A multi-axis coordination-based dynamic balance control method and device for assembly robot

Abstract

The application discloses a kind of based on multi-axis cooperation's dynamic balance control method and device of assembly robot, it is related to robot technical field.Processing of new energy automobile assembly, the motion data of each axis of assembly robot and the positioning data of component to be assembled are collected;Based on the positioning data collected, the position information of component to be assembled is determined using multi-sensor fusion positioning technology;According to position information and preset assembly path, the dynamic compensation of each axis is generated in combination with the motion data of each axis;Using multi-axis cooperation control strategy, the dynamic compensation is distributed to each axis, the coordinated motion of each axis of assembly robot is controlled, and dynamic balance assembly is realized.The dynamic balance assembly of assembly robot is difficult to realize in prior art, which affects the assembly quality and efficiency, and can cause the problems of inaccurate component installation and insecure connection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robots, and in particular to a dynamic balance control method and device for a joint assembly robot based on multi-axis cooperation. BACKGROUND

[0002] In the production of new energy vehicles, joint assembly robots are used for precise assembly of components such as batteries. The joint assembly robot usually has a multi-axis structure, and the independent movement of each axis and the cooperative movement state between the axes have a direct and key impact on the final assembly quality.

[0003] However, the prior art does not sufficiently analyze the dynamic characteristics of the joint assembly robot and the component to be assembled, and the movement of each axis of the joint assembly robot and the position change of the component to be assembled can cause instability in the assembly process, making it difficult to achieve dynamic balance assembly of the joint assembly robot, affecting the assembly quality and efficiency, and causing inaccurate installation and insecure connection of the component. SUMMARY

[0004] In the embodiments of the present application, by providing a dynamic balance control method for a joint assembly robot based on multi-axis cooperation, the problem of the prior art that it is difficult to achieve dynamic balance assembly of the joint assembly robot, affecting the assembly quality and efficiency, and causing inaccurate installation and insecure connection of the component is solved.

[0005] In a first aspect, the embodiments of the present application provide a dynamic balance control method for a joint assembly robot based on multi-axis cooperation, which comprises: collecting movement data of each axis of the joint assembly robot and positioning data of the component to be assembled in the assembly process of the new energy vehicle; determining the position information of the component to be assembled based on the collected positioning data by using a multi-sensor fusion positioning technology; generating a dynamic compensation amount for each axis according to the position information and a preset assembly path, in combination with the movement data of each axis; and distributing the dynamic compensation amount to each axis by using a multi-axis cooperative control strategy to control the cooperative movement of each axis of the joint assembly robot, thereby achieving dynamic balance assembly.

[0006] In a possible implementation manner, the collecting of the movement data of each axis of the joint assembly robot and the positioning data of the component to be assembled in the assembly process of the new energy vehicle comprises: collecting the movement position, speed and acceleration data of each axis in real time by using an encoder arranged on each axis of the joint assembly robot; and collecting the positioning data of the component to be assembled by using a visual positioning system and a laser tracker, wherein the visual positioning system adopts a binocular stereo camera to identify a body positioning hole, and the laser tracker compensates for deformation errors of the body caused by hoisting in real time.

[0007] In a possible implementation manner, the determination of the position information of the component to be assembled based on the collected positioning data by using the multi-sensor fusion positioning technology comprises: determining that the expression of the position information of the component to be assembled is: ;in, This provides the location information of the components to be assembled. The coordinates of the vehicle body positioning holes in the robot's base coordinate system are obtained through the vision positioning system. , Let be the rotation matrix from the camera coordinate system to the robot base coordinate system. Let be the translation vector from the camera coordinate system to the robot's base coordinate system. The three-dimensional coordinates of the vehicle body positioning holes in the camera coordinate system. These are the coordinates of the key points of the component to be assembled in the robot's base coordinate system, obtained from the laser tracker. , Let be the rotation matrix from the laser tracker coordinate system to the robot's base coordinate system. Let be the translation vector from the laser tracker coordinate system to the robot's base coordinate system. Here are the coordinates of the key points of the component to be assembled in the coordinate system of the laser tracker. For the weights of the visual positioning system, , The information entropy of the visual positioning system. The information entropy of the laser tracker, As the weight of the laser tracker, .

