art

[0002] In high-end equipment manufacturing, particularly in the assembly of large, complex aerospace components, these components, characterized by their heavy weight, large size, and high assembly precision, have long faced technical bottlenecks. Traditional heavy-duty industrial robot systems often utilize fixed-station operation modes, with their workspace limited by the robot's range of motion. Furthermore, these components place stringent assembly force requirements, and even slight variations in assembly error can impact final assembly quality. Consequently, fixed-station operation modes struggle to meet the assembly demands of large components.

[0003] In addition, current human-machine collaborative systems are mostly focused on the field of lightweight robots, and there are still technological gaps in heavy-load operation scenarios. The motion hysteresis problem caused by the inertia characteristics of heavy-load industrial robots makes it difficult for traditional safety sensors to complete collision detection and braking response within a short time window. Moreover, due to the large loads and high assembly precision requirements of large aerospace components, as well as the presence of posture uncertainty and high flexibility requirements, the traditional fixed workstation and rigid end-effector assembly method is difficult to adapt, which can easily lead to assembly errors or structural damage; at the same time, manual assembly is inefficient and highly dangerous. Existing heavy-load robot systems are unable to meet the needs of high-precision adaptive assembly due to their limited range of motion and lack of real-time compliant control capabilities.

[0004] To address the above issues, patent application CN119458378A discloses an active compliant control method for human-robot collaboration in industrial robots. This method calculates the actual forces and torques acting on the industrial robot based on a gravity compensation algorithm and a zero-drift compensation algorithm for the industrial robot's six-dimensional force sensor. By establishing an admittance control model and then discretizing the model, the robot is controlled to move to a specified assembly position. While this method can account for the robot's spatial envelope within a certain range and reduce collision risk to a certain extent, the admittance control mode used in this method has certain drawbacks. It fails to account for sudden acceleration and velocity changes, cannot effectively suppress sudden vibrations, and cannot meet the compliance requirements for human-robot collaboration. It still has certain limitations for the safe assembly of large components.

[0005] To sum up, in the field of large component assembly by heavy-load industrial robots, traditional heavy-load industrial robots are restricted by fixed workstation modes and limited motion space, making it difficult to adapt to the needs of large component assembly, while rigid end effectors and preset trajectory programming can easily cause structural damage to large components. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a heavy-duty industrial robot large component mechanism adaptive human-machine collaborative assembly system and its control method, which solves the technical problems of traditional heavy-duty robots such as fixed workstation space limitations, rigid contact that easily damages components, and vibration caused by control delays.

[0007] To address the above technical issues, the present invention expands the working space through an AGV collaborative motion platform, combines an adaptive variable stiffness end effector to reduce contact stress, and designs an intelligent variable admittance algorithm to suppress dynamic disturbances, thereby achieving efficient and safe assembly of large-scale, weakly rigid components. The technical solutions provided are as follows:

[0008] A control method for a heavy-load industrial robot large component mechanism adaptive human-machine collaborative assembly system comprises the following steps:

[0009] S1. Based on the force sensor, the force sensor receives the three-axis force and three-axis moment exerted on the components below it, and identifies the gravity center parameters of the components below the force sensor, thereby performing real-time compensation for the components below the force sensor, and obtaining the actual three-axis force and actual three-axis moment after real-time monitoring and compensation generated by the external force on the components below the force sensor;

[0010] S2, based on the acceleration sensor receiving the end effector at the specified assembly posture point, real-time monitoring of the compensated actual three-axis acceleration and the corresponding actual three-axis angular acceleration;

[0011] S3. Based on the actual three-axis force, actual three-axis torque, actual three-axis acceleration, and actual three-axis angular acceleration, an admittance control model is established as the assembly task control algorithm. The current end position of the robot body is continuously corrected through feedback from the admittance control model until the assembly process is completed.

[0012] Furthermore, in step S1, the specific process includes the following steps:

[0013] S11, the integrated control cabinet receives data from the force sensor and the robot control cabinet, wherein the data of the force sensor are the uncompensated three-axis force (f xi 、f yi 、f zi ) and uncompensated three-axis moments (t xi , t yi , t zi ), the corresponding robot control cabinet data are (X i 、Y i , Z i 、A i 、B i 、C i );

[0014] S12, processing the data received in step S11, the formula is expressed as:

[0015]

[0016] Among them, (l x 、l y 、l z ) is the position of the center of gravity of the component below the force sensor relative to the center point of the force sensor; (f x0 、f y0 、f z0 , t x0 , t y0 , t z0 ) is the zero drift value of the force sensor;

[0017] The unknown quantity (l x 、l y 、l z ) and (f x0 、f y0 、f z0 , t x0 , t y0 , t z0 );

[0018] The formula for calculating the weight of the following components of the force sensor is:

[0019]

[0020] Among them, I 3×3 is a third-order unit matrix; G is the weight of the components below the force sensor; is the rotation transformation matrix of the force sensor at each position relative to the robot base; α and β are the roll angle and pitch angle of the robot base relative to the world coordinate system, respectively;

[0021] Solve the unknown quantities G, α and β by the least square method;

[0022] S13, according to step S11 and step S12, calculating the actual external force and torque applied by the real-time operator;

[0023] First, perform gravity conversion on the following components of the force sensor, and the formula is expressed as:

[0024]

[0025] Among them, (G x , G y , G z ) is the real-time component force of the following components of the force sensor in the force sensor coordinate system; The real-time rotation transformation matrix of the force sensor relative to the robot base;

[0026] Combining the above, the actual external force and moment are calculated, and the formula is expressed as:

[0027]

[0028] Among them, (f x_s 、f y_s 、f z_s , t x_s , t y_s , t z_s ) is the real-time force sensor data; (f x 、f y 、f z , t x , t y , t z ) is to calculate the actual external force and torque applied by the real-time operator, that is, to monitor the actual three-axis force and actual three-axis torque after compensation in real time.

