A new open-source project demonstrates how to combine computer vision and robotics on a single board. The robot, about the size of a desk, detects a human face through locally executed machine learning and turns toward it in real time, working at roughly 10–15 frames per second. Everything runs on the new Arduino UNO Q, without relying on the cloud.
Dual-processor architecture
The operation leverages the hybrid architecture of the UNO Q, which integrates two distinct units. The Qualcomm MPU microprocessor, running Linux, executes the AI model and the control logic written in Python. The STM32 MCU microcontroller, running Zephyr RTOS, drives the PWM signal of the servomotors with real-time precision.
The two processors communicate via Bridge RPC, a protocol based on MessagePack over an internal serial link, with a round-trip latency of about 8 milliseconds. This way, heavy inference stays separate from the real-time path, keeping motor control deterministic.
Vision, control, and web interface
A USB webcam captures video, while a lightweight face-detection model locates the face in the frame. A proportional controller then converts the horizontal position of the face into differential commands for the wheels. The project uses two ready-made App Lab “bricks”: one for camera-based object detection, the other for the web interface.
In particular, the browser-accessible dashboard lets you monitor detections and adjust steering parameters in real time, without recompiling the code. Every change to the sliders takes effect immediately via Socket.IO, making the tuning phase quick.
Technical details and motion safety
The robot uses differential drive with two continuous-rotation servos: a 1500-microsecond pulse corresponds to stop, while lower or higher values determine rotation in either direction. Additionally, the system limits Bridge calls to a maximum of 20 Hz, because sending commands too quickly would block the serial link.
The project therefore includes several protections: a “coasting” mechanism that maintains the last known position when the face disappears for a moment, a watchdog that stops the robot if detections are interrupted, and an emergency stop that ignores rate limiting to halt the motors immediately. Classified as an intermediate-level project, it is fully documented and released under an open-source license.
My previous attempt at this project failed because I used a different microcontroller. That specific setup required a custom hardware interface to handle the sensor data.