[0008] In one possible implementation, generating dynamic compensation amounts for each axis based on position information and a preset assembly path, combined with motion data for each axis, includes: defining position error as the deviation between the actual position of the part to be assembled and the corresponding position on the preset assembly path in the robot's base coordinate system at the current moment; the expression for the dynamic compensation amount is: ;in, For the first Dynamic compensation amount of each axis This is the gain coefficient for the position error. The gain coefficient for speed. The gain coefficient for acceleration. Position error on the first The weight of the dynamic compensation amount of each axis. For speed to the first The weight of the dynamic compensation amount of each axis. For acceleration on the first The weight of the dynamic compensation amount of each axis. This is the position error vector. For the first Velocity vectors along each axis For the first The acceleration vector along each axis; the expression for the position error vector is: ;in, For the preset assembly path at time Location information; position error affects the first The expression for the weight of the dynamic compensation amount of each axis is: ;in, For the first The magnitude of the velocity vector along each axis For the first The magnitude of the acceleration vector along the i-th axis; velocity relative to the i-th axis. The expression for the weight of the dynamic compensation amount of each axis is: ; acceleration on the first The expression for the weight of the dynamic compensation amount of each axis is: .

[0009] In one possible implementation, the multi-axis cooperative control strategy, which distributes dynamic compensation amounts to each axis and controls the cooperative motion of each axis of the assembly robot to achieve dynamic balanced assembly, includes: establishing a communication connection between multiple robots using a master-slave communication protocol to determine the master robot and slave robots; the master robot generating motion trajectories and cooperative control commands for each axis based on the dynamic compensation amounts of each axis and the preset assembly task; the master robot sending the cooperative control commands to the slave robots through the communication protocol; and the slave robots, upon receiving the cooperative control commands, adjusting the speed and acceleration of each axis in conjunction with their own dynamic compensation amounts to achieve synchronized motion with the master robot.

[0010] In one possible implementation, the formula for generating the motion trajectory of each axis is: ;in, For the motion trajectory of each axis, The ideal motion trajectory corresponding to the preset assembly path. Let be the compensation gain matrix for the motion trajectory. , For the first The compensation gain coefficient for each axis, Here is a function used to generate the diagonal elements of a diagonal matrix; the expression for the adjusted velocities from each axis of the robot is: ;in, To the robot The motion speed after each axis adjustment The speed involved in the collaborative control commands sent by the master robot to the slave robot. The velocity compensation gain matrix is... For the robot's first The dynamic compensation amount for each axis; the expression for the acceleration after adjustment of each axis of the robot is: ;in, To the robot Acceleration after axis adjustment acceleration involved in sending the master robot to the slave robot for collaborative control instructions, a compensation gain matrix for acceleration, a dynamic compensation amount for the first axis of the slave robot.

[0011] In a possible implementation, the method further includes: comparing the collected motion data of each axis of the assembly robot and the positioning data of the component to be assembled with a preset safety threshold; if any data exceeds the safety threshold, triggering an emergency stop signal to stop all motion of the assembly robot; recording the abnormal data and the time of occurrence, and generating a fault report.

[0012] In a second aspect, the embodiments of the present application provide a dynamic balance control device for an assembly robot based on multi-axis collaboration, which comprises: an acquisition module configured to acquire motion data of each axis of the assembly robot and positioning data of a component to be assembled in a new energy vehicle assembly process; a determination module configured to determine position information of the component to be assembled based on the acquired positioning data by using a multi-sensor fusion positioning technology; a generation module configured to generate a dynamic compensation amount for each axis according to the position information and a preset assembly path in combination with the motion data of each axis; and a control module configured to distribute the dynamic compensation amount to each axis by using a multi-axis collaborative control strategy, control collaborative motion of each axis of the assembly robot, and realize dynamic balance assembly.

