Intelligent mechanical arm exercise method and system

Abstract

The invention relates to the technical field of mechanical arm training, in particular to an intelligent mechanical arm training method and system which comprises a mechanical arm body, a data acquisition module, an operation processing module and a feedback control module. The system monitors the motion precision of the mechanical arm through the light beam sensor, calculates the completion time based on the time nodes of leaving and entering the induction interval, and compares the completion time with the reference time to evaluate the precision. And if the precision is insufficient, the prompt module outputs feedback information and dynamically adjusts training parameters at the same time. The motion precision of the mechanical arm can be monitored in real time, the autonomous learning ability and the training efficiency are improved, and an efficient solution is provided for intelligent training in a complex scene.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent robots and automation control, and specifically relates to an intelligent robotic arm practice method and system. Background Art

[0002] Long-term operation can cause wear and tear on robotic arms. For example, the movement of the robotic arm may deviate due to factors such as mechanical wear or material aging. For some processes that require very precise robotic arm operations, these deviations often result in abnormalities during the process.

[0003] A robot multi-module combined simulation training teaching device with the publication number CN113920805B. This patent sets a fixed plate and a base, and combines practice balls of different weights and materials to train the precise grasping ability of the robotic arm for workpieces of different materials. At the same time, through the design of a lateral pull handle, the synchronous operation of the longitudinal robotic arm and the operator is achieved, enabling the robotic arm to imitate the movement trajectory of the operator. However, in this technical solution, the training mode of the robotic arm mainly relies on the physical guidance of the operator, lacking intelligent autonomous learning and adaptation capabilities. In addition, the feedback mechanism during the training process is relatively single, and the ability to monitor and optimize the movement accuracy and efficiency of the robotic arm in real time needs to be improved, which may affect the further improvement of the training effect.

[0004] An auxiliary training device using VR and force feedback robotic arms with the publication number CN113181621B. This patent combines a VR device and a service terminal to achieve the linkage between the movement of the robotic arm and virtual training tools, and strengthens the attention practice of the exerciser through force feedback technology. However, in this technical solution, the training content of the robotic arm mainly focuses on specific scenarios (such as athlete training), and the applicable range is relatively limited. In addition, the accuracy and response speed of its force feedback mechanism may be affected by the hardware performance, and there are certain challenges in meeting the high-precision training requirements. At the same time, the system has insufficient ability for in-depth analysis and intelligent processing of training data, and there is still room for improvement in supporting personalized training programs.

[0005] The above problems indicate that the existing robotic arm practice methods and systems still have certain deficiencies in terms of intelligence level, diversity of training modes, accuracy of feedback mechanisms, and support for personalized training. Therefore, the present invention provides an intelligent robotic arm practice method and system, aiming to improve the autonomous learning ability, training efficiency, and applicable range of the robotic arm by introducing artificial intelligence algorithms, multi-modal interaction technologies, and big data analysis, so as to meet the needs of efficient and intelligent robotic arm training in complex scenarios. Summary of the Invention

[0006] The present invention provides an intelligent robotic arm practice system and an intelligent robotic arm practice method. By combining multimodal interaction technology and an adaptive learning algorithm, during the operation of the robotic arm, its motion accuracy is monitored in real time, and the training parameters of the robotic arm are dynamically adjusted according to the monitoring results, thereby improving the autonomous learning ability and training efficiency of the robotic arm.

[0007] The intelligent robotic arm practice system of the present invention includes a robotic arm main body, a data acquisition module, an arithmetic processing module, and a feedback control module. The robotic arm main body performs a first trajectory motion within a preset space, and the first trajectory motion corresponds to a planar path in a three-dimensional coordinate system. The data acquisition module is arranged within the operating range of the robotic arm main body and is used to detect whether the robotic arm main body is located within a predetermined sensing interval. The arithmetic processing module is connected to the robotic arm main body and the data acquisition module, and the arithmetic processing module is configured to: during the execution of the first trajectory motion by the robotic arm main body, calculate the first completion time of the first trajectory motion based on the time nodes when the robotic arm main body leaves the sensing interval and re-enters the sensing interval; compare the first completion time with a reference time to evaluate whether the motion accuracy of the robotic arm main body meets the requirements.

