Industrial robot AI control method and system, teaching method and storage medium

Abstract

The invention provides an industrial robot AI control method, an industrial robot AI control system, a teaching method and a storage medium. The industrial robot AI control method comprises the following steps: an image acquisition step: practically acquiring images of a working environment and a target object through a camera; an object identification and positioning step of performing object identification and positioning on the image by using a deep learning algorithm to obtain position information of a target object; an AI processing step: generating an industrial robot action instruction according to the object identification and positioning information obtained by the reinforcement learning algorithm and the object identification and positioning step; and an execution step: controlling the industrial robot to execute a corresponding task according to the action instruction of the industrial robot. The intelligent control system has the beneficial effects that intelligent control over the industrial robot is achieved by combining the visual system and the AI technology, and the industrial robot can sense environment changes, make decisions autonomously and optimize operation. And by utilizing a reinforcement learning algorithm, the industrial robot can continuously optimize the action decision through tests and feedback, and the task completion efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the field of AI technology, and in particular to an industrial robot AI control method, system, teaching method and storage medium. Background Art

[0002] Existing industrial robots usually rely on pre-programmed paths and movements to complete specific tasks, lacking flexibility and intelligence. With the development of intelligent manufacturing, industrial robots need to combine AI technology to improve their perception, autonomous decision-making and adaptive capabilities to cope with complex and changing working environments.

[0003] Current industrial robots lack technical solutions that can be combined with AI technology, which needs to be addressed urgently. Summary of the invention

[0004] The present invention provides an industrial robot AI control method, comprising sequentially performing the following steps:

[0005] Image acquisition steps: Use the camera to collect images of the working environment and target objects;

[0006] Object recognition and positioning steps: Use deep learning algorithms to identify and locate objects in images and obtain the location information of target objects;

[0007] AI processing step: Generate industrial robot action instructions through object recognition and positioning information obtained through reinforcement learning algorithm and object recognition and positioning step;

[0008] Execution steps: According to the industrial robot action instructions, control the industrial robot to perform corresponding tasks.

[0009] As a further improvement of the present invention, the industrial robot AI control method further includes an image processing step.

[0010] Image processing step: performing image preprocessing on the images collected in the image acquisition step to improve image quality;

[0011] The image acquisition step is followed by the image processing step, and the object recognition and positioning step is followed by the image processing step.

[0012] As a further improvement of the present invention, in the image processing step, image preprocessing includes denoising, enhancement, and edge detection.

[0013] As a further improvement of the present invention, in the object recognition and positioning step, the deep learning algorithm is CNN.

[0014] As a further improvement of the present invention, in the AI ​​processing step, the reinforcement learning algorithm: optimizes the industrial robot's action decisions through experiments and feedback to generate the best operation path.

[0015] As a further improvement of the present invention, the camera includes a 2D camera and a 3D camera.

[0016] As a further improvement of the present invention, the industrial robot includes a six-axis robot and a four-axis robot.

[0017] The present invention also discloses an industrial robot AI control system, comprising: a memory, a processor, and a computer program stored in the memory, wherein the computer program is configured to implement the steps of the industrial robot AI control method of the present invention when called by the processor.

[0018] The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of the industrial robot AI control method of the present invention when called by a processor.

[0019] The invention also discloses a teaching method, comprising:

[0020] Teaching step: The operator manually guides the industrial robot to complete the task, and generates teaching data by recording the movements and paths during the operation;

[0021] Learning steps: Industrial robots use teaching data for preliminary learning, and further optimize operation strategies through reinforcement learning algorithms to improve task completion efficiency and accuracy;

[0022] Autonomous learning steps: In actual operation, industrial robots continuously adjust and optimize operating strategies based on real-time feedback to achieve adaptive control.

[0023] The beneficial effects of the present invention are as follows: the present invention realizes intelligent control of industrial robots by combining visual systems and AI technology, so that industrial robots can perceive environmental changes, make autonomous decisions and optimize operations. By using reinforcement learning algorithms, industrial robots can continuously optimize their action decisions through experiments and feedback, and improve the efficiency and accuracy of task completion. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0025] like Figure 1 As shown, the present invention discloses an industrial robot AI control method, comprising the following steps:

[0026] Step 1, image acquisition step: images of the working environment and target objects are acquired by using a camera, wherein the camera includes a 2D camera and a 3D camera.

[0027] Step 2, image processing step: perform image preprocessing on the image collected in the image acquisition step, such as denoising, enhancement, edge detection, etc., to improve image quality.