[0013] In a third aspect, the embodiments of the present application provide a dynamic balance control server for an assembly robot based on multi-axis collaboration, comprising a memory and a processor; the memory is configured to store computer executable instructions; and the processor is configured to execute the computer executable instructions to implement the method of the first aspect or any possible implementation manner of the first aspect.

[0014] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, which stores executable instructions, and a computer executes the executable instructions to implement the method of the first aspect or any possible implementation manner of the first aspect.

[0015] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects:

[0016] ​The embodiment of the application provides a kind of based on multi-axis cooperation's dynamic balance control method of combined robot, through the motion data of each axis of combined robot in the assembly process of new energy automobile and the positioning data of component to be assembled are collected, based on the positioning data collected, the position information of component to be assembled is determined using multi-sensor fusion positioning technology, according to position information and preset assembly path, the motion data of each axis is combined, the dynamic compensation of each axis is generated, and the dynamic compensation is reasonably distributed to each axis using multi-axis cooperation control strategy, ensure that each axis of combined robot is consistent, stable operation in the assembly process.Solve the dynamic balance assembly of combined robot in prior art, affect assembly quality and efficiency, and will cause the problems of inaccurate component installation, connection is not firm. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the description of the embodiments of the present application or the prior art will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings without creative labor on the basis of these drawings.

[0018] Figure 1 A flow chart of a dynamic balance control method of combined robot based on multi-axis cooperation provided by the embodiment of the present application is provided.

[0019] Figure 2 A schematic diagram of a dynamic balance control device of combined robot based on multi-axis cooperation provided by the embodiment of the present application is provided.

[0020] Figure 3 A schematic diagram of a dynamic balance control server of combined robot based on multi-axis cooperation provided by the embodiment of the present application is provided. DETAILED DESCRIPTION

[0021] The technical solutions of the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0022] Some technologies related to the embodiments of the present application are described below to help understanding, which should be considered only as exemplary. Therefore, those skilled in the art should realize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, in order to be clear and concise, the description of some well-known functions and structures is omitted in the following description.

[0023] The embodiment of the present application provides a dynamic balance control method of a joint assembling robot based on multi-axis cooperation, as shown in the figure, which comprises steps S101 to S104. Figure 1 The embodiment of the present application provides a dynamic balance control method of a joint assembling robot based on multi-axis cooperation, as shown in the figure, which comprises steps S101 to S104. Figure 1 The embodiment of the present application provides a dynamic balance control method of a joint assembling robot based on multi-axis cooperation, as shown in the figure, which comprises steps S101 to S104. Figure 1 The embodiment of the present application provides a dynamic balance control method of a joint assembling robot based on multi-axis cooperation, as shown in the figure, which comprises steps S101 to S104.

[0024] S101: Collecting motion data of each axis of a joint assembling robot in a new energy automobile assembling process and positioning data of a component to be assembled.

[0025] Specifically, the component to be assembled in the present application can be a battery.

[0026] Collecting the motion data of each axis of the joint assembling robot in the new energy automobile assembling process and the positioning data of the component to be assembled comprises the following contents.

[0027] The motion position, speed and acceleration data of each axis are collected in real time by the encoder arranged on each axis of the joint assembling robot.

[0028] The positioning data of the component to be assembled is collected by using a visual positioning system and a laser tracker, wherein the visual positioning system adopts a binocular stereo camera to identify a body positioning hole, and the laser tracker compensates for the deformation error of the body caused by hoisting in real time.

[0029] Specifically, the binocular stereo camera simulates the visual principle of human eyes, and simultaneously shoots the images of the component to be assembled through two cameras with different visual angles. The image processing algorithm is used to match and analyze the two images to obtain the three-dimensional coordinates of the body positioning hole in the camera coordinate system. The image processing algorithm can be SIFT (Scale Invariant Feature Transform) algorithm. The laser tracker can measure the coordinates of the key points of the component to be assembled in the laser tracker coordinate system by emitting a laser beam and tracking a reflecting target. The key points of the component to be assembled can be bolt holes or body positioning holes at the connection of body plates.