[0029] Furthermore, in step S2, the specific process includes the following steps:

[0030] S21, the integrated control cabinet receives data from the acceleration sensor and the robot control cabinet, wherein the data of the acceleration sensor is the uncompensated three-axis acceleration (a xi 、a yi 、a zi ), the corresponding robot control cabinet data are (X i 、Y i , Z i 、A i 、B i 、C i );

[0031] S22, for the uncompensated three-axis acceleration (a xi 、a yi 、a zi ) for processing;

[0032] First, the gravitational acceleration g is processed and the formula is expressed as:

[0033]

[0034] Among them, (g xi 、g yi 、g zi ) is the acceleration component of the gravitational acceleration g corresponding to each position in the robot terminal coordinate system; is the rotation transformation matrix of the acceleration sensor at each position relative to the robot base;

[0035] For the scale factor and zero drift value of each axis of the accelerometer in its own coordinate system, the formula is expressed as:

[0036]

[0037] Among them, (k x 、k y 、k z ) is the scale factor of each axis of the acceleration sensor in its own coordinate system; (a x0 、a y0 、a z0 ) is the zero drift value of each axis of the acceleration sensor in its own coordinate system;

[0038] The unknown quantity (k x 、k y 、k z ) and (a x0 、a y0 、a z0 );

[0039] S23. According to steps S21, S22 and S23, the actual linear acceleration is calculated. The formula is:

[0040]

[0041] Among them, (a x_s 、a y_s 、a z_s ) is the real-time acceleration sensor data; (g x 、g y 、g z ) is the real-time acceleration of gravity g in the robot terminal coordinate system; (a x 、a y 、a z ) is to calculate the real-time three-axis acceleration at the specified position, that is, to monitor the actual three-axis acceleration after compensation in real time;

[0042] S24, solve the actual three-axis angular acceleration (a a 、a b 、a c ), the formula is:

[0043]

[0044] Where Δt is the time interval; (w a0 、w b0 、w c0 ) is the zero drift value of each axis when the acceleration sensor is stationary for a long time; (w a_si+1 、w b_si+1 、w c_si+1) and (w a_si-1 、w b_si-1 、w c_si-1 ) are the uncompensated three-axis angular velocities obtained at the previous moment and the next moment respectively; (a a 、a b 、a c ) is to calculate the three-axis angular acceleration at the specified position in real time, that is, to monitor the actual three-axis angular acceleration after compensation in real time.

[0045] Furthermore, in step S3, the specific process includes the following steps:

[0046] S31. Establish the admittance control model when the robot body performs one-dimensional translation motion, which is expressed as:

[0047]

[0048] Among them, f ext is the interaction force, i.e. the force applied by the operator, is the actual triaxial force (f x 、f y 、f z ) one; m is the virtual mass; c is the virtual damping; They represent the acceleration and velocity of the robot end respectively; a is the acceleration, that is, the acceleration at the specified position, and is the actual three-axis acceleration (a) obtained in step S2. x 、a y 、a z )one;

[0049] S32, control the virtual damping c, when When the tanh function has an approximate linear characteristic (slope ) ensures the sensitivity of small acceleration input; when When , the function saturation characteristic limits the damping change to the range of ±Δc, effectively avoiding parameter loss of control under extreme working conditions;

[0050] The virtual damping c adopts the following change rules:

[0051]

[0052] Among them, Δc is the virtual damping adjustment amplitude; γ is the acceleration sensitivity coefficient; is the acceleration parameter benchmark; c d is the initial virtual damping; c v is the nonlinear time-varying effective virtual damping; is the absolute value of the acceleration of the robot end;

[0053] S33, controlling the virtual mass m to achieve speed-aware mass adjustment, which can maintain system sensitivity at low speeds and suppress inertia overshoot at high speeds;

[0054] The virtual mass m adopts the following change rules:

[0055]

[0056] Among them, μ(c v ) is the virtual damping coupling variable; k is the velocity exponential factor, which is used to adjust the intensity of the influence of velocity on the virtual mass; is the maximum permissible speed; m d is the initial virtual mass; m v is the nonlinear time-varying effective virtual mass; is the absolute value of the velocity of the robot end;

[0057] S34. Establish the admittance control model when the robot body performs one-dimensional rotation motion. The expression is:

[0058]

[0059] Among them, t ext is the interaction torque, i.e. the torque applied by the operator, is the actual three-axis torque obtained in step S1 (t x , t y , t z ) one of them; α is the virtual moment of inertia; β is the virtual rotation damping; They represent the angular acceleration and angular velocity of the robot end respectively; a is the angular acceleration, that is, the angular acceleration of the specified position, and is the actual three-axis angular acceleration (a) obtained in step S2. a 、a b 、a c )one;

[0060] S35. The operator performs human-machine collaboration to push the assembly parts to move, and realizes human-machine compliant operation by controlling the changes of admittance parameters (m, c).

[0061] Furthermore, in step S35, specifically:

[0062] During large-scale dragging, because the speed needs to be fast, the virtual mass m is at a high value, the speed is stable, and the virtual damping c is at a low value, the operator can achieve fast dragging; then, during refined dragging, because the speed needs to be slow, the virtual mass m is at a low value, the speed changes significantly, and the virtual damping c is at a high value, the operator can achieve precise assembly and docking.

[0063] An adaptive human-machine collaborative assembly system applied to the above control method comprises:

[0064] The AGV is a high-load, wide-area mobile platform that receives external position movement instructions and moves to a designated location. The robot body is mounted on its front end through a robot-carrying connecting column, and the integrated control cabinet and robot control cabinet are mounted on its rear end.

[0065] The end effector includes an adsorption mechanism and a towing mechanism located on its lower side, which are respectively used to adsorb and fix any surface of the assembly component, and to drag and clamp any opposite side thereof to achieve synchronous fixation and grasping;

[0066] The end of the robot body is connected to the end effector through the robot end connection flange, receives the control command from the robot control cabinet and drives the end effector to move;

[0067] Assembling parts, which is to select different assembly objects according to the assembly task, the robot body moves to make the assembly parts reach the assembly posture point to complete the specified assembly task.