[0008] Preferably, the above-mentioned intelligent robotic arm practice system further includes a storage module. The storage module is connected to the arithmetic processing module, and the reference time is pre-recorded and stored in the storage module.

[0009] Preferably, after the robotic arm main body completes the first trajectory motion, the robotic arm main body moves vertically to another height position and then performs a second trajectory motion, and the second trajectory motion corresponds to a planar path in another three-dimensional coordinate system. The arithmetic processing module is further configured to: during the execution of the second trajectory motion by the robotic arm main body, calculate the second completion time of the second trajectory motion based on the time nodes when the robotic arm main body leaves the sensing interval and re-enters the sensing interval; compare the second completion time with another reference time to evaluate whether the motion accuracy of the robotic arm main body meets the requirements.

[0010] Preferably, the above-mentioned arithmetic processing module is further connected to a prompt module. When the arithmetic processing module evaluates that the motion accuracy of the robotic arm main body does not meet the requirements, the arithmetic processing module outputs a corresponding prompt message through the prompt module.

[0011] Preferably, the above-mentioned data acquisition module uses a beam sensor, and the beam sensor is set to emit a detection beam signal to the sensing interval.

[0012] The intelligent robotic arm practice method of the present invention is applicable to the robotic arm main body and includes the following steps: The robotic arm main body performs a first trajectory motion corresponding to a first plane in a three-dimensional coordinate system; The data acquisition module detects whether the robotic arm main body is located within a predetermined sensing interval; During the execution of the first trajectory motion by the robotic arm main body, calculate the first completion time of the first trajectory motion based on the time nodes when the robotic arm main body leaves the sensing interval and re-enters the sensing interval; Compare the first completion time with a reference time to evaluate whether the motion accuracy of the robotic arm main body meets the requirements.

[0013] Preferably, the above intelligent robotic arm practice method further includes the following steps: Pre-record and save the reference time in the storage module.

[0014] Preferably, the above intelligent robotic arm practice method further includes the following steps: After the robotic arm main body completes the first trajectory motion, the robotic arm main body moves vertically to another height position and then performs a second trajectory motion corresponding to a second plane in the three-dimensional coordinate system; During the execution of the second trajectory motion by the robotic arm main body, calculate the second completion time of the second trajectory motion based on the time nodes when the robotic arm main body leaves the sensing interval and re-enters the sensing interval; Compare the second completion time with another reference time to evaluate whether the motion accuracy of the robotic arm main body meets the requirements.

[0015] Preferably, the above intelligent robotic arm practice method further includes the following steps: When it is evaluated that the motion accuracy of the robotic arm main body does not meet the requirements, output a corresponding prompt message through the prompt module.

[0016] Preferably, the above data acquisition module uses a beam sensor, and the beam sensor is set to emit a detection beam signal to the sensing interval.

[0017] Based on the above, the intelligent robotic arm practice system of the present invention can real-time monitor the motion accuracy of the robotic arm main body and dynamically adjust the training parameters according to the monitoring results by introducing multi-modal interaction technology and adaptive learning algorithms, thereby significantly improving the autonomous learning ability, training efficiency, and application scope of the robotic arm main body.

[0018] Specific implementation scheme

[0019] Specific design of the data acquisition module

[0020] The core component of the data acquisition module is the beam sensor, which is fixed within the operating range of the robotic arm main body through a bracket. The bracket adopts an adjustable structure, allowing users to adjust the installation angle and position of the beam sensor according to actual needs. Inside the beam sensor, a light source emitter and a receiver are integrated. The beam signal emitted by the light source emitter covers the entire sensing range. When the robotic arm main body enters or exits the sensing range, the receiver will capture the change in the beam signal and transmit the time point of the signal change to the arithmetic processing module.

