We designed and built a 12 degree-of-freedom (3 servos per leg × 4 legs) quadruped robot controlled by a Raspberry Pi Pico W, featuring integrated environmental sensing and a wireless WiFi controller. Starting from a custom CAD body and 3D-printed frame, the robot combines mechanical engineering and embedded electrical engineering to create a platform capable of coordinated four-legged locomotion, heading determination, environmental mapping, and target detection. In order to do so, our system leverages several sensors including an IMU, a solid state LiDAR sensor, and a contact-less infrared sensor.
Program embedded devices with natural language. No firmware updates required. ScriptO Studio is a next-generation Integrated Development and Execution Environment (IDEE) for embedded devices running MicroPython.
A 6502 based laptop design. Specs include a 65C02 running at 8MHz, 46K RAM, BASIC in ROM, 65C22 VIA, 9" display, built-in keyboard, Compact Flash storage, 10000mAh battery, USB-C powered/charged, serial console, and an internal expansion slot. The project details the build process, current status, memory map, and custom BASIC commands.
Build a voice-controlled AI assistant using an ESP32, Xiaozhi and MCP. This project focuses on safe hardware and software automation through the Model Context Protocol.
A demo for turning an ESP32-S3 microcontroller into a tiny, instant-on PC with a shell, editor, compiler, and app installer.
This Python code demonstrates a neural network application on a CircuitPython board, utilizing a camera (OV7670) for image capture, preprocessing, and inference using a digit classifier. It includes image conversion, auto-cropping, and normalization steps.
An Arduino-compliant library for ESP32 and related boards, designed for driving display devices and touch panels with graphical UI capabilities.
A better USB-to-serial adapter with a built-in screen. It supports baud rates from 1200 to 2 Mbit, all the while showing critical line status and traffic on its tiny yet full-featured monitor.
This article details how to train an image classification model on an ESP32 using both the SenseCraft AI platform and a custom TensorFlow Lite implementation. It covers setting up binary classification, training the model, and deploying it on ESP32-S3 devices.
Our goal at OpenMV is to make building machine vision applications on high-performance, low-power microcontrollers easy. We've done the hard work designing professional hardware and writing reliable, high-performance software for you, leaving more time for your creativity.
