An Embedded Vision-Based Autonomous System for Converting Hand-Drawn Glass Sketches into Engraved Objects

This paper presents an embedded vision-based autonomous system designed to convert hand-drawn sketches created on a transparent glass surface into engraved or cut patterns on solid materials such as wood and plastic. The proposed machine aims to simplify human–machine interaction in digital fabrication by enabling users to draw naturally by hand without requiring a computer, display, or specialized software interface.

The system integrates a glass-based drawing surface positioned above an embedded camera that captures the user’s sketch from below. The acquired image is processed locally on a Raspberry Pi, where embedded image processing algorithms are applied to extract contours and geometric features from the hand-drawn sketch. The extracted paths are then converted into standard G-code instructions, ensuring compatibility with conventional CNC motion control principles.

The generated G-code is executed directly by a dedicated mechatronic platform consisting of a Cartesian motion system driven by stepper motors. To ensure positional accuracy and repeatability, a reference positioning sensor is employed to define consistent machine origin prior to each operation. After pressing a single physical start button, the system autonomously reproduces the original hand-drawn sketch as an engraved or cut pattern on the target material without further user intervention.

Unlike conventional CNC or laser engraving systems that depend on external computers and complex graphical interfaces, the proposed solution emphasizes autonomy, usability, and compact system integration. Experimental results demonstrate reliable reproduction of complex hand-drawn shapes with stable motion behavior and repeatable positioning accuracy.

The proposed approach is particularly suitable for educational environments, artistic fabrication, and low-cost rapid prototyping applications, and highlights the potential of embedded vision and autonomous mechatronic systems in human-centered manufacturing workflows.