Intelligent mechanical arm exercise method and system

Abstract

The application relates to the technical field of mechanical arm training, in particular to an intelligent mechanical arm exercise method and system, which comprises a mechanical arm main body, a data acquisition module, an operation processing module and a feedback control module. The system monitors the action precision of the mechanical arm through a light beam sensor, calculates the completion time based on the time nodes of leaving and entering the sensing interval, and compares the precision with the reference time. If the precision is insufficient, the prompt module outputs feedback information, and simultaneously dynamically adjusts the training parameters. The application can monitor the action precision of the mechanical arm in real time, improve the autonomous learning ability and the training efficiency, and provide an efficient solution for intelligent training in a complex scene.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent robots and automation control, and specifically relates to an intelligent mechanical arm exercise method and system. BACKGROUND

[0002] Long-term operation process will cause the mechanical arm to be worn out. For example, the movement of the mechanical arm may be offset due to mechanism wear or material aging. For some processes that require very precise mechanical arm operation, these offsets often cause abnormalities in the process.

[0003] A robot multi-module combined simulation training teaching device with publication number CN113920805B is disclosed. The device is used to train the precise grabbing ability of the mechanical arm for different material processing pieces by setting a fixed plate and a base and combining with exercise balls of different weights and materials. At the same time, through the design of the lateral handle, the longitudinal mechanical arm and the operator are synchronized to run, so that the mechanical arm can imitate the motion trajectory of the operator. However, in the technical solution, the training mode of the mechanical arm mainly depends on the physical guidance of the operator, and the intelligent autonomous learning and adaptation ability is lacking. In addition, the feedback mechanism in the training process is relatively single, and the ability to monitor and optimize the action precision and efficiency of the mechanical arm in real time needs to be improved, which may affect the further improvement of the training effect.

[0004] A kind of auxiliary training equipment using VR and force feedback mechanical arm with publication number CN113181621B is disclosed. The device combines VR equipment and service terminal to realize the linkage of mechanical arm action and virtual training tool, and strengthens the attention practice of the exerciser through force feedback technology. However, in the technical solution, the training content of the mechanical arm is mainly concentrated in specific scenarios (such as athlete training), and the applicable range is relatively limited. In addition, the accuracy and response speed of the force feedback mechanism may be affected by the hardware performance, and there are certain challenges in meeting the high-precision training needs. At the same time, the system lacks deep analysis and intelligent processing ability of training data, and there is still room for improvement in supporting personalized training programs.

[0005] The above problems show that the existing mechanical arm exercise method and system still have certain deficiencies in the degree of intelligence, the diversity of training mode, the accuracy of feedback mechanism and the support of personalized training. Therefore, the present application provides an intelligent mechanical arm exercise method and system, which aims to improve the autonomous learning ability, training efficiency and applicable range of the mechanical arm by introducing artificial intelligence algorithm, multi-modal interaction technology and big data analysis, so as to meet the needs of efficient and intelligent mechanical arm training in complex scenarios. SUMMARY

[0006] The application provides an intelligent mechanical arm exercise system and an intelligent mechanical arm exercise method, which combine multi-modal interaction technology and adaptive learning algorithms to monitor the action accuracy of the mechanical arm in real time during operation of the mechanical arm, and dynamically adjust the training parameters of the mechanical arm according to the monitoring results, thereby improving the autonomous learning ability and training efficiency of the mechanical arm.

[0007] The intelligent mechanical arm exercise system of the application comprises a mechanical arm body, a data acquisition module, an operation processing module and a feedback control module. The mechanical arm body performs a first trajectory motion in 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 in the operation range of the mechanical arm body and is used to detect whether the mechanical arm body is located in a predetermined sensing interval. The operation processing module is connected with the mechanical arm body and the data acquisition module, and the operation processing module is configured to: during the execution of the first trajectory motion by the mechanical arm body, calculate a first completion time of the first trajectory motion based on the time nodes at which the mechanical arm body leaves the sensing interval and reenters the sensing interval; and compare the first completion time with a reference time to evaluate whether the action accuracy of the mechanical arm body meets the requirements.