[0030] S102: Based on the collected positioning data, the position information of the component to be assembled is determined by using a multi-sensor fusion positioning technology.

[0031] Based on the collected positioning data, the position information of the component to be assembled is determined by using a multi-sensor fusion positioning technology, comprising the following contents.

[0032] The expression for determining the position information of the component to be assembled is: Wherein, is the position information of the component to be assembled, coordinates of the body positioning hole in the robot base coordinate system obtained by the visual positioning system, , rotation matrix of the camera coordinate system to the robot base coordinate system, translation vector of the camera coordinate system to the robot base coordinate system, three-dimensional coordinates of the body positioning hole in the camera coordinate system, coordinates of the key points of the component to be assembled in the robot base coordinate system measured by the laser tracker, , rotation matrix of the laser tracker coordinate system to the robot base coordinate system, translation vector of the laser tracker coordinate system to the robot base coordinate system, coordinates of the key points of the component to be assembled in the laser tracker coordinate system, weight of the visual positioning system, , information entropy of the visual positioning system, information entropy of the laser tracker, weight of the laser tracker, .

[0033] Specifically, the application fuses the measurement data of the visual positioning system and the laser tracker according to the weight, which can give full play to their respective advantages, make up for the shortcomings of a single sensor, and thus improve the measurement accuracy of the position information of the component to be assembled.

[0034] Further, the rotation matrix and the translation vector can be obtained by the coordinate system calibration method.

[0035] Specifically, the three-dimensional coordinates of the body positioning hole in the camera coordinate system are . The rotation matrix of the camera coordinate system to the robot base coordinate system is matrix, and the translation vector of the camera coordinate system to the robot base coordinate system is . The coordinates of the body positioning hole in the robot base coordinate system obtained by the visual positioning system are . The coordinates of the key points of the component to be assembled in the robot base coordinate system measured by the laser tracker are . The information entropy of the visual positioning system can be calculated by measuring the position of the same target n times to obtain measurement values , calculating the standard deviation of the measurement values of the same target position measured by the visual positioning system n times , and the information entropy of the visual positioning system which can be expressed as a function related to standard deviation: , is a first proportional coefficient. Similarly, the information entropy of the laser tracker is: , is the standard deviation of the measurement values of the laser tracker for measuring the same target position multiple times, is a second proportional coefficient. It should be noted that the same target for measurement can be a body positioning hole. The specific value of the first proportional coefficient can be determined according to the actual measurement data and accuracy evaluation of the visual positioning system, and the specific value of the second proportional coefficient can be determined according to the actual performance and measurement data of the laser tracker. The standard deviation reflects the dispersion degree of the measurement value, and the greater the dispersion degree, the greater the uncertainty of the measurement result, and the greater the information entropy. The proportional coefficient plays a role in converting the statistical quantity of the standard deviation into the information measurement unit of the information entropy. Different visual positioning systems or different laser trackers have different distribution and characteristics of measurement errors, so it is necessary to determine the appropriate proportional coefficient value through actual measurement and calibration to accurately reflect the information entropy.

[0036] S103: According to the position information and the preset assembly path, the dynamic compensation amount of each axis is generated in combination with the motion data of each axis.

[0037] According to the position information and the preset assembly path, the dynamic compensation amount of each axis is generated in combination with the motion data of each axis, including the following contents.

[0038] The position error is defined as the deviation between the actual position of the component to be assembled and the corresponding position on the preset assembly path in the robot base coordinate system at the current time.

[0039] For example, in the operation of assembling a battery to the bottom of the vehicle body, if the preset assembly path specifies that the battery should reach position A at a certain time, but the actual measurement finds that the battery reaches position B, the coordinate difference between position A and position B in the robot base coordinate system constitutes the position error.