[0021] The specific algorithm of the arithmetic processing module

[0022] The arithmetic processing module incorporates an adaptive learning algorithm for analyzing the motion accuracy of the robotic arm main body. The algorithm consists of the following steps:

[0023] Data acquisition and preprocessing: Receive the time node data from the data acquisition module and remove the outliers caused by environmental interference.

[0024] Time difference calculation: Calculate the time difference between the robotic arm main body leaving the sensing range and re-entering the sensing range based on the time node data to obtain the first completion time or the second completion time.

[0025] Accuracy evaluation: Compare the calculated time difference with the corresponding reference time. If the deviation exceeds the preset threshold, it is determined that the motion accuracy of the robotic arm main body does not meet the requirements.

[0026] Dynamic adjustment: Adjust the training parameters of the robotic arm main body according to the evaluation results, such as increasing the training intensity or changing the training trajectory.

[0027] The specific functions of the feedback control module

[0028] The feedback control module provides visual feedback information to the user through the prompt module. The prompt module adopts a design that combines an LED display screen and a voice broadcast device. When the arithmetic processing module evaluates that the motion accuracy of the robotic arm main body does not meet the requirements, the prompt module will display the specific error information and remind the user to take corresponding measures through voice broadcast.

[0029] The specific implementation of the storage module

[0030] The storage module uses a non-volatile storage medium to save the reference time and other key parameters. The storage module is connected to the arithmetic processing module through a high-speed data interface to ensure the efficiency of data reading and writing. In addition, the storage module also supports users to import or export data through external devices, facilitating subsequent analysis and optimization.

[0031] The overall working process of the system

[0032] The robotic arm main body performs the first trajectory movement according to a preset trajectory, and the data acquisition module monitors in real time whether the robotic arm main body is within the sensing range.

[0033] The operation processing module calculates the first completion time based on the time node data provided by the data acquisition module and compares it with the reference time.

[0034] If the deviation between the first completion time and the reference time exceeds the preset threshold, the feedback control module outputs a prompt message through the prompt module.

[0035] After the robotic arm main body completes the first trajectory movement, it moves vertically to another height position and performs the second trajectory movement, repeating the above steps.

[0036] Through the above specific implementation solutions, the intelligent robotic arm training system of the present invention can effectively solve the problems existing in the prior art, such as insufficient intelligence, single training mode, and imperfect feedback mechanism, and provides a new solution for efficient and intelligent robotic arm training in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a schematic diagram of the overall structure of the intelligent robotic arm training system of the present invention;

[0038] Figure 2 is a schematic diagram of the specific structure of the data acquisition module in the present invention;

[0039] Figure 3 is a schematic diagram of the flow of the intelligent robotic arm training method of the present invention;

[0040] Figure 4 is a schematic diagram of the working principle of the feedback control module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0041] The intelligent robotic arm training system of the present invention includes a robotic arm main body, a data acquisition module, an operation processing module, a beam sensor, a sensing range, a prompt module, and a storage module. The following details the specific implementation of the present invention with reference to the accompanying drawings.

[0042] Such as Figure 1As shown, the robotic arm main body is the execution component of the entire system. It is fixed to the working platform through a base and can complete multi-degree-of-freedom movement within a preset space. The data acquisition module is set within the operating range of the robotic arm main body and is used to detect whether the robotic arm main body is within the sensing interval. The core component of the data acquisition module is the beam sensor. The beam sensor is fixed around the operating path of the robotic arm main body through a bracket. The bracket adopts an adjustable structure, allowing users to adjust the installation angle and position of the beam sensor according to actual needs to ensure that the beam signal can cover the entire sensing interval. The beam sensor internally integrates a light source emitter and a receiver. The beam signal emitted by the light source emitter covers the sensing interval. When the robotic arm main body enters or leaves the sensing interval, the receiver will capture the change in the beam signal and transmit the time node of the signal change to the arithmetic processing module.