[0008] Preferably, the intelligent mechanical arm exercise system further comprises a storage module. The storage module is connected with the operation processing module, and the reference time is recorded and saved in the storage module in advance.

[0009] Preferably, after the mechanical arm body completes the first trajectory motion, the mechanical arm body moves to another height position along the vertical direction and then performs a second trajectory motion, and the second trajectory motion corresponds to a planar path in another three-dimensional coordinate system. The operation processing module is further configured to: during the execution of the second trajectory motion by the mechanical arm body, calculate a second completion time of the second trajectory motion based on the time nodes at which the mechanical arm body leaves the sensing interval and reenters the sensing interval; and compare the second completion time with another reference time to evaluate whether the action accuracy of the mechanical arm body meets the requirements.

[0010] Preferably, the operation processing module is further connected to a prompt module, and when the operation processing module evaluates that the action accuracy of the mechanical arm body does not meet the requirements, the operation processing module outputs corresponding prompt information through the prompt module.

[0011] Preferably, the data acquisition module adopts a light beam sensor, and the light beam sensor is arranged to emit a detection light beam signal to the sensing interval.

[0012] The intelligent mechanical arm exercise method of the present application is applicable to a mechanical arm body and comprises the following steps: a first trajectory movement corresponding to a first plane in a three-dimensional coordinate system is performed by the mechanical arm body; whether the mechanical arm body is located within a predetermined sensing interval is detected by a data acquisition module; during the execution of the first trajectory movement by the mechanical arm body, a first completion time of the first trajectory movement is calculated based on the time nodes at which the mechanical arm body leaves the sensing interval and re-enters the sensing interval; and the first completion time is compared with a reference time to evaluate whether the action accuracy of the mechanical arm body meets the requirements.

[0013] Preferably, the intelligent mechanical arm exercise method further comprises the following step: the reference time is pre-recorded and saved in a storage module.

[0014] Preferably, the intelligent mechanical arm exercise method further comprises the following steps: after the completion of the first trajectory movement by the mechanical arm body, the mechanical arm body is moved to another height position in a vertical direction 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 mechanical arm body, a second completion time of the second trajectory movement is calculated based on the time nodes at which the mechanical arm body leaves the sensing interval and re-enters the sensing interval; and the second completion time is compared with another reference time to evaluate whether the action accuracy of the mechanical arm body meets the requirements.

[0015] Preferably, the intelligent mechanical arm exercise method further comprises the following step: when the action accuracy of the mechanical arm body is evaluated as not meeting the requirements, corresponding prompt information is output by a prompt module.

[0016] Preferably, the data acquisition module adopts a light beam sensor which is arranged to emit a detection light beam signal to the sensing interval.

[0017] Based on the above, the intelligent mechanical arm exercise system of the present application can monitor the action accuracy of the mechanical arm body in real time and dynamically adjust the training parameters according to the monitoring results, thereby significantly improving the autonomous learning ability, training efficiency and application range of the mechanical arm body by introducing a multi-modal interaction technology and a self-adaptive learning algorithm.

[0018] Specific implementation scheme

[0019] Specific design of the data acquisition module

[0020] The core component of the data acquisition module is a beam sensor, which is fixed within the operating range of the robotic arm via a bracket. The bracket is adjustable, allowing users to adjust the installation angle and position of the beam sensor according to their needs. The beam sensor integrates a light source emitter and a receiver. The beam signal emitted by the emitter covers the entire sensing range. When the robotic arm enters or leaves the sensing range, the receiver detects the change in the beam signal and transmits the time of the signal change to the processing module.

[0021] The specific algorithm of the computation processing module

[0022] The processing module incorporates an adaptive learning algorithm to analyze the motion accuracy of the robotic arm. The algorithm consists of the following steps:

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

[0024] Time difference calculation: Based on time node data, calculate the time difference between the robotic arm leaving the sensing zone and re-entering the sensing zone to obtain the first completion time or the second completion time.