[0040] The expression of the dynamic compensation amount is: . Wherein, is the dynamic compensation amount of the first axis, is the gain coefficient of the position error, is the gain coefficient of the speed, is the gain coefficient of the acceleration, is the weight of the position error on the dynamic compensation amount of the first axis, is the weight of the speed on the dynamic compensation amount of the first axis, is the weight of the acceleration on the dynamic compensation amount of the first axis, This is the position error vector. For the first Velocity vectors along each axis For the first The acceleration vector along each axis.

[0041] Specifically, As a three-dimensional vector, because robot motion involves three spatial dimensions (such as x, y, and z directions), dynamic compensation quantities need to adjust the axis motion in these three dimensions to ensure the accurate position of the part to be assembled in space. For example, for an axis responsible for horizontal movement, dynamic compensation quantities might adjust its movement distance in the x and y directions. For an axis responsible for vertical movement, its position in the z direction is mainly adjusted.

[0042] Furthermore, the gain coefficient of the position error The value of is between 0.1 and 10. Smaller values ​​are suitable for situations where positional accuracy requirements are not high or where the system has significant inertia and a slow response; larger values ​​are suitable for scenarios where positional accuracy requirements are extremely high and the system has good response capabilities. (Velocity gain coefficient) The value of is between 0.01 and 1. A smaller value is used when the robot's movement speed is slow or the speed change has little impact on assembly accuracy; a larger value is used when the movement speed is fast and the speed factor has a significant impact on the assembly position. (Acceleration gain coefficient) The value is between 0.001 and 0.1. When the robot acceleration is small or the acceleration change has little impact on the assembly, the smaller value is taken; if the acceleration is large and has a significant impact on the assembly accuracy, the larger value is taken.

[0043] The expression for the position error vector is: .in, For the preset assembly path at time Location information, This provides the location information of the components to be assembled.

[0044] Position error on the first The expression for the weight of the dynamic compensation amount of each axis is: .in, For the first The magnitude of the velocity vector along each axis For the first The magnitude of the acceleration vector along each axis.

[0045] When the speed and acceleration are large As the value decreases, the impact of position error is relatively weakened.

[0046] Speed ​​relative to the first The expression for the weight of the dynamic compensation amount of each axis is: .

[0047] When the shaft speed is high As the value of increases, the impact of speed on the dynamic compensation becomes more significant.

[0048] Acceleration on the first The expression for the weight of the dynamic compensation amount of each axis is: .

[0049] When the acceleration of the shaft is large, As the value of increases, the influence of acceleration on the dynamic compensation becomes more prominent.

[0050] S104: By using a multi-axis cooperative control strategy, dynamic compensation is distributed to each axis to control the cooperative motion of each axis of the assembly robot and achieve dynamic balanced assembly.

[0051] By utilizing a multi-axis collaborative control strategy, dynamic compensation is distributed to each axis to control the coordinated movement of each axis of the assembly robot, thereby achieving dynamic balanced assembly. This includes the following:

[0052] A master-slave communication protocol is used to establish communication connections between multiple robots, and the master robot and slave robots are identified.

[0053] Specifically, the master-slave communication protocol ensures stable and efficient information exchange between the master and slave robots, providing a communication foundation for collaborative work. By establishing the master-slave relationship, tasks and instructions can be rationally allocated, ensuring the orderly progress of the entire assembly process.

[0054] The main robot generates motion trajectories and collaborative control commands for each axis based on the dynamic compensation amount of each axis and the preset assembly tasks.

[0055] The master robot sends collaborative control commands to the slave robot via a communication protocol.

[0056] After receiving the collaborative control command, the robot adjusts the speed and acceleration of each axis by combining the dynamic compensation of each axis to achieve synchronous movement with the main robot.

[0057] The formulas for generating the motion trajectory of each axis are: .in, For the motion trajectory of each axis, The ideal motion trajectory corresponding to the preset assembly path. Let be the compensation gain matrix for the motion trajectory. , For the first The compensation gain coefficient for each axis This is a function used to generate the diagonal elements of a diagonal matrix.

