WLKATA AI Automatic Sorting Cell (Mirobot) | Touch Screen

The WLKATA AI Automatic Sorting Cell is a comprehensive AI Vision-Empowered Robotics Automation training cell that combines two Mirobot 6-axis arms, the WLKATA AI Vision Set, a conveyor belt set, and a master control box to perform vision-based dynamic object identification and intelligent robotic sorting. The vision set is capable of dynamically identifying target objects on the moving conveyor, enabling the robotic arms to execute coordinated pick-and-place sorting based on real-time visual recognition. Students learn to construct complete AI-driven automation systems through hands-on experimental sessions.

The cell architecture replicates real smart-manufacturing sorting lines: parts arrive on the conveyor belt, the AI Vision Set identifies them by color, shape, or other features, and the dual Mirobots execute coordinated pick-and-place to sort parts into designated destinations. The identification criteria can be altered to other items beyond the included color blocks, allowing students to create more intricate application scenarios that foster innovation and product-development skills. Programming is performed in C++ via Arduino using the Arduino Mega 2560-based master control box, with additional support for Python, Blockly, and ROS.

The product comes with the included WLKATA Mirobot Professional Kit (with Bluetooth controller), a WLKATA Mirobot Education Kit, an acrylic bottom plate, a WLKATA Conveyor Belt Set, the WLKATA AI Vision Set, a main control box, and color block sorting accessories (color sorting stand and color blocks). A flash disk delivers the complete curriculum package: experiment textbook for educators, source code, installation handbook, PPT quick start guide, and demonstration videos. Total weight across all five shipping packages: 23 kg (51 lbs).

• Product Type: Integrated AI vision-guided robotics sorting training cell
• Primary Robot: WLKATA Mirobot Professional Kit (x1) with Bluetooth controller
• Secondary Robot: WLKATA Mirobot Education Kit (x1)
• Mirobot Repeatability: ±0.2 mm
• Mirobot Payload: 250 g standard
• Mirobot Reach: 315 mm
• Vision System: WLKATA AI Vision Set with OpenMV-based camera module
• Vision Software: OpenCV-based object detection and classification, Python integration
• Conveyor System: WLKATA Conveyor Belt Set (x1), 720 x 260 x 85 mm package size, with variable-speed control
• Master Controller: Main control box based on Arduino Mega 2560
• Operator Interface: Touch-screen HMI module
• Sorting Accessories: Color sorting stand and color blocks (x1 set)
• Acrylic Bottom Plate: 825 x 520 x 30 mm
• Programming Languages: C++ via Arduino (primary), Blockly, Python, G-code, MATLAB, ROS
• AI/CV Frameworks: OpenCV, OpenMV; Python integration supports TensorFlow, PyTorch, YOLOv5
• Total Package Weight: Approximately 23 kg (51 lbs) across 5 shipping packages
• Power Supply: Standard wall outlet (110V/220V depending on region)
• Curriculum Materials: Flash disk (x1) with experiment textbook for educators, source code, installation handbook, PPT quick start guide, and demonstration videos (digital format)
• Target Industries Simulated: Electronics assembly, pharmaceutical packaging, food sorting, e-commerce fulfillment

• Dual-Mirobot Architecture: Two Mirobots coordinate to teach multi-robot allocation, work-zone scheduling, and collision avoidance. The Professional Kit adds a Bluetooth controller for handheld teach-pendant-style programming alongside the Education Kit's standard programming interface.
• WLKATA AI Vision Set: Dynamic object identification using the OpenMV-based camera module enables the cell to recognize parts by color, shape, and feature on the moving conveyor. Sorting decisions are made in real time based on visual classification.
• OpenCV-Based Curriculum: Vision processing uses OpenCV, the industry-standard computer vision framework. Skills transfer directly to industrial vision-guided automation roles using FANUC iRVision, Cognex VisionPro, and Keyence systems.
• Changeable Identification Type: Beyond the included color blocks, the AI Vision Set's identification criteria can be reprogrammed for other items: shape recognition, custom-trained AI models, barcode/QR detection. This extends the cell's curriculum scope to research-grade vision tasks.
• WLKATA Conveyor Belt Set: Programmable conveyor with adjustable speed teaches synchronous robot-conveyor coordination. The conveyor extends transport distance to 0.59 meters (1.9 feet) between work zones.
• Touch-Screen HMI: Operator interface enables recipe-based program selection, sort criteria configuration, and real-time status monitoring. The HMI workflow mirrors operator stations on real smart-factory sorting lines.
• Arduino Mega 2560 Master Control: The master control box runs on an industry-familiar Arduino Mega 2560 platform, giving students transferable embedded-systems experience alongside vision-AI training.
• C++ via Arduino Programming: Students program the cell in C++ via the Arduino IDE, learning industry-standard embedded programming alongside vision-system integration.
• Multi-Programming-Language Support: Mirobot's broad language support (Blockly, Python, C++, MATLAB, ROS) lets curriculum scale from K-12 introductory through university research using the same hardware platform.
• Comprehensive Textbook and Resources: Included flash disk delivers the textbook, source code, installation handbook, PPT quick start guide, and demonstration videos. The textbook covers AI Automatic Sorting project practices with detailed experimental objectives, instruments, and procedures.