[0043] The arithmetic processing module is connected to the robotic arm main body and the data acquisition module and is used to receive the time node data from the data acquisition module and perform analysis and processing. The arithmetic processing module has a built-in adaptive learning algorithm, which is divided into multiple steps. First, the arithmetic processing module receives the time node data from the data acquisition module and removes the outliers caused by environmental interference. Subsequently, the arithmetic processing module calculates the time difference between the robotic arm main body leaving the sensing interval and re-entering the sensing interval based on the time node data to obtain the first completion time or the second completion time. Then, the arithmetic processing module compares the calculated time difference with the reference time saved in the storage module. If the deviation exceeds the preset threshold, it is determined that the action accuracy of the robotic arm main body does not meet the requirements. Finally, the arithmetic processing module adjusts the training parameters of the robotic arm main body according to the evaluation results, such as increasing the training intensity or changing the training trajectory.

[0044] The storage module is connected to the arithmetic processing module and is used to save the reference time and other key parameters. The storage module uses a non-volatile storage medium and is connected to the arithmetic processing module through a high-speed data interface to ensure the efficiency of data reading and writing. In addition, the storage module also supports users to import or export data through external devices for subsequent analysis and optimization.

[0045] The prompt module is connected to the arithmetic processing module. When the arithmetic processing module evaluates that the action accuracy of the robotic arm main body does not meet the requirements, the prompt module provides visual feedback information to the user. The prompt module adopts a design that combines an LED display screen and a voice broadcast device. When the arithmetic processing module evaluates that the action accuracy of the robotic arm main body does not meet the requirements, the prompt module will display the specific error information and remind the user to take corresponding measures through voice broadcast.

[0046] As Figure 2As shown, the installation position of the beam sensor and its relationship with the sensing range are further clarified. The beam sensor is fixed around the running path of the robotic arm main body through a bracket. The bracket adopts an adjustable structure, allowing users to adjust the installation angle and position of the beam sensor according to actual needs. Inside the beam sensor, a light source emitter and a receiver are integrated. The beam signal emitted by the light source emitter covers the entire sensing range. When the robotic arm main body enters or leaves the sensing range, the receiver will capture the change in the beam signal and transmit the time node of the signal change to the operation processing module.

[0047] As Figure 3 shown, the intelligent robotic arm practice method of the present invention is applicable to the robotic arm main body and includes the following steps: the robotic arm main body performs a first trajectory movement corresponding to a first plane in a three-dimensional coordinate system; the data acquisition module detects whether the robotic arm main body is located within a predetermined sensing range; during the execution of the first trajectory movement by the robotic arm main body, the first completion time of the first trajectory movement is calculated based on the time nodes when the robotic arm main body leaves and re-enters the sensing range; the first completion time is compared with a reference time to evaluate whether the motion accuracy of the robotic arm main body meets the requirements. When the robotic arm main body completes the first trajectory movement, it moves vertically to another height position and then performs a second trajectory movement corresponding to a second plane in the three-dimensional coordinate system; during the execution of the second trajectory movement by the robotic arm main body, the second completion time of the second trajectory movement is calculated based on the time nodes when the robotic arm main body leaves and re-enters the sensing range; the second completion time is compared with another reference time to evaluate whether the motion accuracy of the robotic arm main body meets the requirements. When it is evaluated that the motion accuracy of the robotic arm main body does not meet the requirements, the corresponding prompt information is output through the prompt module.

[0048] As Figure 4 shown, the working principle of the feedback control module is further clarified. When the operation processing module evaluates that the motion accuracy of the robotic arm main body does not meet the requirements, the prompt module displays specific error information through an LED display screen and plays a voice prompt through a voice broadcast device to remind the user to take corresponding measures.