[0058] Specifically, based on assembly process requirements, the ideal motion trajectories of the two axes are pre-planned. For example, the ideal motion trajectory of axis 1 is to move from position x=0 to x=100 (unit: millimeters) in the x-direction, while the y and z directions remain unchanged. Assume the dynamic compensation amount of axis 1... The value (2,0,0) corresponds to the deviation in the x, y, and z directions in three-dimensional space. The dynamic compensation amount for axis 2 is determined through debugging and experience. Given (0,1,0), the compensation gain coefficient for axis 1 is determined to be 0.8, and the compensation gain coefficient for axis 2 is determined to be 0.6. The compensation gain matrix of the motion trajectory is... for For axis 1, The result is (0.8×2, 0.6×0, 0) = (1.6, 0, 0). For axis 2, The result is (0.8×0, 0.6×1, 0) = (0, 0.6, 0). That is, the target position of the trajectory of axis 1 in the x-direction becomes 100 + 1.6 = 101.6 mm. The target position of the trajectory of axis 2 in the y-direction becomes 50 + 0.6 = 50.6 mm.

[0059] The expression for the adjusted speed of each axis of the robot is: .in, To the robot The motion speed after each axis adjustment The speed involved in the collaborative control commands sent by the master robot to the slave robot. The velocity compensation gain matrix is... For the robot's first Dynamic compensation amount for each axis.

[0060] The expression for the acceleration after adjustment of each axis of the robot is: .in, To the robot Acceleration after axis adjustment The acceleration involved in the collaborative control commands sent from the master robot to the slave robot. The compensation gain matrix for acceleration. For the robot's first Dynamic compensation amount for each axis.

[0061] Specifically, through the above speed and acceleration adjustment process, the robot can make real-time adjustments based on its own dynamic compensation amount and the main robot's collaborative control commands, ensuring coordinated movement of each axis and achieving high-precision battery assembly tasks.

[0062] This application also includes the following.

[0063] The collected motion data of each axis of the assembly robot and the positioning data of the component to be assembled are compared with preset safety thresholds.

[0064] Specifically, the safety thresholds are preset according to performance parameters of the robot, assembly process requirements, safety standards and other factors. For example, for the speed of the robot axis, the safety threshold can be set to 90% of the maximum allowable speed of the axis. For the position deviation of the component to be assembled, the safety threshold can be set to ±1mm.

[0065] If any data exceeds the safety threshold, an emergency stop signal is triggered to stop all movements of the assembly robot.

[0066] The abnormal data and the occurrence time are recorded, and a fault report is generated.

[0067] The embodiment of the application also provides a dynamic balance control device 200 for an assembly robot based on multi-axis cooperation, as shown in the figure, the device comprises an acquisition module 201, a determination module 202, a generation module 203 and a control module 204. Figure 2

[0068] The acquisition module 201 is used to acquire motion data of each axis of the assembly robot and positioning data of the component to be assembled in the assembly process of the new energy vehicle.

[0069] The determination module 202 is used to determine the position information of the component to be assembled based on the acquired positioning data by using a multi-sensor fusion positioning technology.

[0070] The generation module 203 is used to generate dynamic compensation amounts of each axis according to the position information and a preset assembly path in combination with the motion data of each axis.

[0071] The control module 204 is used to distribute the dynamic compensation amounts to each axis by using a multi-axis cooperation control strategy, control the cooperative movement of each axis of the assembly robot, and realize dynamic balance assembly.

[0072] Some of the modules in the device described in the application can be described in the general context of computer-executable instructions, such as program modules, which are executed by computers. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. The application can also be practiced in a distributed computing environment in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0073] ​The apparatuses or modules illustrated in the above application examples can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above apparatuses are described as various modules with functions. In the implementation of the application examples, the functions of the modules can be implemented in one or more software and / or hardware. Of course, the modules with certain functions can also be implemented by a combination of multiple sub-modules or sub-units.