[0028] Step 3: Object recognition and positioning: Use deep learning algorithms (such as CNN) to identify and locate objects in the image and obtain the location information of the target object.

[0029] Step 4, AI processing step: Generate industrial robot action instructions through the object recognition and positioning information obtained by the reinforcement learning algorithm and the object recognition and positioning step; Reinforcement learning algorithm: Optimize the industrial robot action decision through experiments and feedback to generate the best operation path.

[0030] Step 5, execution step: according to the industrial robot action instructions, control the industrial robot to perform corresponding tasks.

[0031] The industrial robot includes a six-axis robot and a four-axis robot. The industrial robot AI control method of the present invention can complete a variety of operations. For example, the flat wire motor assembly can be completed by the cooperation of the six-axis robot and the four-axis robot. The present invention also includes a human-machine interaction interface: for operators to monitor and intervene in the robot operation.

[0032] The industrial robot AI control method of the present invention combines visual perception and reinforcement learning algorithm to achieve intelligent control of the industrial robot.

[0033] The invention also discloses a teaching method, comprising:

[0034] Teaching step: The operator manually guides the industrial robot to complete the task, and generates teaching data by recording the movements and paths during the operation;

[0035] Learning steps: Industrial robots use teaching data for preliminary learning, and further optimize operation strategies through reinforcement learning algorithms to improve task completion efficiency and accuracy;

[0036] Autonomous learning steps: In actual operation, industrial robots continuously adjust and optimize operating strategies based on real-time feedback to achieve adaptive control.

[0037] Through the teaching method of the present invention, the industrial robot can complete tasks autonomously through teaching and learning.

[0038] The present invention has the following advantages:

[0039] 1. The present invention realizes intelligent control of industrial robots by combining visual systems and AI technology, enabling industrial robots to perceive environmental changes, make autonomous decisions and optimize operations.

[0040] 2. Using reinforcement learning algorithms, industrial robots can continuously optimize their action decisions through experiments and feedback, thereby improving the efficiency and accuracy of task completion.

[0041] 3. Through the combination of teaching steps, learning steps and autonomous learning steps, industrial robots can quickly learn and adapt to new tasks, reducing students' programming and debugging time.

[0042] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. An industrial robot AI control method, characterized in that: The method comprises the following steps: an image acquisition step: acquiring images of a working environment and a target object by using a camera; Object recognition and positioning steps: Use deep learning algorithms to identify and locate objects in images and obtain the location information of target objects; AI processing step: Generate industrial robot action instructions through object recognition and positioning information obtained through reinforcement learning algorithm and object recognition and positioning step; Execution steps: According to the industrial robot action instructions, control the industrial robot to perform corresponding tasks.

2. The industrial robot AI control method according to claim 1, characterized in that: The industrial robot AI control method also includes an image processing step. Image processing step: performing image preprocessing on the images collected in the image acquisition step to improve image quality; The image acquisition step is followed by the image processing step, and the object recognition and positioning step is followed by the image processing step.

3. The industrial robot AI control method according to claim 2, characterized in that: In the image processing step, image preprocessing includes denoising, enhancement, and edge detection.

4. The industrial robot AI control method according to claim 1, characterized in that: In the object recognition and positioning step, the deep learning algorithm is CNN.

5. The industrial robot AI control method according to claim 1, characterized in that: In the AI ​​processing step, the reinforcement learning algorithm optimizes the industrial robot's action decisions through experiments and feedback to generate the best operation path.

6. The industrial robot AI control method according to claim 1, characterized in that: The camera includes a 2D camera and a 3D camera.

7. The industrial robot AI control method according to claim 1, characterized in that: The industrial robots include six-axis robots and four-axis robots.

8. An industrial robot AI control system, characterized in that: include: A memory, a processor, and a computer program stored in the memory, wherein the computer program is configured to implement the steps of the industrial robot AI control method according to any one of claims 1 to 7 when called by the processor.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of the industrial robot AI control method according to any one of claims 1 to 7 when called by a processor.

10. A teaching method, characterized in that: include: Teaching step: The operator manually guides the industrial robot to complete the task, and generates teaching data by recording the movements and paths during the operation; Learning steps: Industrial robots use teaching data for preliminary learning, and further optimize operation strategies through reinforcement learning algorithms to improve task completion efficiency and accuracy; Autonomous learning steps: In actual operation, industrial robots continuously adjust and optimize operating strategies based on real-time feedback to achieve adaptive control.

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