[0068] Furthermore, the end effector mechanism further comprises:

[0069] The force sensor connected to the robot body through the end connection flange of the robot can synchronously measure the three-axis force and three-axis torque of the object in three-dimensional space, receive the three-axis force and three-axis torque data of the components below the force sensor, and then transmit the data to the integrated control cabinet;

[0070] A force sensor connecting flange is mounted on the force sensor and is used to connect with the quick-change mechanism;

[0071] The quick-change mechanism is a component that connects the force sensor flange and the adsorption mechanism, and is used to replace the end effector to achieve the grasping of different assembly parts;

[0072] The acceleration sensor is used to measure the three-axis acceleration and three-axis angular velocity of the object's motion. The acceleration sensor is fixed on the upper end of the adsorption mechanism, receives the three-axis acceleration and three-axis angular velocity data of the adsorption mechanism at a specified position, and transmits the data to the integrated control cabinet;

[0073] Radars are installed in pairs on the left and right sides of the adsorption mechanism to carry out real-time data transmission and communication to ensure that the two sides of the end effector do not touch other surrounding objects during the assembly process.

[0074] Furthermore, the adsorption mechanism includes an adsorption frame as its supporting and fixing structure, and a suction cup assembly distributed in an array and fixed at the lower end of the adsorption frame, and the suction cup assembly is a vacuum suction cup assembly composed of at least six pairs of suction cups.

[0075] Furthermore, the towing mechanism includes:

[0076] The cylinder is an actuator that converts compressed air energy into a linear motion. It is mounted on the clamping and tightening frame, and the end telescopic drive drives the jaw assembly to move.

[0077] The telescopic shaft assembly is an auxiliary telescopic assembly, which is fixed on the clamping and tightening frame, and the auxiliary telescopic drive drives the clamping claw assembly to move;

[0078] Adsorption mechanism frame, which is the supporting and fixing structure of the towing mechanism;

[0079] The driving clamping claw assembly is a component that drags and clamps the assembly parts. It is installed and fixed on the cylinder and telescopic shaft assembly, and the clamping claw assembly is driven to move by the cylinder telescopic drive.

[0080] By means of the above technical solution, the present invention provides a heavy-duty industrial robot large component mechanism adaptive human-machine collaborative assembly system and control method thereof, which has at least the following beneficial effects:

[0081] 1. The present invention realizes the flexible assembly of large component mechanisms through the collaborative mechanism of multi-sensor data fusion and intelligent variable admittance algorithm. The real-time feedback of force sensors and acceleration sensors effectively solves the problems of component deformation and stress concentration caused by factors such as position deviation in the rigid assembly process of traditional industrial robots, greatly improving the assembly qualification rate of large component mechanisms.

[0082] 2. The present invention combines the high flexibility of the robot, the large range of the AGV and the high-precision feedback of multiple sensors, and then combines the closed-loop control of the force / position / acceleration three domains to achieve a synergistic breakthrough in the collaborative motion architecture, dynamic stiffness adjustment mechanism and multi-sensor data fusion strategy, realizing the unity of full-domain accessibility and high-precision positioning, greatly improving the assembly efficiency of the system.

[0083] 3. The present invention adopts force sensors and acceleration sensors, combined with the variable admittance control method of human-machine collaboration, to construct a dynamic and compliant perception network for human-machine collaboration. While maintaining the high load characteristics of heavy-duty robots, it achieves millimeter-level contact force feedback precision control, significantly improving the smoothness and operational safety of human-machine collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0085] Figure 1 Schematic diagram of the structure of the adaptive human-machine collaborative assembly system of the present invention;

[0086] Figure 2 Schematic diagram of the structure of the end effector in the present invention;

[0087] Figure 3 Schematic diagram of the structure of the adsorption mechanism of the present invention;

[0088] Figure 4 It is a structural schematic diagram of the towing mechanism of the present invention;

[0089] Figure 5 This is a flow chart of the control method of the adaptive human-machine collaborative assembly system in the present invention.

[0090] In the picture:

[0091] 1. AGV trolley; 2. Integrated control cabinet; 3. Robot control cabinet; 4. Robot body; 5. Robot bearing connection column; 6. Robot end connection flange;

[0092] 7. End effector; 71. Force sensor; 72. Force sensor connection flange; 73. Quick-change mechanism; 74. Acceleration sensor; 75. Radar;

[0093] 76. Adsorption mechanism; 761. Adsorption frame; 762. Suction cup assembly;

[0094] 77. Towing mechanism; 771. Cylinder; 772. Telescopic shaft assembly; 773. Clamping and towing frame; 774. Driving clamping claw assembly;

[0095] 8. Assemble parts. DETAILED DESCRIPTION

[0096] To make the above-mentioned objectives, features, and advantages of the present invention more clearly understood, the present invention is further described below in detail with reference to the accompanying drawings and specific embodiments. This will enable a full understanding of how this application uses technical means to solve technical problems and achieve technical effects, and to implement the invention accordingly.

[0097] Example 1

[0098] Please refer to Figure 1-Figure 4, this embodiment proposes an adaptive human-machine collaborative assembly system for large-component mechanisms of heavy-duty industrial robots, which consists of an AGV trolley 1, an integrated control cabinet 2, a robot control cabinet 3, a robot body 4, a robot load-bearing connecting column 5, a robot end connection flange 6, an end actuator 7, and an assembly component 8. Among them, the AGV trolley 1 moves to the placement position of the assembly component 8, the robot body 4 moves, and drives the end actuator 7 to reach the designated position of the assembly component 8 to be adsorbed and clamped. After that, the adsorption mechanism 76 adsorbs the assembly component 8 through the suction cup assembly 762, and then the tightening mechanism 77 tightens the assembly component 8 by driving the clamping claw assembly 774. Finally, the robot body 4 moves to move the assembly component 8 away from its placement position, and finally completes the subsequent assembly, fitting, alignment, and bolt tightening operations of the assembly component 8. The details are as follows:

[0099] The AGV 1 is a high-load, wide-area mobile platform designed to receive external position movement commands and move to a designated location. Its upper front end carries the robot body 4 via a robot-carrying connecting column 5, while its upper rear end houses an integrated control cabinet 2 and a robot control cabinet 3. The integrated control cabinet 2 is a hardware integration and software centralized management system used to implement sensor signal processing, electrical control, and robot algorithm control. It is the key to the system and is mounted at the rear end of the AGV 1. The robot control cabinet 3 and the robot body 4 form a large-range, multi-degree-of-freedom, heavy-duty industrial robot system. This system integrates algorithms such as high-precision servo drive control, and a communication connection is established between the two. The robot control cabinet 3 controls the movement of the robot body 4. The end of the robot body 4 is connected to the end effector 7 via a robot end connection flange 6. It receives control commands from the robot control cabinet 3 and drives the end effector 7 to move.