[0049] The intelligent robotic arm training system of the present invention realizes real-time monitoring and dynamic adjustment of the motion accuracy of the robotic arm main body through the coordinated cooperation of the robotic arm main body, data acquisition module, operation processing module, beam sensor, induction area, prompt module, and storage module. Specifically, the robotic arm main body executes the first trajectory motion according to a preset trajectory, and the data acquisition module monitors in real time whether the robotic arm main body is within the induction area. The operation processing module calculates the first completion time based on the time node data provided by the data acquisition module and compares it with the reference time saved in the storage module. If the deviation between the first completion time and the reference time exceeds a preset threshold, the feedback control module outputs a prompt message through the prompt module. After the robotic arm main body completes the first trajectory motion, it moves vertically to another height position and executes the second trajectory motion, repeating the above steps. Through the above specific implementation solutions, the intelligent robotic arm training system of the present invention can effectively solve the problems of insufficient intelligence, single training mode, and imperfect feedback mechanism existing in the prior art, and provides a new solution for efficient and intelligent robotic arm training in complex scenarios.

[0050] To better enable relevant personnel in the technical field to fully understand and implement the present invention, the following supplements the specific implementation principle of the present invention in combination with a specific application scenario.

[0051] In practical applications, the intelligent robotic arm training system of the present invention can be deployed in the assembly link of an industrial production line to improve the motion accuracy and efficiency of the robotic arm in complex assembly tasks. For example, in the process of electronic component assembly, the robotic arm needs to complete the task of accurately inserting a micro-component into a specified position on a printed circuit board (PCB). This scenario requires the robotic arm to have high-precision motion control capabilities, and the system of the present invention can meet this requirement by real-time monitoring and dynamically adjusting training parameters.

[0052] First, the robotic arm main body is fixed on the working platform of the assembly station, and the core component of the data acquisition module, the beam sensor, is installed around its running path. The beam sensor is adjusted to an appropriate angle and position through a bracket to ensure that the beam signal it emits can cover the entire induction area. The position of the induction area is set according to the actual requirements of the assembly task and usually corresponds to key nodes in the robotic arm motion trajectory, such as component picking points or placement points. When the robotic arm main body executes the first trajectory motion, the receiver of the beam sensor will capture the time nodes when the robotic arm main body enters or leaves the induction area and transmit these time node data to the operation processing module.

[0053] Subsequently, the operation processing module analyzes and processes the received time node data. In the first step, the operation processing module uses a built-in algorithm to remove outliers caused by environmental interference (such as light reflection or vibration) to ensure the accuracy of the data. Then, the operation processing module calculates the time difference between the main body of the robotic arm leaving the sensing interval and re-entering the sensing interval based on the time node data, obtaining the first completion time. This time difference reflects the actual action speed and stability of the main body of the robotic arm when performing the first trajectory movement. The operation processing module compares the calculated first completion time with the reference time saved in the storage module. If the deviation between the first completion time and the reference time exceeds the preset threshold, it is determined that the action accuracy of the main body of the robotic arm does not meet the requirements.

[0054] On this basis, the feedback control module outputs specific error information to the user through the prompt module. The prompt module adopts a design combining an LED display screen and a voice broadcast device. When the action accuracy of the main body of the robotic arm does not meet the requirements, the LED display screen will display the specific error value, and at the same time, the voice broadcast device will play a prompt tone to remind the user to take corresponding measures. For example, the user can adjust the movement parameters of the main body of the robotic arm according to the prompt information, or increase the training intensity to improve its action accuracy.

[0055] After the main body of the robotic arm completes the first trajectory movement, it moves vertically to another height position and starts to execute the second trajectory movement. The second trajectory movement usually corresponds to a planar path in another three-dimensional coordinate system, such as the process of moving from the component picking point to the placement point. During this process, the data acquisition module continues to monitor whether the main body of the robotic arm is within the sensing interval and transmits the time node data to the operation processing module. The operation processing module repeats the above steps, calculates the second completion time and compares it with another reference time to evaluate the action accuracy of the main body of the robotic arm during the second trajectory movement.

