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:

   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

Schematic Diagram

Wiring Diagram