Portable Artificial Intelligence
Educational Workstation
The Portable Artificial Intelligence Educational Workstation is a highly integrated, ready-to-use teaching platform featuring a modular quick-plug design that combines portability with strong scalability. The device comes with a built-in Ubuntu system, supports ROS1/ROS2, and integrates multiple technologies including machine vision, sensor control, and collaborative robotics—covering a wide range of interdisciplinary knowledge areas. It is equipped with 5 major visual recognition algorithms and 8 core learning points, supporting various interactive methods such as one-click startup and drag-and-teach. It enables students to quickly engage in AI practices without programming. Compatible with 6 types of robotic arms and 4 types of end effectors, it is ideal for teaching and training, helping students systematically master different knowledge.
Five Major Visual Recognition Algorithms
The workstation offers 4 colors of wooden blocks and various card shapes to choose from. It allows students to explore the relationship between 2D and 3D worlds through QR code recognition, learn image segmentation and feature extraction using feature point recognition, and deepen their understanding of the YOLOv5 algorithm. With 4 different robotic accessories, it supports a wide range of application scenarios.
Six Compatible Robotic Arms
It supports the M5Stack and Raspberry Pi versions of myCobot 280, mechArm 270, and myPalletizer 260.
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COMPATIBLE WITH VARIOUS END EFFECTORS
Visualization Software
It supports visualization software operation and is equipped with an integrated keyboard, mouse, and joystick, enabling quick startup of the artificial intelligence AI kit.
Standardized Educational Curriculum Support
Adhering to the educational philosophy of “theory as the foundation + practice as empowerment,” the workstation deeply covers core technical modules such as machine vision cognition, image analysis and transformation, and object recognition. It integrates cutting-edge algorithms like YOLOv5 for object detection and incorporates practical projects involving shape, color, and QR code recognition. Over a 15-week cycle, it systematically builds a complete machine vision technology system from theory to application, empowering learners to advance their technical capabilities.