Vocational training institutes and community colleges use the AI Automatic Sorting Cell for smart-manufacturing certificate programs. Students complete the textbook's structured exercises: robotic arm teaching programming, Arduino communication via RS485, Arduino-vision integration, conveyor-coupled sorting sequences, and dual-Mirobot coordination. The skills map directly to vision-system technician and smart-manufacturing operator roles in electronics, pharmaceutical, food processing, and e-commerce industries.

University courses in robotics, AI, computer vision, and industrial automation integrate the cell into multi-semester programs. Computer vision courses use it as a real-hardware target for OpenCV exercises; AI courses use it for vision-driven classification and online learning; robotics courses use it for multi-arm coordination and motion planning. Capstone and master's-level projects commonly deploy custom deep-learning models on the cell, with students presenting integrated AI-robotics systems for novel sort criteria.

Corporate workforce development programs preparing workers for smart-manufacturing transitions use the cell for hands-on retraining. The vision-AI-robotics integration mirrors the technology stack in modern Tier-1 supplier facilities, OEM smart factories, and Industry 4.0 deployments. R&D labs studying flexible automation, human-robot collaboration in sorting tasks, and AI-vision pipeline optimization also use the platform for benchtop prototyping before scaling to industrial cells.

Estimated Setup Time: 3-4 hours for unboxing, mechanical assembly, electrical connections, vision system calibration, and first integrated demo execution.

1. Step 1 - Unbox and Inventory: Verify the Mirobot Professional Kit, Mirobot Education Kit, AI Vision Set, Conveyor Belt Set, main control box, acrylic bottom plate (825 x 520 x 30 mm), color sorting stand and color blocks, touch-screen HMI, flash disk, and all cabling against the included packing list. Total weight across all 5 packages is approximately 23 kg.
2. Step 2 - Mechanical Assembly: Position the acrylic bottom plate on a level work surface. Mount both Mirobots in their designated work zones. Install the conveyor belt between them. Mount the AI Vision Set camera at its sensing position above the conveyor. Install the color sorting stand and touch-screen HMI mount.
3. Step 3 - Electrical Connections: Connect the Arduino Mega 2560 main control box to both Mirobots, conveyor drive, AI Vision Set, and HMI. Verify all power and signal connections including emergency-stop wiring.
4. Step 4 - Install Software and Load Curriculum: Copy the flash disk contents (textbook, source code, installation handbook, PowerPoints, videos) to a host PC. Install WLKATA Studio, the Arduino IDE, and OpenCV development tools.
5. Step 5 - Calibrate Mirobots and Vision: Run calibration for each Mirobot independently, then establish coordinate transformations between each arm and the conveyor. Run the AI Vision Set calibration to establish camera-to-conveyor coordinate transformation: critical for accurate pick-up of detected parts.
6. Step 6 - First Integrated Demo: Load and run an included color-sorting demo. Verify that color blocks on the conveyor are detected by the AI Vision Set, classified by color, and sorted by the two Mirobots into the correct positions on the color sorting stand.
7. Step 7 - Curriculum Onboarding: Begin the textbook's Project 1 (Robotic arm teaching programming). Progress through Project 2 (Arduino and RS485 communication), Project 3 (Arduino-vision integration), and subsequent projects in sequence.