[0056] Through the above steps, the intelligent robotic arm training system of the present invention realizes real-time monitoring and dynamic adjustment of the action accuracy of the main body of the robotic arm. For example, in the scenario of electronic component assembly, when the main body of the robotic arm fails to complete the component picking or placement action within the specified time, the system will promptly send a prompt message to help the user quickly locate the problem and optimize the training plan. In addition, the storage module supports the user to import or export data through an external device, facilitating subsequent in-depth analysis and optimization of the training effect of the robotic arm.

[0057] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0058] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent robotic arm training system, characterized in that, Including: A robotic arm body that performs a first trajectory movement corresponding to a first plane in a three-dimensional coordinate system; A data acquisition module arranged within the operating range of the robotic arm body for detecting whether the robotic arm body is within a predetermined induction interval; An operation processing module connected to the robotic arm body and the data acquisition module, and the operation processing module is configured to: During the execution of the first trajectory movement by the robotic arm body, calculate a first completion time of the first trajectory movement based on the time nodes when the robotic arm body leaves and re-enters the induction interval; Compare the first completion time with a reference time to evaluate the motion accuracy of the robotic arm body.

2. The intelligent robotic arm training system according to claim 1, characterized in that It further includes a storage module connected to the operation processing module, and the reference time is pre-recorded and stored in the storage module.

3. The intelligent robotic arm training system according to claim 1, characterized in that, After the robotic arm body completes the first trajectory movement, the robotic arm body moves vertically to another height position and then performs a second trajectory movement corresponding to a second plane in the three-dimensional coordinate system. The operation processing module is further configured to: During the execution of the second trajectory movement by the robotic arm body, calculate a second completion time of the second trajectory movement based on the time nodes when the robotic arm body leaves and re-enters the induction interval; And compare the second completion time with another reference time to evaluate the motion accuracy of the robotic arm body.

4. The intelligent robotic arm training system according to claim 1, wherein, It further includes a prompt module connected to the operation processing module. When the operation processing module evaluates that the motion accuracy of the robotic arm body does not meet the requirements, the operation processing module outputs corresponding prompt information through the prompt module.

5. The intelligent robotic arm training system according to claim 1, characterized in that The data acquisition module uses a beam sensor, and the beam sensor is set to emit a detection beam signal towards the induction interval.

6. An intelligent robotic arm practice method, applicable to a robotic arm main body, characterized in that, Including the following steps: S1. The robotic arm body performs a first trajectory movement corresponding to a first plane in a three-dimensional coordinate system; S2. The data acquisition module detects whether the robotic arm body is within a predetermined induction interval; S3. During the execution of the first trajectory movement by the robotic arm body, calculate a first completion time of the first trajectory movement based on the time nodes when the robotic arm body leaves and re-enters the induction interval; S4. Compare the first completion time with a reference time to evaluate the motion accuracy of the robotic arm body.

7. The intelligent robotic arm practice method according to claim 6, characterized in that, It further includes the following steps: S5. After the robotic arm body completes the first trajectory movement, the robotic arm body moves vertically to another height position and then performs a second trajectory movement corresponding to a second plane in the three-dimensional coordinate system; S6. During the execution of the second trajectory movement by the robotic arm body, calculate a second completion time of the second trajectory movement based on the time nodes when the robotic arm body leaves and re-enters the induction interval; S7. Compare the second completion time with another reference time to evaluate the motion accuracy of the robotic arm body.

8. The intelligent robotic arm practice method according to claim 6, wherein The data acquisition module uses a beam sensor, and the beam sensor is set to emit a beam signal for detection towards the induction interval.

9. The intelligent robotic arm training system according to claim 1, wherein, The prompt module includes an LED display screen and a voice broadcast device.

10. The intelligent robotic arm training system according to claim 1, wherein, The beam sensor is fixed within the operating range of the robotic arm body through a bracket, and the bracket adopts an adjustable structure to adjust the installation angle and position of the beam sensor.

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