Sorting mechanical arm control system based on two-dimensional code recognition

Abstract

The invention relates to the field of industrial robot control, discloses a sorting mechanical arm control system based on two-dimensional code recognition, and aims to improve the recognition efficiency and the automatic control capability in the material sorting process. According to the system, an industrial camera is used for collecting a material image with a two-dimensional code, a two-dimensional code recognition module is used for analyzing the content of the two-dimensional code, and the category, position information and a sorting instruction of the material are obtained. Compared with a traditional recognition mode based on colors, shapes or depth images, the two-dimensional code has the advantages of being high in recognition accuracy, small in background interference, high in recognition speed, high in information bearing capacity, simple in implementation algorithm and the like, and the response efficiency and the control precision of a system can be remarkably improved. The system completes conversion from an image coordinate to a mechanical arm coordinate system through the computer processing unit, generates a control instruction in combination with inverse kinematics and a path planning algorithm, and drives the mechanical arm to complete material grabbing and placing operation. The system is simple in structure, stable in recognition, high in adaptability and suitable for various automatic sorting application scenes such as intelligent warehousing, production lines and sorting centers.

Description

Technical Field

[0001] The present invention relates to the field of industrial robot control, and specifically to a sorting robot arm control system based on QR code recognition, which aims to improve the recognition efficiency and automated control capabilities during the material sorting process. Background Art

[0002] With the continuous improvement of industrial automation, robotic arms are widely used for tasks such as sorting and handling on production lines. However, existing sorting systems generally rely on traditional material recognition methods such as color, shape, and depth images. These methods have shortcomings such as low recognition accuracy, sensitivity to environmental interference, and slow processing speed. They are unable to meet the increasingly complex production needs. In particular, in scenarios where fast, efficient, and precise automated sorting is required, traditional technologies often fail to achieve ideal performance.

[0003] QR code technology has been widely adopted in logistics and warehousing in recent years due to its large information capacity, strong anti-interference capabilities, and efficient recognition capabilities. QR codes can provide detailed information about materials in a concise and efficient manner, and the recognition process is not significantly affected by environmental complexity. However, existing sorting robot systems have not yet fully integrated QR code technology. In particular, in scenarios involving dynamic operations and multi-material sorting, effectively combining QR code recognition with robot control systems to achieve efficient material handling and placement remains an urgent challenge.

[0004] Therefore, it is very meaningful to design a new sorting robot control system based on QR code recognition, which can combine QR code recognition technology, visual information processing, robot control and path planning algorithm to solve the efficiency, accuracy and adaptability problems in traditional sorting systems. This will improve the level of automation in the material sorting process and is widely applicable to automated sorting applications such as smart warehousing, production lines, and logistics sorting centers. Summary of the Invention

[0005] This invention provides a sorting robot control system based on QR code recognition. This technology aims to improve automated control capabilities and recognition efficiency during the material sorting process. The system uses an industrial camera to capture images of materials labeled with QR codes. A QR code recognition module parses the codes to obtain the material's category, location, and sorting instructions. Finally, the robot arm performs the material grabbing and placement operations.

[0006] To implement the technical solution of the present invention, an industrial camera is first selected as an image collection unit to obtain images of materials with QR code labels on a workbench in real time;

[0007] Then, the collected image is processed by the QR code recognition module, the QR code is parsed, and the category and location information of the target material are obtained;

[0008] The system further converts the coordinate information in the QR code image into three-dimensional space coordinates in the robotic arm coordinate system through a computer processing module, and generates control instructions by combining inverse kinematics algorithms and path planning;

[0009] The control instructions drive the end effector of the robot arm to complete the material grabbing operation through the path planning and motion control module. The end effector is a claw-type structure with the ability to adapt to the grabbing of different materials;

[0010] After the grabbing operation is completed, the robotic arm moves the material to the designated location according to the control instructions, places the material and completes the task;

[0011] The system further improves the accuracy of material positioning by combining a computer vision module with a depth camera module. The depth camera provides three-dimensional spatial information of the object, ensuring that the robotic arm can accurately perform tasks.

[0012] The QR code recognition module of the present invention is based on the OpenCV image processing library. It extracts the QR code area through steps such as image grayscale, filtering, binarization, morphological closing operation and perspective correction, thereby effectively improving the recognition accuracy of the QR code and ensuring efficient execution during the sorting process.

[0013] The computer processing module also uses hand-eye calibration technology to establish a mapping relationship between the industrial camera coordinate system and the robotic arm coordinate system, thereby accurately calculating the position of the target object in three-dimensional space and ensuring that the robotic arm can respond accurately based on the image coordinates;

[0014] The path planning module of the present invention adopts the MoveIt motion planning module based on the ROS framework. Through inverse kinematics and path planning algorithms, the robot arm can calculate the optimal motion trajectory, thereby ensuring that the robot arm can complete the task efficiently and accurately;

[0015] The control module of the present invention realizes real-time communication with the robot control unit through the TCP / IP protocol and supports the coordinated operation of multiple robot arms, thereby improving the flexibility and collaboration capability of the system;

[0016] The end effector is equipped with a force sensor to monitor the gripping force in real time and dynamically adjust the gripping force to prevent damage or slipping of materials during the gripping process, ensuring the safety and stability of the gripping operation.