[0100] The robot supporting connecting column 5 is a component that connects the AGV trolley 1 and the robot body 4, and is used to fix and support the robot body 4. The robot end connecting flange 6 is a component that connects the robot body 4 and the end effector 7, and is used to fix the end effector 7. The end effector 7 uses the suction mechanism 76 located on its lower side to suck the assembly part 8 and the towing mechanism 77 to tow the assembly part 8 to prevent it from falling. The assembly part 8 is selected according to different assembly objects according to the assembly task. The assembly part 8 is fixed by suction by the suction mechanism 76 of the end effector 7 and is towed and clamped by the towing mechanism 77. The robot body 4 moves to make the assembly part 8 reach the assembly posture point, completing the specified assembly task.

[0101] More specifically, Figure 2 As shown, the end effector 7 includes a force sensor 71, a force sensor connecting flange 72, a quick-change mechanism 73, an acceleration sensor 74, a radar 75, an adsorption mechanism 76 and a towing mechanism 77, wherein:

[0102] Force sensor 71 is a six-dimensional force sensor capable of simultaneously measuring the triaxial forces and triaxial moments of an object in three-dimensional space. One end of force sensor 71 is connected to robot body 4 via robot end connection flange 6, and the other end is connected to quick-change mechanism 73 via force sensor connection flange 72. The sensor receives triaxial force and triaxial moment data on components below force sensor 71 and transmits the data to integrated control cabinet 2, which performs filtering, control, and other algorithmic processing on the data.

[0103] Force sensor connection flange 72 is mounted on force sensor 71 and connects to quick-change mechanism 73. Quick-change mechanism 73 is an electromechanical coupling system that enables automatic replacement of end effectors. Its core consists of a standard flange interface, an electro-hydraulic drive module, and a communication bus. Quick-change mechanism 73 connects force sensor connection flange 72 and suction mechanism 76, and is used to replace end effector 7 and grasp different assembly components 8.

[0104] The acceleration sensor 74 is a six-axis acceleration sensor that measures the three-axis acceleration and three-axis angular velocity of an object's motion based on the principle of inertia. The acceleration sensor 74 is installed and fixed on the upper end of the adsorption mechanism 76 to perform real-time data transmission and communication, receive the three-axis acceleration and three-axis angular velocity data of the adsorption mechanism 76 at a specified position, and transmit the data to the integrated control cabinet 2. The integrated control cabinet 2 performs filtering, control and other algorithm processing on the data.

[0105] Radar 75 is an active optical remote sensing sensor that achieves high-precision three-dimensional environmental perception by emitting laser pulses and analyzing reflected signals. Radar 75 is installed in pairs on the left and right sides of the adsorption mechanism 76 to carry out real-time data transmission and communication to ensure that the two sides of the end effector 7 will not touch other surrounding objects during the assembly process, thereby ensuring assembly safety.

[0106] The suction mechanism 76 is the mechanism for the end effector 7 to suck and fix the assembly part 8. It is connected to the lower end of the quick-change mechanism 73. The radar 75 is installed and fixed on its left and right sides. The lower end of the suction mechanism is fixed with six pairs of suction cups distributed in an array, which mainly play the role of sucking and fixing the assembly part 8. More specifically, as Figure 3 As shown, the suction mechanism 76 consists of a suction frame 761 and a suction cup assembly 762. The suction frame 761 serves as the supporting structure for the suction mechanism 76, primarily supporting and securing it. The suction cup assembly 762 is a vacuum suction cup assembly with six pairs of suction cups arranged in an array and fixed to the lower end of the suction frame 761. Because the suction cup assembly is compliant and consists of twelve suction cups, it can adhere to curved components. The number and layout of the suction cups can be adjusted appropriately based on the desired assembly task.

[0107] The towing mechanism 77 is a mechanism for the end effector 7 to tow the clamping assembly part 8. The towing mechanism 77 is connected in pairs to the front and rear sides of the adsorption mechanism 76. The towing mechanism 77 drives the clamping claw assembly 774 to move through the extension and contraction of the cylinder 771, mainly playing the role of towing the clamping assembly part 8. More specifically, as Figure 4 As shown, the towing mechanism 77 is composed of a cylinder 771, a telescopic shaft assembly 772, a clamping and towing frame 773 and a driving clamping claw assembly 774, wherein:

[0108] The cylinder 771 is an actuator that converts compressed air energy into a straight line. It is installed and fixed on the clamping and towing frame 773, and the end telescopic drive drives the clamping jaw assembly 774 to move; the telescopic shaft assembly 772 is an auxiliary telescopic assembly, installed and fixed on the clamping and towing frame 773, and the auxiliary telescopic drive drives the clamping jaw assembly 774 to move; the clamping and towing frame 773 is a supporting and fixing structure of the towing mechanism 77, which mainly plays the role of supporting the towing mechanism 77; the driving clamping jaw assembly 774 is a component that tows and clamps the assembly part 8, which is installed and fixed on the cylinder 771 and the telescopic shaft assembly 772, and the clamping jaw assembly 774 is driven to move by the telescopic drive of the cylinder 771, which mainly plays the role of towing and clamping the assembly part 8.

[0109] In this embodiment, the AGV trolley 1 moves to the placement position of the assembly component 8, and the robot body 4 moves, driving the end actuator 7 to reach the specified position of the assembly component 8 to be adsorbed and clamped. Then, the adsorption mechanism 76 adsorbs the assembly component 8 through the suction cup assembly 762, and then the tightening mechanism 77 tightens the assembly component 8 by driving the clamping claw assembly 774. Finally, the robot body 4 moves to move the assembly component 8 away from its placement position, thereby completing the fixation of the assembly component 8.

[0110] This embodiment combines the high flexibility of the robot, the large range of the AGV and the high-precision feedback of multiple sensors, and then combines the closed-loop control of the force / position / acceleration three domains to achieve a synergistic breakthrough in the collaborative motion architecture, dynamic stiffness adjustment mechanism and multi-sensor data fusion strategy, realizing the unity of full-domain accessibility and high-precision positioning, greatly improving the assembly efficiency of the system.