[0025] Accuracy assessment: The calculated time difference is compared with the corresponding reference time. If the deviation exceeds the preset threshold, the motion accuracy of the robotic arm body is determined to be unacceptable.

[0026] Dynamic adjustment: Based on the evaluation results, adjust the training parameters of the robotic arm body, such as increasing the training intensity or changing the training trajectory.

[0027] Specific functions of the feedback control module

[0028] The feedback control module provides visual feedback information to the user through a prompting module. The prompting module combines an LED display with a voice broadcast device. When the processing module determines that the robotic arm's motion accuracy does not meet requirements, the prompting module displays specific error information and prompts the user to take appropriate measures via voice broadcast.

[0029] Specific implementation of the storage module

[0030] The storage module uses non-volatile storage media to store reference time and other critical parameters. It connects to the processing module via a high-speed data interface, ensuring efficient data reading and writing. Furthermore, the storage module supports data import and export via external devices, facilitating subsequent analysis and optimization.

[0031] Overall System Workflow

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

[0033] The 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 baseline time exceeds a preset threshold, the feedback control module outputs a prompt message through the prompt module.

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

[0036] Through the above specific implementation scheme, 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 in the existing technology, and provide a brand-new solution for efficient and intelligent robotic arm training in complex scenarios. Attached Figure Description

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

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

[0039] Figure 3 This is a flowchart illustrating the intelligent robotic arm training method of the present invention;

[0040] Figure 4 This is a schematic diagram illustrating the working principle of the feedback control module of the present invention. Detailed Implementation

[0041] The intelligent robotic arm training system of the present invention includes a robotic arm body, a data acquisition module, a computation and processing module, a beam sensor, a sensing range, a prompting module, and a storage module. The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0042] like Figure 1As shown, the robotic arm body is the execution component of the entire system. It is fixed to the work platform by a base and can perform multi-degree-of-freedom movements within a preset space. The data acquisition module is located within the operating range of the robotic arm body to detect whether the robotic arm body is within the sensing range. The core component of the data acquisition module is a beam sensor, which is fixed around the operating path of the robotic arm body by a bracket. The bracket has 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 range. The beam sensor integrates a light source emitter and a receiver. The beam signal emitted by the light source emitter covers the sensing range. When the robotic arm body enters or leaves the sensing range, the receiver captures the change in the beam signal and transmits the time point of the signal change to the processing module.

[0043] The processing module connects to the robotic arm and the data acquisition module, receiving and analyzing time-aware data from the data acquisition module. The processing module incorporates an adaptive learning algorithm, which consists of multiple steps. First, it receives the time-aware data from the data acquisition module and removes outliers caused by environmental interference. Then, it calculates the time difference between the robotic arm leaving and re-entering the sensing zone based on the time-aware data, obtaining either a first completion time or a second completion time. Next, it compares the calculated time difference with a baseline time stored in the storage module. If the deviation exceeds a preset threshold, the robotic arm's motion accuracy is deemed unsatisfactory. Finally, the processing module adjusts the robotic arm's training parameters based on the evaluation results, such as increasing training intensity or changing the training trajectory.

[0044] The storage module connects to the processing module to store the base time and other key parameters. Utilizing non-volatile storage media, the storage module connects to the processing module via a high-speed data interface, ensuring efficient data reading and writing. Furthermore, the storage module supports data import and export via external devices, facilitating subsequent analysis and optimization.

[0045] The prompting module is connected to the calculation and processing module. When the calculation and processing module determines that the motion accuracy of the robotic arm does not meet the requirements, the prompting module provides the user with visual feedback information. The prompting module adopts a design that combines an LED display screen and a voice broadcast device. When the calculation and processing module determines that the motion accuracy of the robotic arm does not meet the requirements, the prompting module will display specific error information and remind the user to take appropriate measures through voice broadcast.

[0046] like Figure 2As shown, the installation location 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 body by 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. The beam sensor integrates a light source emitter and a receiver. The beam signal emitted by the light source emitter covers the entire sensing range. When the robotic arm body enters or leaves the sensing range, the receiver captures the change in the beam signal and transmits the time point of the signal change to the computing module.

