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Smart Cane Project

This project integrates multiple sensors and peripherals with a Raspberry Pi to collect data, capture images, and provide feedback through a buzzer. The setup includes GPS, LiDAR, a camera, a temperature sensor, and a buzzer.

Features

  • GPS Module (GY-GPS6MV2, NEO-6M): Reads GPS coordinates in real-time.
  • LiDAR (RPLIDAR A1/A2): Collects distance and angle data from the surroundings.
  • Camera (Raspberry Pi Camera): Captures images and saves them locally.
  • Buzzer: Provides audible feedback.
  • Temperature Sensor (MLX90614): Measures ambient and object temperature.
  • Object Detection (YOLOv5): Detects objects in the captured images.

Requirements

  • Raspberry Pi (with GPIO support)

  • Python 3

  • Installed libraries:

    • serial
    • RPi.GPIO
    • cv2 (OpenCV)
    • rplidar-roboticia
    • pynmea2
    • torch (for YOLOv5)
    • requests
    • dotenv
    • adafruit-mlx90614
    • geocoder

  • Hardware:

    • GPS Module (GY-GPS6MV2, NEO-6M)
    • RPLIDAR A1/A2
    • Raspberry Pi Camera
    • 2-pin Buzzer
    • MLX90614 Temperature Sensor

Setup

  1. Connect the hardware:

    • Connect the GPS module to the Raspberry Pi's UART pins.
    • Connect the LiDAR to a USB port.
    • Connect the camera to the Raspberry Pi's camera interface.
    • Connect the buzzer to GPIO pin 17 (or modify the pin in the code).
    • Connect the MLX90614 temperature sensor to the I2C pins of the Raspberry Pi.

  2. Install dependencies:

    # Install libraries
pip install pyserial rplidar-roboticia requests boto3 python-dotenv geocoder adafruit-circuitpython-mlx90614

# Clone the YOLOv5 repository
git clone https://github.com/ultralytics/yolov5

# Navigate to the cloned directory
cd yolov5

# Install required packages
pip install -r requirements.txt

  3. Enable camera support:

    • Run sudo raspi-config and enable the camera interface.

  4. Create a .env file for storing AWS credentials and other environment variables:

    PORT=6001
DBURI=<your-mongodb-uri>
JWT_SECRET=<your-jwt-secret>

BREVO_API_KEY=<your brevo-api-key>
FRONTEND_URL=<your frontend-url>

AWS_ACCESS_KEY_ID=<your-aws-access-key-id>
AWS_SECRET_ACCESS_KEY=<your-aws-secret-access-key>
AWS_REGION=<your-aws-region>
S3_BUCKET_NAME=<your-s3-bucket-name>

Usage

  1. Clone or copy the project to your Raspberry Pi.

  2. Run the script:

    python3 main.py

  3. The script will:

    • Continuously read GPS coordinates and print them.
    • Collect LiDAR data and print the points.
    • Capture an image and save it with a timestamped filename.
    • Measure the ambient and object temperature using the MLX90614 sensor.
    • Beep the buzzer for feedback.
    • Log the location, path, and temperature data in MongoDB.

Notes

  • Ensure the GPS module has a clear view of the sky for accurate readings.
  • The LiDAR should be placed in an open area for effective scanning.
  • Modify the GPIO pin for the buzzer if needed.
  • The MLX90614 temperature sensor needs to be connected to the I2C pins. Ensure I2C is enabled on the Raspberry Pi.
  • Make sure to configure your credentials properly in the .env file.

Cleanup

The script includes a cleanup routine to:

  • Close the GPS serial connection.
  • Stop and disconnect the LiDAR.
  • Release the camera.
  • Reset GPIO pins.

Block Diagram

block_diagram

Schematic Diagram

schematic_diagram

Wiring Diagram

wiring_diagram