[0111] Example 2

[0112] Based on the adaptive human-machine collaborative assembly system proposed in Example 1, please refer to Figure 5This embodiment proposes a control method for implementing the system. First, the AGV trolley 1 moves to the position for taking the assembly component 8. The robot body 4 moves to drive the end effector 7 to the placement position of the assembly component 8 to be adsorbed and clamped. The adsorption mechanism 76 adsorbs the assembly component 8 through the suction cup assembly 762, and then the tightening mechanism 77 tightens the assembly component 8 by driving the clamping claw assembly 774 to complete the fixation of the assembly component 8. Secondly, the robot body 4 moves to move the assembly component 8 away from its placement position. The AGV trolley 1 moves to the position to be assembled. After reaching this position, the operator applies force to the end. The force sensor 71 and the acceleration sensor 74 communicate and transmit data in real time to the integrated control cabinet 2. The integrated control cabinet 2 performs algorithm processing on the data, controls the movement of the robot body 4, drives the end effector 7 and the assembly component 8 to move, that is, continuously adjusts the posture until the specified assembly, alignment, and bolt tightening operations are completed. Finally, the tightening mechanism 77 opens, the adsorption mechanism 76 releases, and the robot body 4 exits, completing the assembly. Specifically, the following steps are included:

[0113] S1, based on the force sensor 71 receiving the three-axis force and three-axis moment of the components below it, and identifying the gravity center parameters of the components below the force sensor 71, so as to compensate the components below the force sensor 71 in real time, and obtain the actual three-axis force (f) after real-time monitoring and compensation generated by the external force on the components below the force sensor 71 x 、f y 、f z ) and actual triaxial moment (t x , t y , t z ). The components below the force sensor 71 include a force sensor connecting flange 72, a quick-change mechanism 73, an acceleration sensor 74, a radar 75, an adsorption mechanism 76, and a tightening mechanism 77. As a preferred embodiment of step S1, step S1 specifically includes:

[0114] S11, the integrated control cabinet 2 receives data from the force sensor 71 and the robot control cabinet 3, and receives data corresponding to 16 positions, wherein the data of the force sensor 71 are respectively the uncompensated three-axis force (f xi 、f yi 、f zi ) and uncompensated three-axis moments (t xi , t yi , t zi ), the corresponding data of the robot control cabinet 3 are (X i 、Y i , Z i 、A i 、B i 、C i ).

[0115] S12, processing the data received in step S11, the formula is expressed as:

[0116]

[0117] Among them, (l x 、l y 、l z ) is the position of the center of gravity of the component below the force sensor relative to the center point of the force sensor; (f x0 、f y0 、f z0 , t x0 , t y0 , t z0 ) is the zero drift value of the force sensor.

[0118] The unknown quantity (l x 、l y 、l z ) and (f x0 、f y0 、f z0 , t x0 , t y0 , t z0 );

[0119] The weight of the components below the force sensor 71 can be calculated using the following formula:

[0120]

[0121] Among them, I 3×3 is a third-order unit matrix; G is the weight of the components below the force sensor; is the rotation transformation matrix of the force sensor at each position relative to the robot base, through (A i 、B i 、C i ) to be calculated; α and β are the roll angle and pitch angle of the robot base relative to the world coordinate system respectively.

[0122] The unknown quantities G, α and β are solved by the least squares method.

[0123] S13. According to step S11 and step S12, the actual external force and torque applied by the operator are calculated. First, gravity conversion is performed on the components below the force sensor 71. The formula is expressed as:

[0124]

[0125] Among them, (G x , G y , G z) is the real-time component force of the following components of the force sensor in the force sensor coordinate system; It is the real-time rotation transformation matrix of the force sensor relative to the robot body base, which is obtained through the real-time data (A, B, C) of the robot control cabinet 3.

[0126] Combining the above, the actual external force and moment are calculated, and the formula is expressed as:

[0127]

[0128] Among them, (f x_s 、f y_s 、f z_s , t x_s , t y_s , t z_s ) is the real-time force sensor data; (f x 、f y 、f z , t x , t y , t z ) is to calculate the actual external force and torque applied by the real-time operator, that is, to monitor the actual three-axis force and actual three-axis torque after compensation in real time.

[0129] S2, based on the acceleration sensor 74 receiving the actual three-axis acceleration (a) after compensation at the specified assembly point of the end actuator 7, x 、a y 、a z ) and the corresponding actual three-axis angular acceleration (a a 、a b 、a c As a preferred implementation of step S2, step S2 specifically includes:

[0130] S21, the integrated control cabinet 2 receives data from the acceleration sensor 74 and the robot control cabinet 3, and receives data corresponding to 16 positions, wherein the data of the acceleration sensor 74 is the uncompensated three-axis acceleration (a xi 、a yi 、a zi ), the corresponding data of the robot control cabinet 3 are (X i 、Y i , Z i 、A i 、B i 、C i ).

[0131] S22, for the uncompensated three-axis acceleration (a xi 、a yi 、a zi) is processed, first the gravitational acceleration g is processed, and the formula is expressed as:

[0132]

[0133] Among them, (g xi 、g yi 、g zi ) is the acceleration component of the gravitational acceleration g corresponding to each position in the robot terminal coordinate system; is the rotation transformation matrix of the acceleration sensor at each position relative to the robot base, through (A i 、B i 、C i ) and the rotation transformation matrix of the acceleration sensor relative to the end of the robot body are calculated.

[0134] The scale factors and zero drift values of each axis of the acceleration sensor 74 in its own coordinate system are expressed as follows:

[0135]

[0136] Among them, (k x 、k y 、k z ) is the scale factor of each axis of the acceleration sensor in its own coordinate system; (a x0 、a y0 、a z0 ) is the zero drift value of each axis of the acceleration sensor in its own coordinate system.

[0137] The unknown quantity (k x 、k y 、k z ) and (a x0 、a y0 、a z0 ).

