Man-machine operation cooperation method oriented to prefabricated part installation and based on operation intention

Abstract

The invention belongs to the technical field of building robots for heavy-load assembly, and particularly discloses a man-machine operation cooperation method based on operation intention for prefabricated part installation. The method comprises the steps that firstly, an improved shear thickening fluid dynamic model and an improved robot kinematics dynamic model are built, and a robot control model is built according to the two dynamic models; thirdly, collecting data to train a robot control model; then, an operator continuously adjusts the positions of a sensor and an operation handle at a robot end effector according to the actual position on site and then executes installation operation; and finally, continuously transmitting the operation data to the robot control model, analyzing the operation intention, optimizing the parameters, obtaining an action scheme, and executing the action scheme until the end. According to the invention, the robot can accurately capture the operation intention of the operator, and the flexibility and efficiency of the assembly operation are improved. The method can be widely applied to man-machine cooperation of prefabricated part installation.

Description

Technical Field

[0001] The present invention belongs to the technical field of heavy-load assembly construction robots, and in particular relates to a human-machine collaborative operation method based on operation intention for prefabricated component installation. Background Art

[0002] With the continuous advancement of urbanization and the rapid development of the economy, modern construction projects are becoming more and more large-scale, complex and intelligent. In the field of prefabricated component installation, the problem of large size and heavy weight of prefabricated components is often encountered during the assembly of prefabricated components.

[0003] At present, the assembly of prefabricated components is mostly done with the help of heavy-duty assembly robots, in which the end effector of the robot is used to pick up and place prefabricated components, and the operator controls the robot to install the prefabricated components through the operating handle on the end effector based on force feedback. In this operation mode, the sensor is usually placed at the end of the robot arm. When dealing with medium and large-sized prefabricated building components, the sensor position is fixed and the operator's operating position will be restricted during the force interaction between the human and the robot, which can easily lead to obstruction of vision, causing inconvenience and trouble to the operation process.

[0004] In the field of robot compliance control, impedance control is highly dependent on the precise construction of dynamic models. In the process of human-machine collaboration, only fixed impedance parameters can be set, and it is difficult to flexibly and dynamically adapt to real-time changes in the field. For example, the Chinese invention patent with application publication number CN115026820A discloses a human-machine collaborative assembly robot control system and control method. Although it has the advantages of less development difficulty and low cost, this method has obvious shortcomings under complex working conditions. It fails to fully consider the impact of the operator's impedance model in the process of human-machine collaboration, which weakens the operator's sense of presence to a certain extent. Therefore, it is necessary to break through the inherent installation mode of traditional sensors, fully consider the impedance characteristics of humans, and design a human-machine collaborative operation method based on operational intentions. ‌‌ Summary of the invention

[0005] The purpose of the present invention is to provide a human-machine collaborative operation method based on operation intention for prefabricated component installation, establish and train a dynamic model of thickening fluid control, and combine it with a sensor that can change position with the operating handle, so that the robot can accurately capture the operator's operation intention, thereby improving the flexibility and efficiency of the assembly operation.

[0006] In order to achieve the above object, the technical scheme adopted by the present invention is as follows:

[0007] A human-machine collaborative operation method for prefabricated component installation based on operation intention includes the following steps performed in sequence:

[0008] S1. Establish an improved shear thickening fluid dynamics model and a robot kinematics dynamics model, and build a robot control model based on the two dynamics models;

[0009] S2, collect human body impedance parameter data and robot dynamic parameter data of individuals of different height ranges and weight levels in each link of the operation process;

[0010] S3, inputting the human body impedance parameter data and the robot dynamic parameter data into the robot control model, and using the machine learning algorithm to train and improve the robot control model to generate a trained robot control model;

[0011] S4. The operator performs the installation operation after adjusting the position of the sensor and the operating handle of the robot end effector according to the actual position on site;

[0012] S5, receiving the operation data transmitted from the operating handle, transmitting the operation data to the trained robot control model, and generating operation intention data;

[0013] S6. Based on the operation intention data, adjust the human body impedance parameters and robot dynamic parameters in the trained robot control model, generate robot execution data and perform corresponding actions;

[0014] S7. Whether the operator has any follow-up operations;

[0015] If yes, return to step S4;

[0016] If not, proceed to step S8;

[0017] S8. End.

[0018] As a limitation, the control formula of the shear thickening fluid dynamics model includes:

[0019]

[0020]

[0021] In the formula, is the external force applied to the object, is the speed of the controlled object, is the fictitious inertia term, is the system output value, is the viscosity of the system, is a power term, is the acceleration of the controlled object.