[0017] Through the present invention, the material sorting system can efficiently and accurately complete the grabbing and placing operations of materials, and has broad application prospects in application scenarios such as intelligent warehousing, production lines, and logistics sorting centers. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is the overall block diagram of the system structure;

[0019] Figure 2 It is the system workflow diagram; DETAILED DESCRIPTION

[0020] In order to clearly illustrate the above-mentioned objects and features of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present invention.

[0022] Implementation example.

[0023] like Figure 1 As shown, this embodiment provides a sorting robot arm control system based on QR code recognition, which is mainly used in express sorting scenarios. The system includes a six-degree-of-freedom robot arm, an industrial camera, a QR code recognition module, a computer processing module, and an end effector.

[0024] In this example, materials are first divided into three types of express boxes, each of which is affixed with a unique QR code. To accurately classify and grasp materials, an industrial camera is first required to capture images with QR codes. In the system, the industrial camera is used to capture images in real time, and the QR code recognition module is responsible for parsing the content in the QR code and extracting the category (such as the size of the express box) and location information of the target material.

[0025] The QR code recognition module used in this system is based on the OpenCV image processing library. It processes images through grayscale conversion, filtering, binarization, morphological closing operations, and perspective correction techniques to accurately extract the QR code area and decode the QR code. Compared with traditional color or shape recognition methods, QR codes provide higher recognition accuracy and stronger anti-interference capabilities, making them adaptable to various complex environments.

[0026] After the QR code recognition is completed, the computer processing module converts the image coordinates into three-dimensional coordinates in the robot arm's base coordinate system based on the image coordinates and the position information in the QR code, combined with hand-eye calibration technology. To achieve this conversion, the system uses the Zhang Zhengyou calibration method to perform internal reference correction on the industrial camera and calculates the mapping relationship between the camera coordinate system and the robot arm coordinate system using the nine-point calibration method.

[0027] Next, the system uses inverse kinematics and path planning algorithms to generate the robot's motion path. Based on the ROS framework and the MoveIt module, the path planning module uses inverse kinematics to calculate the precise pose required for the robot's end effector, ensuring the robot can accurately reach its target position.

[0028] The robotic arm then moves according to the generated path instructions, and the end effector (gripper) performs the grasping action according to the control instructions. During the grasping process, the end effector monitors the gripping force in real time through the integrated force sensor to ensure stability and safety during the grasping process, avoiding damage or slipping of the express box.

[0029] After completing the material grabbing, the robot arm moves the material to the designated location according to the control instructions. The system uses the path planning and motion control modules to ensure the accuracy of the robot arm's path during the handling process, and finally places the express box in the target area. If the task includes the "placement" operation of the material, the system will continue to plan the action path and place the material at the designated target location;

[0030] After the task is completed, the system automatically returns to the standby state and prepares to perform the next round of sorting tasks;

[0031] The entire task process is as follows Figure 2 shown.

[0032] The foregoing description is merely a preferred embodiment of the present invention. Furthermore, those skilled in the art will readily appreciate that various modifications and variations of the present invention may be made while maintaining the principles of the present invention. If such modifications and variations fall within the scope of the claims and their equivalents, such modifications and variations shall also be considered within the scope of protection of the present invention.

Claims

1. A sorting robot arm control system based on QR code recognition, characterized in that: include: Industrial cameras, QR code recognition modules, computer processing modules, robotic arms and their end effectors; Wherein, the industrial camera is used to capture material images containing QR codes; The QR code recognition module is used to parse the QR code and obtain the category information and location information of the target material; The computer processing module is used to convert the image coordinates of the QR code into three-dimensional coordinates in the robotic arm coordinate system, and generate control instructions by combining the inverse kinematics algorithm and path planning; The robotic arm execution module is used to complete the grabbing and sorting and placing operations of the target materials according to the control instructions.

2. The system according to claim 1, wherein: The QR code recognition module is based on the OpenCV image processing library, and extracts the QR code area through image grayscale, filtering, binarization, morphological closing operation and perspective correction, and locates and decodes the QR code pattern.

3. The system according to claim 1, wherein: The computer processing module establishes a mapping relationship between the image coordinate system and the robotic arm base coordinate system based on hand-eye calibration to achieve spatial positioning.

4. The system according to claim 1, wherein: The path planning uses the MoveIt module under the ROS framework for motion planning and trajectory generation.

5. The system according to claim 1, wherein: The control module realizes real-time communication with the robot control unit via a local area network (LAN) of TCP / IP protocol and supports the collaborative work of multiple robot arms.

6. The system according to claim 1, wherein: The robotic arm is a six-degree-of-freedom robotic arm with a gripper end effector at the end for performing object grasping, moving and placing operations.

7. The system according to claim 1, wherein: The end effector is integrated with a force sensor to monitor the contact force during the grasping process in real time and dynamically adjust the gripping force of the gripper based on the feedback to avoid material damage.

8. A remote control method for a robotic arm based on the system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: A method for remotely controlling a robotic arm based on the system according to any one of claims 1 to 7, characterized in that it comprises the following steps; Step 2: Use an industrial camera to capture the object image, identify and analyze the QR code information; Step 3: Extract the object’s position information based on the recognized QR code information, and generate the robot’s motion path through coordinate transformation and inverse kinematics calculation; Step 4: The robotic arm performs the grasping operation according to the generated path instructions; Step 5: After the robot arm completes the grabbing, it places the material in the designated location according to the instructions; Step 6: After completing the task, the system ends the current operation and prepares for the next sorting task.

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