[0138] S23. According to steps S21, S22 and S23, the actual linear acceleration is calculated. The formula is:

[0139]

[0140] Among them, (a x_s 、a y_s 、a z_s ) is the real-time acceleration sensor data; (g x 、g y 、g z ) is the real-time acceleration of gravity g in the robot terminal coordinate system; (a x 、a y 、a z) is to calculate the real-time three-axis acceleration at the specified position, that is, to monitor the actual three-axis acceleration after compensation in real time.

[0141] S24, for solving the actual three-axis angular acceleration (a a 、a b 、a c ), which can be solved by the central difference method;

[0142] Before solving the problem, it is necessary to obtain the zero drift value of each axis of the acceleration sensor 74. This can be obtained by the static method. The acceleration sensor 74 is kept stationary for a long time to obtain the zero drift value of each axis (w a0 、w b0 、w c0 );

[0143] Then the actual three-axis angular acceleration is obtained by central difference, and the formula is expressed as:

[0144]

[0145] Where Δt is the time interval; (w a_si+1 、w b_si+1 、w c_si+1 ) and (w a_si-1 、w b_si-1 、w c_si-1 ) are the uncompensated three-axis angular velocities obtained at the previous moment and the next moment respectively; (a a 、a b 、a c ) is to calculate the three-axis angular acceleration at the specified position in real time, that is, to monitor the actual three-axis angular acceleration after compensation in real time.

[0146] S3: Based on the actual three-axis forces, actual three-axis torques, actual three-axis accelerations, and actual three-axis angular accelerations, an admittance control model is established as the assembly task control algorithm. The current end position of the robot body 4 is continuously corrected through feedback from the admittance control model until the assembly process of the assembly component 8 is completed. As a preferred embodiment of step S3, step S3 specifically includes:

[0147] S31. Establish an admittance control model for the robot body 4 when performing one-dimensional translation motion, expressed as:

[0148]

[0149] Among them, f ext is the interaction force, i.e. the force applied by the operator, is the actual triaxial force (f x 、f y 、f z ) one; m is the virtual mass; c is the virtual damping; They represent the acceleration and velocity of the robot end respectively; a is the acceleration, that is, the acceleration at the specified position, and is the actual three-axis acceleration (a) obtained in step S2. x 、a y 、a z )one.

[0150] S32. By analyzing the above formula and combining it with the actual operation situation, in order to achieve collaborative work between the operator and the robot, when the virtual damping c is set to a high value, the operator can perform fine movements more easily, which is suitable for situations where the acceleration is relatively small; conversely, when the virtual damping c is set to a low value, the operator can move the robot quickly with less force to achieve fast dragging, but it is more difficult to perform fine movements.

[0151] According to the above strategy, the virtual damping c is controlled, wherein the virtual damping c adopts the following change rule:

[0152]

[0153] Among them, Δc is the virtual damping adjustment amplitude, which defines the maximum change of damping; γ is the acceleration sensitivity coefficient, which is used to adjust the amplification factor of the acceleration signal; As the acceleration parameter benchmark, the actual acceleration dimension is converted into a dimensionless ratio; c d is the initial virtual damping; c v is the nonlinear time-varying effective virtual damping; is the absolute value of the acceleration of the robot end.

[0154] This virtual damping change rule changes the mutation defect of traditional linear regulation. When the tanh function has an approximate linear characteristic (slope ) ensures the sensitivity of small acceleration input; when When , the function saturation characteristic limits the damping change to within the range of ±Δc, effectively avoiding parameter loss of control under extreme working conditions.

[0155] S33. According to step S31 and step S32, combined with the actual operation situation, when the virtual mass m is set to a high value, the operator applies the same force, and the acceleration of the model with the high virtual mass m setting does not change significantly, which is suitable for high-speed movement of the robot and is not easily affected by speed fluctuations; on the contrary, when the virtual mass m is set to a low value, the operator applies the same force, and the acceleration of the model with the low virtual mass m setting changes significantly, the speed changes significantly, and the sensitivity is high.

[0156] According to the above strategy, the virtual mass m is controlled, wherein the virtual mass m adopts the following change rules:

[0157]

[0158] Among them, μ(c v ) is the virtual damping coupling variable, Its mathematical properties guarantee that m v / c v The ratio always satisfies This ensures that the system is extremely Located in the stable region, where the system pole is the critical point between the stable region and the unstable region; k is the speed exponential factor, which is used to adjust the intensity of the influence of speed on virtual mass; is the maximum permissible speed; m d is the initial virtual mass; m v is the nonlinear time-varying effective virtual mass; is the absolute value of the velocity of the robot end.

[0159] The virtual mass change rule has a dynamic inertia compensation mechanism. The item realizes speed-aware quality adjustment, which can maintain system sensitivity at low speeds and suppress inertia overshoot at high speeds.

[0160] S34. According to step S31, establish an admittance control model for the robot body 4 when performing one-dimensional rotational motion. The expression is:

[0161]

[0162] Among them, t ext is the interaction torque, i.e. the torque applied by the operator, is the actual three-axis torque obtained in step S1 (t x , t y , t z ) one of them; α is the virtual moment of inertia; β is the virtual rotation damping; They represent the angular acceleration and angular velocity of the robot end respectively; a is the angular acceleration, that is, the angular acceleration of the specified position, and is the actual three-axis angular acceleration (a) obtained in step S2. a 、a b 、a c )one.

[0163] The analysis of the admittance control model for one-dimensional rotational motion is similar to that for one-dimensional translational motion.

[0164] S35: The operator uses human-machine collaboration to move assembly component 8, achieving smooth human-machine operation by controlling the changes in admittance parameters (m, c). During large-scale dragging, because the speed is high, the virtual mass m is at a high value, the speed is stable, and the virtual damping c is at a low value, the operator can achieve rapid dragging. Subsequently, during refined dragging, because the speed is slow, the virtual mass m is at a low value, the speed changes significantly, and the virtual damping c is at a high value, the operator can achieve precise assembly and docking. When assembly component 8 reaches the predetermined position, assembly is considered complete.

[0165] This embodiment uses force sensors and acceleration sensors, combined with the variable admittance control method of human-machine collaboration, to construct a dynamic and compliant perception network for human-machine collaboration. While maintaining the high load characteristics of the heavy-load robot, it achieves millimeter-level contact force feedback precision control, significantly improving the smoothness and operational safety of human-machine collaboration.

