Garage133

Garage133 is a DIY garage door automation and monitoring system based on the ESP32. It provides capability to remotely control up to two garage doors, monitor their state (open/closed), detect vehicle presence, measure environmental conditions (temperature/humidity), and detect motion within the garage.

The system is designed to integrate seamlessly with Home Assistant via MQTT but also provides a modern, responsive Svelte-based Web Interface for control and configuration.

What's New in v1.0.3

• Modernized API: Full transition to camelCase JSON keys for all status and configuration endpoints, enabling simpler and more robust integration with the Svelte frontend.
• og3 v0.6.0 Core: Leveraging the latest framework improvements, including declarative require() dependencies and optimized memory management.
• Automatic Build Pipeline: Frontend assets are now automatically rebuilt during the PlatformIO compilation process.
• Coding and infrastructure cleanups: Linting, CI, coding improvements.

Features

• Dual Door Control: Independent control for two garage doors using relays.
• HMM State Estimation: Uses Hidden Markov Models (HMM) and sonar sensors to robustly determine door and car states:
  • Open (Door is rolled up).
  • Closed with Car (Door is down, vehicle detected).
  • Closed (Empty) (Door is down, bay is empty).
• Modern Web Dashboard: A responsive Svelte SPA to:
  • Monitor real-time sensor data and door status.
  • Trigger door relays remotely.
  • Visualize HMM detection probabilities.
  • Manually correct/label states to refine detection.
  • Upload and reload HMM JSON models dynamically.
• Environmental Monitoring: SHTC3 sensor for Temperature and Humidity.
• Motion Detection: PIR sensor integration.
• Light Monitoring: Analog light sensor to detect garage lighting levels.
• Home Assistant: Native MQTT Discovery support for easy integration.
• OLED Display: Shows status, IP address, and sensor readings locally.
• OTA Updates: Support for reliable Over-The-Air firmware updates using expanded flash partitions.

Hardware

The project is built around an ESP32 (Node32s / ESP32 Dev Module).

Pinout Configuration

Component	Pin (ESP32)	Description
Relay (Left)	GPIO 15	Control for Left Garage Door
Relay (Right)	GPIO 2	Control for Right Garage Door
Sonar Trigger (Left)	GPIO 16	HC-SR04 Trigger
Sonar Echo (Left)	GPIO 17	HC-SR04 Echo
Sonar Trigger (Right)	GPIO 5	HC-SR04 Trigger
Sonar Echo (Right)	GPIO 18	HC-SR04 Echo
PIR Sensor	GPIO 25	Motion Detection
Light Sensor	GPIO 33	Analog Light Level
I2C SDA	GPIO 21	SHTC3 & OLED Display
I2C SCL	GPIO 22	SHTC3 & OLED Display

Note: Pin definitions can be found in src/main.cpp.

Fabrication

This repository includes files for fabricating the custom PCB and 3D printed case:

• KiCAD: Circuit board designs are located in the KiCAD/ directory.
• 3D Parts: OpenSCAD and STL files for the enclosure and sensor mounts are in the scad/ directory.

Getting Started

Prerequisites

• PlatformIO (VSCode Extension or CLI)
• Node.js & npm (For building the Web Interface)
• Git

Installation

1. Clone the repository:

   git clone https://github.com/chl33/Garage133.git
   cd Garage133

2. Build the Web Interface: Generate the C++ header containing the Svelte assets:

   ./build-svelte.sh

3. Configuration: Copy the example secrets file and configure your environment settings.

   cp secrets.ini.example secrets.ini

   Edit secrets.ini to set your upload port, UDP log target, and OTA password.

• *Note: WiFi and MQTT credentials can be configured via the captive portal on
• first boot, when you attach to the board's WiFi network named 'garage133.' They can be configured via the web dashboard therafter.*

4. Build and Flash: Connect your ESP32 via USB for the first flash (to apply the min_spiffs partition table).

   pio run -e usb_node32s --target upload

Usage

Web Interface

Once connected to WiFi, navigate to the device's IP address in your browser.

• Overview: Real-time status of both doors and environment.
• Door Details: Click the Gear icon on a door card to see HMM probabilities, manually label the current state, or upload a new model.
• Settings: Configure WiFi, MQTT, and restart the device.

Home Assistant

Ensure your Home Assistant instance has an MQTT broker configured. The device uses Home Assistant MQTT Discovery. Once the device connects to your MQTT broker, the following entities should automatically appear:

• Covers: left_door, right_door (Open/Close control)
• Sensors: temperature, humidity, light
• Binary Sensors: car (presence for each bay), motion

HMM Analysis Toolkit

The analysis/ directory contains Python tools to help refine your detection models:

• Download historical sonar data from InfluxDB, if you have this setup.
• Train HMM models using the Viterbi algorithm.
• Evaluate and export models to JSON for upload to the device.

Blog Post

For a more detailed write-up on the background and design of this project, please see the blog post.

License

This project is licensed under the MIT License - see the LICENSE file for details.