[0047] like Figure 3 As shown, the intelligent robotic arm training method of the present invention is applicable to the robotic arm body and includes the following steps: the robotic arm body executes a first trajectory movement corresponding to a first plane in a three-dimensional coordinate system; a data acquisition module detects whether the robotic arm body is located within a predetermined sensing range; during the execution of the first trajectory movement, the first completion time of the first trajectory movement is calculated based on the time nodes when the robotic arm 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 body meets the requirements. After the robotic arm body completes the first trajectory movement, it moves vertically to another height position and then executes a second trajectory movement corresponding to a second plane in a three-dimensional coordinate system; during the execution of the second trajectory movement, the second completion time of the second trajectory movement is calculated based on the time nodes when the robotic arm 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 body meets the requirements. When the motion accuracy of the robotic arm body is found to be unsatisfactory, a corresponding prompt message is output through a prompt module.

[0048] like Figure 4 As shown, the working principle of the feedback control module is further clarified. When the calculation and processing module determines that the motion accuracy of the robotic arm does not meet the requirements, the prompting module displays specific error information on the LED display screen and plays voice prompts through the voice broadcasting device to remind the user to take appropriate measures.

[0049] The intelligent robotic arm training system of this invention achieves real-time monitoring and dynamic adjustment of the robotic arm's motion accuracy through the coordinated operation of the robotic arm body, data acquisition module, computation and processing module, beam sensor, sensing range, prompting module, and storage module. Specifically, the robotic arm body executes a first trajectory movement according to a preset trajectory, and the data acquisition module monitors in real time whether the robotic arm body is within the sensing range. The computation and 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 stored 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 prompting module. After the robotic arm body completes the first trajectory movement, it moves vertically to another height position and executes a second trajectory movement, repeating the above steps. Through the above specific implementation scheme, the intelligent robotic arm training system of this invention can effectively solve the problems of insufficient intelligence, single training mode, and imperfect feedback mechanism in the prior art, providing a brand-new solution for efficient and intelligent robotic arm training in complex scenarios.

[0050] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention will be further explained below in conjunction with a specific application scenario.

[0051] In practical applications, the intelligent robotic arm training system of this invention can be deployed in the assembly process of industrial production lines to improve the accuracy and efficiency of robotic arms in complex assembly tasks. For example, in the assembly of electronic components, the robotic arm needs to accurately insert micro-components into designated positions on a printed circuit board (PCB). This scenario requires the robotic arm to have high-precision motion control capabilities, and the system of this invention can meet this requirement by real-time monitoring and dynamic adjustment of training parameters.

[0052] First, the robotic arm is fixed to the work 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 a suitable angle and position via a bracket to ensure that the emitted beam signal can cover the entire sensing range. The location of the sensing range is set according to the actual needs of the assembly task, typically corresponding to key nodes in the robotic arm's movement trajectory, such as component pick-up or placement points. When the robotic arm performs its first trajectory movement, the receiver of the beam sensor captures the time points when the robotic arm enters or leaves the sensing range and transmits these time point data to the processing module.

[0053] Subsequently, the processing module analyzes and processes the received time node data. In the first step, the processing module uses a built-in algorithm to remove outliers caused by environmental interference (such as light reflection or vibration) to ensure data accuracy. Next, the processing module calculates the time difference between the robotic arm leaving and re-entering the sensing zone based on the time node data, obtaining the first completion time. This time difference reflects the actual speed and stability of the robotic arm when executing the first trajectory movement. The processing module compares the calculated first completion time with the reference time stored in the storage module. If the deviation between the first completion time and the reference time exceeds a preset threshold, the robotic arm's motion accuracy is deemed unsatisfactory.

[0054] Based on this, the feedback control module outputs specific error information to the user through the prompt module. The prompt module combines an LED display screen with a voice broadcast device. When the robotic arm's motion accuracy does not meet requirements, the LED display screen shows the specific error value, while the voice broadcast device plays a prompt tone to remind the user to take appropriate measures. For example, the user can adjust the robotic arm's motion parameters based on the prompt information, or increase the training intensity to improve its motion accuracy.