[0022] As a second limitation, in step S4, the sensor and the operating handle are installed at one end of a telescopic rod; the other end of the telescopic rod is rotatably connected to the end effector of the robot.

[0023] As a third limitation, the human body impedance parameters include: human body motion damping coefficient, human body motion stiffness coefficient and human body virtual mass.

[0024] As a fourth limitation, the robot dynamics parameters include: joint angle position parameters, joint angle acceleration, joint driving force, joint friction coefficient, joint stiffness coefficient, joint damping coefficient and viscosity parameters of the shear thickening fluid.

[0025] As a fifth limitation, the machine learning algorithm is a Transformer algorithm.

[0026] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0027] (1) The method of the present invention establishes and trains a robot control model, which can adaptively perform refined optimization processing on human body impedance parameters and robot dynamic parameters, so that the robot can accurately capture the operator's operating intention, thereby improving the flexibility and efficiency of assembly operations;

[0028] (2) In the method of the present invention, the sensor and the operating handle are installed at one end of a telescopic rod; the other end of the telescopic rod is rotatably connected to the end effector of the robot, and the operator can adjust the position of the sensor and the operating handle according to the actual position on site, thereby solving the problem of line of sight obstruction caused by the fixed position of the sensor, and greatly improving the convenience and efficiency of the operation;

[0029] (3) The method of the present invention mines the mapping relationship between human body impedance parameters and robot dynamic parameters, and can design a more accurate control strategy. It can enable the robot to interact with the external environment or the human body more smoothly and stably when performing tasks, reduce shock and vibration, and improve the precision and safety of operation;

[0030] (4) The method of the present invention uses the Transformer algorithm when selecting the machine learning algorithm, which can handle the complex nonlinear relationship between the human body impedance parameters and the robot dynamics parameters, and can improve the accuracy of the dynamic model.

[0031] The present invention belongs to the technical field of construction robots for heavy-load assembly. By establishing and training a dynamic model of thickening fluid control and combining it with a sensor that can change position with an operating handle, the robot can accurately capture the operator's operating intentions, thereby improving the flexibility and efficiency of assembly operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0033] In the attached picture:

[0034] Figure 1 is a processing flow chart of an embodiment of the present invention;

[0035] Figure 2 A schematic diagram of a robot control model according to an embodiment of the present invention;

[0036] Figure 3 Schematic diagram of human-machine collaboration in an embodiment of the present invention.

[0037] In the figure, 1. end effector, 2. telescopic rod, 3. six-dimensional force sensor, 4. operating handle. DETAILED DESCRIPTION

[0038] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0039] This embodiment is a human-machine collaborative operation method based on operation intention for prefabricated component installation. Figure 1 As shown, this embodiment includes the following steps performed in sequence:

[0040] S1. Establish an improved shear thickening fluid dynamics model and a robot kinematic dynamics model, and build a robot control model based on the two dynamics models.

[0041] Before establishing the shear thickening fluid dynamics model and the robot kinematic dynamics model, it is necessary to explore the mapping relationship between the human body impedance parameters and the robot dynamics parameters. Among them, the human body impedance parameters include: human body motion damping coefficient, motion stiffness coefficient, and human body virtual mass; the robot dynamics parameters include: joint angle position parameters, joint angle acceleration, joint driving force, joint friction coefficient, joint stiffness coefficient, joint damping coefficient, and viscosity parameters of the shear thickening fluid.

[0042] The governing equations for the shear thickening fluid dynamics model include:

[0043]

[0044]

[0045] In the formula, is the external force applied to the object, is the speed of the controlled object, is the fictitious inertia term, is the system output value, is the viscosity of the system, is a power term, is the acceleration of the controlled object.

[0046] The robot control model obtained is as follows Figure 2 As shown in the figure. The force feedback generated by the operator is integrated with the force of the external environment to form the total input force of the system. This input force is input into the robot control model, and the robot's movement speed is calculated. Then, the movement speed is inversely solved into the speed information of each joint of the robot using the mapping relationship between the inverse kinematic solution and the speed Jacobian. Finally, the driving force of each joint of the robot is calculated based on this speed through the PID speed controller, and then the robot is driven to perform the corresponding movement.

[0047] S2. Collect human body impedance parameter data and robot dynamic parameter data of individuals in different height ranges and weight levels in each link of the operation process.

[0048] S3. Input the human body impedance parameter data and the robot dynamic parameter data into the robot control model, and use the machine learning algorithm to train and improve the robot control model to generate a trained robot control model.