[0074] The methods, apparatuses or modules described in the present application can be implemented in a computer readable program code in any appropriate manner, for example, the controller can take the form of, for example, a microprocessor or a processor and a computer readable medium storing computer readable program code (for example, software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers and embedded microcontrollers. Examples of the controller include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a pure computer readable program code, the same function can also be implemented by logically programming the method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers. Therefore, such a controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the devices for implementing various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0075] As shown in Figure 3 The application examples also provide a multi-axis cooperative based assembly robot dynamic balance control server, including a memory 301 and a processor 302; the memory 301 is used for storing computer executable instructions; the processor 302 is used for executing the computer executable instructions to implement the multi-axis cooperative based assembly robot dynamic balance control method provided in the above application examples.

[0076] The application examples also provide a computer readable storage medium, which stores executable instructions, and a computer executes the executable instructions to implement the multi-axis cooperative based assembly robot dynamic balance control method provided in the above application examples.

[0077] From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary hardware. Based on such an understanding, the technical solutions of the present application can be embodied in the form of a software product or can be embodied in the implementation process of data migration. The computer software product can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in the embodiments of the present application.

[0078] The various embodiments in the specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment mainly describes the difference from other embodiments. The whole or part of the present application can be used in a plurality of general or special computer system environments or configurations.

[0079] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the present application.

Claims

1. A multi-axis coordination-based dynamic balancing control method for a hybrid robot, characterized by, The method comprises the following steps: Collecting the motion data of each axis of the assembly robot and the positioning data of the component to be assembled in the assembly process of the new energy vehicle; Based on the collected positioning data, the position information of the component to be assembled is determined by using a multi-sensor fusion positioning technology; According to the position information and the preset assembly path, the dynamic compensation amount of each axis is generated in combination with the motion data of each axis; By using a multi-axis cooperative control strategy, the dynamic compensation amount is distributed to each axis to control the cooperative motion of each axis of the assembly robot, and dynamic balance assembly is realized; Said based on the collected positioning data, the position information of the component to be assembled is determined by using a multi-sensor fusion positioning technology, comprising: The expression for determining the position information of the component to be assembled is: ; wherein, is the position information of the component to be assembled, is the coordinate of the body positioning hole in the robot base coordinate system obtained by the visual positioning system, , is the rotation matrix of the camera coordinate system to the robot base coordinate system, is the translation vector of the camera coordinate system to the robot base coordinate system, is the three-dimensional coordinate of the body positioning hole in the camera coordinate system, is the coordinate of the key point of the component to be assembled measured by the laser tracker in the robot base coordinate system, , is the rotation matrix of the laser tracker coordinate system to the robot base coordinate system, is the translation vector of the laser tracker coordinate system to the robot base coordinate system, is the coordinate of the key point of the component to be assembled in the laser tracker coordinate system, is the weight of the visual positioning system, , is the information entropy of the visual positioning system, is the information entropy of the laser tracker, is the weight of the laser tracker, .

2. The multi-axis coordination based hybrid robot dynamic balancing control method according to claim 1, wherein, Said collecting the motion data of each axis of the assembly robot and the positioning data of the component to be assembled in the assembly process of the new energy vehicle, comprising: The motion position, speed and acceleration data of each axis are collected in real time by the encoder arranged on each axis of the assembly robot; The positioning data of the component to be assembled is collected by using a visual positioning system and a laser tracker, wherein the visual positioning system adopts a binocular stereo camera to identify the body positioning hole, and the laser tracker compensates the deformation error of the body caused by hoisting in real time.