[0055] After the robotic arm completes its first trajectory movement, it moves vertically to another height position and begins its second trajectory movement. This second trajectory movement typically corresponds to a planar path in another three-dimensional coordinate system, such as moving from a component pickup point to a placement point. During this process, the data acquisition module continues to monitor whether the robotic arm is within the sensing range and transmits time node data to the processing module. The processing module repeats the above steps, calculates the second completion time, and compares it with another reference time to evaluate the motion accuracy of the robotic arm in the second trajectory movement.

[0056] Through the above steps, the intelligent robotic arm training system of this invention achieves real-time monitoring and dynamic adjustment of the robotic arm's movement accuracy. For example, in an electronic component assembly scenario, when the robotic arm fails to complete the component picking or placing action within a specified time, the system will promptly issue a prompt message to help the user quickly locate the problem and optimize the training plan. Furthermore, the storage module supports users importing or exporting data via external devices, facilitating subsequent in-depth analysis and optimization of the robotic arm's training effect.

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

[0058] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent robotic arm training system, characterized in that, include: The main body of the robotic arm executes a first trajectory motion corresponding to the first plane in the three-dimensional coordinate system; After completing the first trajectory movement, it moves vertically to another height position, and then executes the second trajectory movement corresponding to the second plane in the three-dimensional coordinate system; The data acquisition module employs a beam sensor, which is fixed within the operating range of the robotic arm body via an adjustable bracket. The beam sensor is used to emit a detection beam signal into a predetermined sensing range to detect whether the robotic arm body is located within the sensing range. The computation processing module is connected to the robotic arm body and the data acquisition module. The computation processing module is configured to: calculate the first completion time of the first trajectory movement based on the time nodes when the robotic arm body leaves the sensing range and re-enters the sensing range during the execution of the first trajectory movement by the robotic arm body; and compare the first completion time with the reference time to evaluate the motion accuracy of the robotic arm body. During the execution of the second trajectory motion, the second completion time of the second trajectory motion is calculated based on the time nodes when the robotic arm body leaves the sensing range and re-enters the sensing range, and the second completion time is compared with another reference time to evaluate the motion accuracy of the robotic arm body; A storage module is connected to the processing module, and the reference time and another reference time are pre-recorded and stored in the storage module; The prompting module is connected to the computing and processing module. The prompting module includes an LED display screen and a voice broadcasting device. When the computing and processing module determines that the motion accuracy of the robotic arm body does not meet the requirements, the prompting module outputs corresponding prompt information.

2. An intelligent robotic arm training method, applicable to the main body of a robotic arm, characterized in that: Includes the following steps: S1. The robotic arm body performs a first trajectory motion corresponding to the first plane in the three-dimensional coordinate system; S2. The data acquisition module detects whether the main body of the robotic arm is located within the predetermined sensing range. The data acquisition module uses a beam sensor, which is fixed within the operating range of the main body of the robotic arm by an adjustable bracket and emits a beam signal for detection into the sensing range. S3. During the execution of the first trajectory motion by the robotic arm body, calculate the first completion time of the first trajectory motion based on the time nodes when the robotic arm body leaves the sensing range and re-enters the sensing range. S4. Compare the first completion time with the reference time that has been pre-recorded and stored in the storage module to evaluate the motion accuracy of the robotic arm body; if the motion accuracy does not meet the requirements, output the corresponding prompt information through the prompt module that includes an LED display screen and a voice broadcast device. S5. After the main body of the robotic arm completes the first trajectory movement, it moves vertically to another height position and then executes the second trajectory movement corresponding to the second plane in the three-dimensional coordinate system. S6. During the second trajectory motion performed by the robotic arm body, calculate the second completion time of the second trajectory motion based on the time nodes when the robotic arm body leaves the sensing range and re-enters the sensing range. S7. Compare the second completion time with another reference time that has been pre-recorded and stored in the storage module to evaluate the motion accuracy of the robotic arm body; if the motion accuracy does not meet the requirements, output the corresponding prompt information through the prompt module.

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