[0049] In this embodiment, the machine learning algorithm used is the Transformer algorithm. This reinforcement learning algorithm can handle the complex nonlinear relationship between human body impedance parameters and robot dynamic parameters, providing strong support for the accuracy and reliability of the dynamic model.

[0050] S4. The operator performs installation operation after adjusting the position of the sensor and the operating handle 4 on the robot end effector 1 according to the actual position on site.

[0051] like Figure 3 As shown, when the operator is installing the prefabricated components, he needs to stand at the end effector 1 of the robot to control the operating handle 4. In this embodiment, the sensor is a six-dimensional force sensor 3, and the six-dimensional force sensor 3 is installed on the operating handle 4. The operating handle 4 is rotatably connected to the end effector 1 of the robot through a telescopic rod 2. The telescopic rod 2 can be rotated and extended on the end effector 1, thereby changing the positions of the operating handle 4 and the six-dimensional force sensor 3.

[0052] In order to ensure the installation effect, the operator needs to constantly adjust the steering and telescopic length of the telescopic rod 2 according to the actual installation situation on site, so as to adjust the positions of the six-dimensional force sensor 3 and the operating handle 4.

[0053] S5. Receive the operation data transmitted from the operating handle 4, transmit the operation data to the trained robot control model, and generate operation intention data.

[0054] S6. Based on the operation intention data, adjust the human body impedance parameters and robot dynamic parameters in the trained robot control model, generate robot execution data and perform corresponding actions.

[0055] The operator will generate operation data on the operating handle 4. Previously, the robot control model has been trained based on the collected data. Based on the currently received operation data, the robot can accurately analyze the operation intention, and with the help of flexible adjustment of the human body impedance parameters and robot dynamic parameters, quickly obtain the optimal action plan and execute the corresponding movement.

[0056] S7. Whether the operator has any follow-up operations;

[0057] If yes, return to step S4;

[0058] If not, proceed to step S8.

[0059] S8. End.

[0060] In summary, this embodiment, by establishing and training a robot control model, combined with a six-dimensional force sensor 3 that can change position with the operating handle 4, can enable the robot to accurately capture the operator's operating intentions, thereby improving the flexibility and efficiency of assembly operations.

Claims

1. A human-machine collaborative operation method based on operation intention for prefabricated component installation, characterized in that: The process includes the following steps: S1. Establish an improved shear thickening fluid dynamics model and a robot kinematics dynamics model, and build a robot control model based on the two dynamics models; S2, collect human body impedance parameter data and robot dynamic parameter data of individuals of different height ranges and weight levels in each link of the operation process; S3, inputting the human body impedance parameter data and the robot dynamic parameter data into the robot control model, and using the machine learning algorithm to train and improve the robot control model to generate a trained robot control model; S4. The operator performs the installation operation after adjusting the position of the sensor and the operating handle of the robot end effector according to the actual position on site; S5, receiving the operation data transmitted from the operating handle, transmitting the operation data to the trained robot control model, and generating operation intention data; S6. Based on the operation intention data, adjust the human body impedance parameters and robot dynamic parameters in the trained robot control model, generate robot execution data and perform corresponding actions; S7. Whether the operator has any follow-up operations; If yes, return to step S4; If not, proceed to step S8; S8. End.

2. According to claim 1, a human-machine collaborative operation method based on operation intention for prefabricated component installation is characterized in that: The control formula of the shear thickening fluid dynamics model includes: ； ； In the formula, is the external force applied to the object, is the speed of the controlled object, is the fictitious inertia term, is the system output value, is the viscosity of the system, is a power term, is the acceleration of the controlled object.

3. The human-machine collaborative operation method based on operation intention for prefabricated component installation according to claim 1 is characterized in that: In step S4, the sensor and the operating handle are installed at one end of a telescopic rod; the other end of the telescopic rod is rotatably connected to the end effector of the robot.

4. A human-machine collaborative operation method based on operation intention for prefabricated component installation according to any one of claims 1 to 3, characterized in that: The human body impedance parameters include: human body motion damping coefficient, human body motion stiffness coefficient and human body virtual mass.

5. A human-machine collaborative operation method based on operation intention for prefabricated component installation according to any one of claims 1 to 3, characterized in that: The robot dynamics parameters include: joint angle position parameters, joint angle acceleration, joint driving force, joint friction coefficient, joint stiffness coefficient, joint damping coefficient and viscosity parameter of shear thickening fluid.

6. A human-machine collaborative operation method based on operation intention for prefabricated component installation according to any one of claims 1 to 3, characterized in that: The machine learning algorithm is the Transformer algorithm.

Images