WLKATA Fruit Picking Cell (Mirobot) | AI Vision Touch Screen |

The WLKATA Mirobot Fruit Picking Cell is a turnkey training workstation that combines a Mirobot 6-axis robotic arm with an AI vision system and an interactive touch screen interface to simulate an automated fruit sorting and picking production line. Designed for vocational training centers, technical colleges, and industry certification programs, this cell provides a realistic, hands-on environment where students learn the principles of vision-guided robotic automation as applied in the food processing, agriculture, and logistics industries.

The integrated AI vision system uses a camera module and machine learning algorithms to identify, classify, and locate simulated fruit objects on the workspace based on color, shape, and size. Once detected, the Mirobot arm autonomously plans and executes pick-and-place motions to sort the objects into designated bins or conveyor positions. This closed-loop workflow of perception, decision, and action teaches students the full automation pipeline used in modern smart manufacturing and agricultural processing facilities.

An interactive touch screen interface provides intuitive control over the entire cell, allowing operators to configure sorting rules, monitor real-time vision output, adjust robotic arm parameters, and review production statistics without writing code. The system ships as a complete, pre-integrated package requiring minimal setup, making it accessible to institutions that need an immediate, classroom-ready demonstration of how AI, robotics, and automation converge in Industry 4.0 applications.

• Robotic Arm: WLKATA Mirobot 6-axis (included)
• Vision System: AI camera module with real-time object detection and classification
• Display: Interactive touch screen HMI panel
• Sorting Criteria: Color, shape, size recognition
• End-Effector: VisionSoftGrip (AI Vision + Three-finger Soft Gripper)
• Workspace: Integrated baseplate with designated picking and sorting zones
• Communication: USB, Wi-Fi, serial interfaces
• Power Supply: AC adapter (included)
• Controller: WLKATA AI-HUB (Arduino MEGA 2560-based)
• Software: WLKATA BlockFactory drag-and-drop programming platform
• Curriculum Materials: Lesson plans, lab exercises, and assessment guides included

• Turnkey AI-Vision-Guided Workstation: Simulates real-world automated fruit picking and sorting production line operations for vocational and technical education.
• Integrated AI Vision System: Performs real-time object detection, classification by color, shape, and size, and localization for closed-loop pick-and-place automation.
• Interactive Touch Screen HMI: Enables intuitive configuration of sorting rules, real-time vision monitoring, arm parameter adjustment, and production statistics review.
• Pre-Integrated System Design: All mechanical, electrical, and software components are included and factory-calibrated for immediate classroom deployment with minimal setup.
• WLKATA Mirobot 6-Axis Arm: Provides authentic industrial robot kinematics in a safe, desktop-scale form factor suitable for classroom and laboratory environments.
• Full Industry 4.0 Automation Pipeline: Demonstrates perception, decision, and action workflow, preparing students for careers in smart manufacturing and agricultural technology.
• Configurable Sorting Rules: Allows educators to create varied exercise scenarios of increasing complexity, from simple color sorting to multi-criteria classification challenges.
• Comprehensive Curriculum Materials: Lesson plans, lab exercises, and assessments support structured teaching of AI, vision systems, and robotic automation.

The WLKATA Mirobot Fruit Picking Cell is designed for vocational training centers, technical colleges, and university engineering departments that teach robotics, automation, and artificial intelligence. Instructors can use the cell to demonstrate how AI vision, robotic manipulation, and process control work together in real production environments, giving students direct experience with the technologies driving Industry 4.0 transformation across manufacturing and agriculture.

Agricultural technology programs benefit from the fruit picking simulation, which models the challenges of automated harvesting including object identification under variable conditions, gentle handling of irregularly shaped items, and high-speed sorting based on multiple quality criteria. Food processing and logistics training programs can adapt the cell to demonstrate quality inspection, automated packaging, and warehouse sorting operations.

Research laboratories and corporate training facilities use the Fruit Picking Cell as a prototyping platform for vision-guided robotic workflows. The modular architecture and open programming interface allow advanced users to develop custom sorting algorithms, integrate external sensors, and test new end-effector designs before deploying solutions on full-scale industrial equipment.

The WLKATA Mirobot Fruit Picking Cell arrives as a pre-integrated system with all mechanical and electrical connections factory-assembled. Place the cell on a stable, flat surface with access to a standard power outlet. Connect the AC adapter to the main control box mounted beneath the baseplate and power on the system. The touch screen interface will display the main control dashboard within seconds of startup.

Calibrate the AI vision system using the included calibration stand and fruit models. The touch screen guides you through the automatic calibration sequence, which aligns the camera coordinate system with the Mirobot arm workspace. Once calibration is complete, place the fruit tree stand in its designated position, attach the fruit models, and select a sorting program from the touch screen menu to begin the automated picking demonstration.

For custom programming, install the WLKATA BlockFactory software on a connected computer. BlockFactory provides a drag-and-drop interface for creating new sorting rules, adjusting vision parameters, and programming complex robotic motion sequences. The included curriculum materials contain step-by-step lab exercises that progressively introduce students to each subsystem before combining them into complete automated workflows.