[0166] In summary, the present invention proposes an adaptive human-machine collaborative assembly system for large-component mechanisms of heavy-duty industrial robots and a control method thereof. Through the collaborative mechanism of multi-sensor data fusion and intelligent variable admittance algorithm, the flexible assembly of large-component mechanisms and the real-time feedback of force sensors and acceleration sensors are achieved, which effectively solves the problems of component deformation and stress concentration caused by factors such as position deviation in the rigid assembly process of traditional industrial robots, thereby greatly improving the assembly qualification rate of large-component mechanisms.

[0167] Those skilled in the art will appreciate that all or part of the steps in the above-mentioned embodiment methods can be accomplished by instructing the relevant hardware through a program. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0168] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the same or similar parts between the embodiments. For the above embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiments.

[0169] The above embodiments provide a detailed introduction to the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A control method for a heavy-duty industrial robot large component mechanism adaptive human-machine collaborative assembly system, characterized in that: The following steps are involved: S1. Based on the force sensor, the force sensor receives the three-axis force and three-axis moment exerted on the components below it, and identifies the gravity center parameters of the components below the force sensor, thereby performing real-time compensation for the components below the force sensor, and obtaining the actual three-axis force and actual three-axis moment after real-time monitoring and compensation generated by the external force on the components below the force sensor; S2, based on the acceleration sensor receiving the end effector at the specified assembly posture point, real-time monitoring of the compensated actual three-axis acceleration and the corresponding actual three-axis angular acceleration; S3. Based on the actual three-axis force, actual three-axis torque, actual three-axis acceleration, and actual three-axis angular acceleration, an admittance control model is established as the assembly task control algorithm. The current end position of the robot body is continuously corrected through feedback from the admittance control model until the assembly process is completed.

2. The control method according to claim 1, characterized in that: The following components of the force sensor include: a force sensor connecting flange, a quick-change mechanism, an acceleration sensor, a radar, an adsorption mechanism, and a tightening mechanism.

3. The control method according to claim 1, wherein: In step S1, the specific process includes the following steps: S11, the integrated control cabinet receives data from the force sensor and the robot control cabinet, wherein the data of the force sensor are the uncompensated three-axis force (f xi 、f yi 、f zi ) and uncompensated three-axis moments (t xi , t yi , t zi ), the corresponding robot control cabinet data are (X i 、Y i , Z i 、A i 、B i 、C i ); S12, processing the data received in step S11, the formula is expressed as: Among them, (l x 、l y 、l z ) is the position of the center of gravity of the component below the force sensor relative to the center point of the force sensor; (f x0 、f y0 、f z0 , t x0 , t y0 , t z0 ) is the zero drift value of the force sensor; The unknown quantity (l x 、l y 、l z ) and (f x0 、f y0 、f z0 , t x0 , t y0 , t z0 ); The formula for calculating the weight of the following components of the force sensor is: Among them, I 3×3 is a third-order unit matrix; G is the weight of the components below the force sensor; is the rotation transformation matrix of the force sensor at each position relative to the robot base; α and β are the roll angle and pitch angle of the robot base relative to the world coordinate system, respectively; Solve the unknown quantities G, α and β by the least square method; S13, according to step S11 and step S12, calculating the actual external force and torque applied by the real-time operator; First, perform gravity conversion on the following components of the force sensor, and the formula is expressed as: Among them, (G x , G y , G z ) is the real-time component force of the following components of the force sensor in the force sensor coordinate system; The real-time rotation transformation matrix of the force sensor relative to the robot base; Combining the above, the actual external force and torque are calculated, and the formula is expressed as: Among them, (f x_s 、f y_s 、f z_s , t x_s , t y_s , t z_s ) is the real-time force sensor data; (f x 、f y 、f z , t x , t y , t z ) is to calculate the actual external force and torque applied by the real-time operator, that is, to monitor the actual three-axis force and actual three-axis torque after compensation in real time.

4. The control method according to claim 1, wherein: In step S2, the specific process includes the following steps: S21, the integrated control cabinet receives data from the acceleration sensor and the robot control cabinet, wherein the data of the acceleration sensor is the uncompensated three-axis acceleration (a xi 、a yi 、a zi ), the corresponding robot control cabinet data are (X i 、Y i , Z i 、A i 、B i 、C i ); S22, for the uncompensated three-axis acceleration (a xi 、a yi 、a zi ) for processing; First, the gravitational acceleration g is processed and the formula is expressed as: Among them, (g xi 、g yi 、g zi ) is the acceleration component of the gravitational acceleration g corresponding to each position in the robot terminal coordinate system; is the rotation transformation matrix of the acceleration sensor at each position relative to the robot base; For the scale factor and zero drift value of each axis of the accelerometer in its own coordinate system, the formula is expressed as: Among them, (k x 、k y 、k z ) is the scale factor of each axis of the acceleration sensor in its own coordinate system; (a x0 、a y0 、a z0 ) is the zero drift value of each axis of the acceleration sensor in its own coordinate system; The unknown quantity (k x 、k y 、k z ) and (a x0 、a y0 、a z0 ); S23. According to steps S21, S22 and S23, the actual linear acceleration is calculated. The formula is: Among them, (a x_s 、a y_s 、a z_s ) is the real-time acceleration sensor data; (g x 、g y 、g z ) is the real-time acceleration of gravity g in the robot terminal coordinate system; (a x 、a y 、a z ) is to calculate the real-time three-axis acceleration at the specified position, that is, to monitor the actual three-axis acceleration after compensation in real time; S24, solve the actual three-axis angular acceleration (a a 、a b 、a c ), the formula is: Where Δt is the time interval; (w a0 、w b0 、w c0 ) is the zero drift value of each axis when the acceleration sensor is stationary for a long time; (w a_si+1 、w b_si+1 、w c_si+1 ) and (w a_si-1 、w b_si-1 、w c_si-1 ) are the uncompensated three-axis angular velocities obtained at the previous moment and the next moment respectively; (a a 、a b 、a c ) is to calculate the three-axis angular acceleration at the specified position in real time, that is, to monitor the actual three-axis angular acceleration after compensation in real time.

