Autonomous Car Robot with Obstacle Avoidance and Infrared Detection (KAYO TEAM)

Overview

This project involves designing, building, and programming a robot capable of both remote-controlled operation and autonomous navigation. The robot Detection lane while avoiding obstacles using Arduino and Raspberry Pi. Part of Kuwait University, Robotics class with Dr. Alshaibani.

Project Phases:

1. Phase 1: Assembly and remote operation.
2. Phase 2: Autonomous driving with obstacle avoidance.

Key Features

• Remote Control: Manual operation via a web interface (Flask server on Raspberry Pi).
• Autonomous Navigation: Utilizes Q-learning for path-following and obstacle avoidance.
• Sensor Integration:
  • 3x IR Sensors for line detection.
  • 3x Ultrasonic Sensors for obstacle detection.
• Motor Control: L298N motor driver with 4 DC motors.
• Communication: UART serial between Raspberry Pi (main controller) and Arduino (motor/sensor handler).

Components

Hardware

• Raspberry Pi 4 (Main controller, runs Flask server).
• Arduino Uno (Handles motor control & sensor readings).
• L298N Motor Driver (Controls DC motors).
• 4x DC Motors with Wheels (Movement).
• 3x IR Infrared Sensors (Line detection).
• 3x Ultrasonic Sensors (HCSR04) (Obstacle detection).
• Power Bank & Li-ion Battery (Power supply).

Software & Tools

• Python (Flask server, Q-learning algorithm).
• Arduino IDE (Motor/sensor control logic).
• GitHub (Version control).

Phase 1: Remote Control

Assembly & Operation

• Flask web server allows manual control (forward, backward, left, right).
• Commands sent via UART to Arduino, which drives motors.

Phase 2: Autonomous Mode

Q-Learning Algorithm

• States: IR sensor readings (e.g., 000 = all white, 010 = middle black).
• Actions: Forward, left, right, scan_left, scan_right, strongLeft, strongRight.
• Rewards: Predefined based on sensor inputs (e.g., staying centered = high reward).
• Training: Robot learns optimal path-following via reinforcement learning.

Obstacle Avoidance

• Ultrasonic sensors detect obstacles (threshold: 20cm).
• If an obstacle is detected, the robot switches lanes and signals with LEDs.

Setup & Usage

Setup Guide

Hardware Assembly

1. Connect motors, sensors, and controllers as per the wiring diagram.
2. Power Raspberry Pi (USB-C) and Arduino (Li-ion battery).

Software Setup

1. Upload Arduino code to handle sensors/motors.
2. Run Flask server on Raspberry Pi (app.py) for web interface.

Training Autonomous Mode

1. Start training via the web interface.
2. Robot explores and updates Q-table (model.pth).

Demo

Robot Demo

• Place robot on a track with lanes and obstacles.
• Switch between manual and autonomous modes.

Challenges & Improvements

Challenges

• Sensor calibration for varying light conditions.
• Motor synchronization issues.
• Q-learning convergence time.

Future Work

• Fix Issues with Q-learning
• Add a camera for advanced object recognition.

Contributors

• Hala Almutairi
• Zaharaa Alrashidi