🔥 Fire-Bot: FOMO-vision model based autonomous robot for fire detection and suppression

This project presents the design and implementation of Fire-Bot, an image processing-based autonomous firefighting robot. The system addresses the complex challenge of real-time fire detection and response using a low-powered microcontroller, onboard stereoscopic cameras, PID-controlled motion, and integrated IMU feedback for precise mobility. The solution brings together machine learning, control systems, real-time image processing, and robotics in a single cohesive system.

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🚀 Project Highlights

• Real-Time Fire Detection using an optimized ML model deployed on a 248kB microcontroller.
• Autonomous Navigation with discrete-time PID control coded from scratch.
• Stereoscopic Distance Estimation using dual ESP32-CAMs with custom frame synchronization.
• IMU Feedback Integration using MPU6050 DMP for orientation correction.
• Custom Communication Protocol for transmitting image data with structured identifiers (camera ID + frame number).
• Efficient deployment of FOMO (Fast Object Detection Model) on edge hardware.

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📂 Repository Structure

Fire-Bot/
├── report/
│   └── Fire-Bot_Project_Report.pdf  # Contains detailed documentation, methodology, and experimental analysis
└── source code/
    ├── Camera/
    │   ├── esp32_camera_one/
    │   │   └── camera_one_code.ino  # ESP32-CAM node 1 with object detection
    │   ├── esp32_camera_two/
    │   │   └── camera_two_code.ino  # ESP32-CAM node 2 with stereo calibration
    │   └── src/
    │       └── fire_detection_fomo.tflite  # Optimized TFLite ML model for fire detection
    ├── Rover/
    │   ├── Rover.ino                # Main rover code: diff drive + stereo camera manager + IMU PID feedback
    │   └── pins_me.h                # Pin mapping and hardware definitions

🧠 Machine Learning Model

• Model: FOMO (Fast Object Detection Model)
• Optimized and quantized for deployment on ESP32-CAM (approx. 248kB footprint)
• Detects fire/flame regions in real-time under diverse lighting conditions

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⚙️ Hardware Used

• 2 × ESP32-CAM modules for stereoscopic imaging
• MPU6050 with DMP support for orientation data
• Custom-built Differential Drive Rover with geared motors
• Power-efficient ESP-32 Dev microcontroller (no external GPU/CPU needed)

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🔄 Control System

• A discrete-time PID controller built from scratch
• Corrects for orientation drift using MPU6050 DMP data
• Ensures stable and accurate movement towards detected fire source

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🧪 Data Handling & Communication

• Custom image frame packet includes:
  • Camera ID
  • Frame Number
• Data transmitted over ESP-Now Data Transmission Protocol
• Stereo vision logic aligns frames from both cameras to estimate fire distance

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📚 How to Run

Steps

1. Flash ESP32-CAMs:

   • Upload esp32_camera_one to one ESP32-CAM
   • Upload esp32_camera_two to the second ESP32-CAM

2. Flash the Rover:

   • Upload Rover.ino to the rover's MCU
   • Ensure correct IMU pin mapping via pins_me.h

3. Connect Hardware:

   • Mount and align cameras for stereoscopic vision
   • Power up the system

4. Test & Debug:

   • Use Serial Monitor to view distance estimates and PID outputs
   • Tune PID gains as needed

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📄 Report

You can find the full project documentation including:

• System architecture
• ML model training & optimization
• PID tuning
• Stereo calibration
• Experimental results

In the report/Fire-Bot_Project_Report.pdf file.

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💡 Future Improvements

• Thermal camera integration
• SLAM-based navigation
• Wireless streaming of camera data

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🤝 Contributing

Feel free to open issues or contribute to the codebase!

git clone https://github.com/AbrarMahmud/Ai_FireBot.git