5. The control method according to claim 1, characterized in that: In step S3, the specific process includes the following steps: S31. Establish the admittance control model when the robot body performs one-dimensional translation motion, which is expressed as: Among them, f ext is the interaction force, i.e. the force applied by the operator, is the actual triaxial force (f x 、f y 、f z ) one; m is the virtual mass; c is the virtual damping; They represent the acceleration and velocity of the robot end respectively; a is the acceleration, that is, the acceleration at the specified position, and is the actual three-axis acceleration (a) obtained in step S2. x 、a y 、a z )one; S32, control the virtual damping c, when When the tanh function has an approximate linear characteristic (slope ) ensures the sensitivity of small acceleration input; when When , the function saturation characteristic limits the damping change to the range of ±Δc, effectively avoiding parameter loss of control under extreme working conditions; The virtual damping c adopts the following change rules: Among them, Δc is the virtual damping adjustment amplitude; γ is the acceleration sensitivity coefficient; is the acceleration parameter benchmark; c d is the initial virtual damping; c v is the nonlinear time-varying effective virtual damping; is the absolute value of the acceleration of the robot end; S33, controlling the virtual mass m to achieve speed-aware mass adjustment, which can maintain system sensitivity at low speeds and suppress inertia overshoot at high speeds; The virtual mass m adopts the following change rules: Among them, μ(c v ) is the virtual damping coupling variable; k is the velocity exponential factor, which is used to adjust the intensity of the influence of velocity on the virtual mass; is the maximum permissible speed; m d is the initial virtual mass; m v is the nonlinear time-varying effective virtual mass; is the absolute value of the velocity of the robot end; S34. Establish the admittance control model when the robot body performs one-dimensional rotation motion. The expression is: Among them, t ext is the interaction torque, i.e. the torque applied by the operator, is the actual three-axis torque obtained in step S1 (t x , t y , t z ) one of them; α is the virtual moment of inertia; β is the virtual rotation damping; They represent the angular acceleration and angular velocity of the robot end respectively; a is the angular acceleration, that is, the angular acceleration of the specified position, and is the actual three-axis angular acceleration (a) obtained in step S2. a 、a b 、a c )one; S35. The operator performs human-machine collaboration to push the assembly parts to move, and realizes human-machine compliant operation by controlling the changes of admittance parameters (m, c).

6. The control method according to claim 5, characterized in that: In step S35, specifically: During large-scale dragging, because the speed needs to be fast, the virtual mass m is at a high value, the speed is stable, and the virtual damping c is at a low value, the operator can achieve fast dragging; During the subsequent refined dragging process, because the speed needs to be slow, the virtual mass m is at a low value, the speed changes significantly, and the virtual damping c is at a high value, the operator can achieve precise assembly and docking.

7. An adaptive human-machine collaborative assembly system applied to the control method according to any one of claims 1 to 6, characterized in that: include: The AGV trolley (1) is a high-load wide-area mobile platform trolley for receiving external position movement instructions and moving to a specified position point. The upper front end of the trolley carries a robot body (4) through a robot carrying connecting column (5), and the upper rear end of the trolley carries an integrated control cabinet (2) and a robot control cabinet (3). The end effector (7) includes an adsorption mechanism (76) and a towing mechanism (77) located on its lower side, which are respectively used to adsorb and fix any surface of the assembly component (8) and to drag and clamp any opposite side thereof to achieve synchronous fixation and grasping; The end of the robot body (4) is connected to the end actuator (7) via the robot end connecting flange (6), receives the control instruction from the robot control cabinet (3) and drives the end actuator (7) to move; The assembly component (8) selects different assembly objects according to the assembly task, and the robot body (4) moves to make the assembly component (8) reach the assembly posture point to complete the specified assembly task.

8. The adaptive human-machine collaborative assembly system according to claim 7, characterized in that: The end effector (7) further comprises: A force sensor (71) connected to the robot body (4) via a connecting flange (6) at the end of the robot is capable of synchronously measuring the three-axis force and three-axis moment of an object in three-dimensional space, receiving the three-axis force and three-axis moment data of the components below the force sensor (71), and then transmitting the data to the integrated control cabinet (2); A force sensor connecting flange (72) is mounted on the force sensor (71) and is used to connect to the quick-change mechanism (73); A quick-change mechanism (73) is a component connecting the force sensor connecting flange (72) and the adsorption mechanism (76), and is used to replace the end effector (7) to achieve the grasping of different assembly parts (8); An acceleration sensor (74) is used to measure the three-axis acceleration and three-axis angular velocity of the object's motion. The acceleration sensor (74) is fixedly mounted on the upper end of the adsorption mechanism (76), receives the three-axis acceleration and three-axis angular velocity data of the adsorption mechanism (76) at a specified position, and transmits the data to the integrated control cabinet (2); Radars (75) are installed in pairs on the left and right sides of the adsorption mechanism (76) to perform real-time data transmission and communication, ensuring that the two sides of the end actuator (7) will not touch other surrounding objects during the assembly process.

9. The adaptive human-machine collaborative assembly system according to claim 7, characterized in that: The adsorption mechanism (76) includes an adsorption frame (761) as its supporting and fixing structure, and a suction cup assembly (762) distributed in an array and fixed at the lower end of the adsorption frame (761). The suction cup assembly (762) is a vacuum suction cup assembly composed of at least six pairs of suction cups.

10. The adaptive human-machine collaborative assembly system according to claim 7, characterized in that: The tightening mechanism (77) comprises: The cylinder (771) is an actuator that converts compressed air energy into a linear motion and is mounted on the clamping and tightening frame (773). The end of the cylinder is telescopically driven to move the clamping jaw assembly (774). The telescopic shaft assembly (772) is an auxiliary telescopic assembly, which is fixed on the clamping and tightening frame (773) and drives the clamping claw assembly (774) to move. An adsorption mechanism frame (773), which is a supporting and fixing structure of the towing mechanism (77); The driving clamping claw assembly (774) is a component for dragging and clamping the assembly component (8), which is fixed on the cylinder (771) and the telescopic shaft assembly (772), and is driven to move the clamping claw assembly (774) by the telescopic drive of the cylinder (771).

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