Autonomous Tracking Trolley

Overview

The Autonomous Tracking Trolley is an intelligent, user-friendly solution designed to assist elderly individuals or those needing support in carrying luggage. This trolley autonomously follows its user by analyzing Wi-Fi signal strength and features advanced functionalities such as obstacle avoidance, face recognition for security, and location monitoring via an online server.

Features

1. User Tracking via Wi-Fi Signal Strength

   • Measures Wi-Fi signal strength received from multiple access points.
   • Utilizes an SVM machine learning model to classify the user's region and the trolley's region.
   • Employs a mapping algorithm to navigate the trolley to the user.

2. Mapping Feature

   • Users can send the trolley to a specific location via the mapping system.

3. Security Feature

   • Locker access is secured using Face ID recognition via a camera.

4. Online Monitoring via Firebase

   • Tracks the battery charge percentage and requests recharging when necessary.
   • Monitors the trolley's real-time location using a GPS sensor.

5. Obstacle Avoidance

   • Ultrasonic sensors detect obstacles to ensure safe navigation.

System Architecture

Microcontrollers

• Arduino Uno: Controls ultrasonic sensors and handles obstacle avoidance and communicates with the ATmega32 via I2C protocol.
• ATmega32: Controls the 4 DC motors through H-bridges.
• ESP32: One ESP32 gathers Wi-Fi signal strength and applies the mapping algorithm. Another ESP32 monitors the battery and location, sending data to the online server.

Communication Protocols

• I2C: Used between Arduino Uno and ATmega32 for obstacle-related commands.
• UART: Used between ESP32 and ATmega32 for mapping-related commands.
• ESP-NOW: Enables communication between the user and the trolley.

Motor Control

• 4 DC motors and 4 wheels are controlled by 2 H-bridges for movement and navigation.

Additional Components

• Ultrasonic sensors for obstacle detection.
• Camera for face recognition.
• GPS sensor for location tracking.

System Workflow

1. Tracking the User

   • Measure Wi-Fi signal strength and classify regions using SVM.
   • Use mapping algorithms to navigate the trolley.

2. Mapping and Navigation

   • The user can send commands to the trolley for specific destinations.

3. Security and Monitoring

   • Face ID recognition for locker security.
   • Firebase server for monitoring battery status and location.

4. Obstacle Avoidance

   • Ultrasonic sensors trigger immediate stops via external interrupt and rerouting when obstacles are detected.

Technologies Used

• Hardware: Arduino Uno, ATmega32, ESP32, Ultrasonic sensors, GPS sensor, Camera, DC Motors, H-bridges.
• Machine Learning: SVM for region classification.
• Cloud Services: Firebase for battery and location monitoring.
• Communication: I2C, UART, ESP-NOW.

Prototype