3. The multi-axis coordination based hybrid robot dynamic balancing control method according to claim 2, wherein, Said according to the position information and the preset assembly path, the dynamic compensation amount of each axis is generated in combination with the motion data of each axis, comprising: The position error is defined as the deviation between the actual position of the component to be assembled and the corresponding position on the preset assembly path in the robot base coordinate system at the current time; The expression of the dynamic compensation amount is: ; wherein, is the dynamic compensation amount of the th axis, is a gain coefficient of the position error, is a gain coefficient of the velocity, is a gain coefficient of the acceleration, is a weight of the position error to the dynamic compensation amount of the th axis, is a weight of the velocity to the dynamic compensation amount of the th axis, is a weight of the acceleration to the dynamic compensation amount of the th axis, is a position error vector, is a velocity vector of the th axis, is an acceleration vector of the th axis. The expression of the position error vector is: ; wherein, is the position information at time on the preset assembly path. Position error for the first The expression for the weight of the dynamic compensation amount of each axis is: ;in, For the first The magnitude of the velocity vector along each axis For the first The magnitude of the acceleration vector along each axis; The expression of the weight of the speed to the dynamic compensation amount of the first axis is: ; and the expression of the weight of the speed to the dynamic compensation amount of the second axis is: ; The expression of the weight of the acceleration to the dynamic compensation amount of the first axis is: .​ 4. The multi-axis coordination based hybrid robot dynamic balancing control method according to claim 3, characterized in that, Said by using a multi-axis cooperative control strategy, the dynamic compensation amount is distributed to each axis to control the cooperative motion of each axis of the assembly robot, and dynamic balance assembly is realized, comprising: A master-slave communication protocol is adopted to establish a communication connection between the multiple robots, and the master robot and the slave robot are determined; The master robot generates the motion trajectory and the cooperative control instruction of each axis according to the dynamic compensation amount of each axis and the preset assembly task; The master robot sends the cooperative control instruction to the slave robot through the communication protocol; After receiving the cooperative control instruction, the slave robot adjusts the speed and acceleration of each axis in combination with the dynamic compensation amount of each axis to realize the synchronous motion with the master robot.

5. The multi-axis coordination based hybrid robot dynamic balancing control method according to claim 4, wherein, The generation formula of the motion trajectory of each axis is: ; wherein, is the motion trajectory of each axis, is the ideal motion trajectory corresponding to the preset assembly path, is the compensation gain matrix of the motion trajectory, , is the compensation gain coefficient of the first axis, is a function for generating diagonal elements of a diagonal matrix; The expression of the adjusted speed of each axis of the slave robot is: wherein, is the adjusted motion speed of the first axis of the slave robot, is the adjusted motion speed of the second axis of the slave robot, is the speed involved in the cooperative control instruction sent by the master robot to the slave robot, is the compensation gain matrix of the speed, is the dynamic compensation amount of the first axis of the slave robot, is the dynamic compensation amount of the second axis of the slave robot. The expression of the adjusted acceleration of each axis of the slave robot is: ; wherein, is the adjusted acceleration of the first axis of the slave robot, is the adjusted acceleration of the second axis of the slave robot, is the acceleration involved in the cooperative control instruction sent by the master robot to the slave robot, is the compensation gain matrix of the acceleration, is the dynamic compensation amount of the first axis of the slave robot, is the dynamic compensation amount of the second axis of the slave robot.

6. The multi-axis coordination based hybrid robot dynamic balancing control method according to claim 5, wherein, Further comprising: Comparing the collected motion data of each axis of the assembly robot and the positioning data of the component to be assembled with the preset safety threshold; If any data exceeds the safety threshold, an emergency stop signal is triggered to stop all movements of the assembly robot; The abnormal data and the occurrence time are recorded to generate a fault report.

7. A dynamic balance control device for an assembly robot based on multi-axis cooperation, which executes the method according to any one of claims 1 to 6, comprising: a collection module for collecting the motion data of each axis of the assembly robot and the positioning data of the component to be assembled in the assembly process of the new energy vehicle; a determination module for determining the position information of the component to be assembled based on the collected positioning data by using a multi-sensor fusion positioning technology; a generation module for generating the dynamic compensation amount of each axis in combination with the motion data of each axis according to the position information and the preset assembly path; a control module for distributing the dynamic compensation amount to each axis by using a multi-axis cooperative control strategy to control the cooperative motion of each axis of the assembly robot and realize dynamic balance assembly.

8. A multi-axis coordination-based assembly robot dynamic balance control server, characterized by, comprising a memory and a processor; The memory is configured to store computer executable instructions; The processor is configured to execute the computer executable instructions to implement the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores executable instructions, and the computer executes the executable instructions to implement the method of any one of claims 1-6.

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