Autonomously Driving Vehicle

Overview

This repository contains the ROS2 workspace and associated projects developed as part of the "Robotik" module during the Summer Semester 2024. The primary objective is to design and implement a 1:10 scale autonomous vehicle capable of navigating unfamiliar tracks using a combination of cameras and various sensors. The system integrates multiple Arduino boards and a Raspberry Pi 4 as the main control unit, with inter-component communication facilitated through ROS2 on an Ubuntu-based operating system.

Key Objectives

1. Hardware Integration

   • Utilize a Raspberry Pi 4 as the central processing unit.
   • Incorporate multiple Arduino boards for sensor data acquisition and actuator control.
   • Equip the vehicle with cameras and various sensors for environmental perception.

2. Software Development

   • Develop ROS2 nodes for sensor data processing, decision-making, and control algorithms.
   • Implement communication protocols between the Raspberry Pi and Arduino boards using ROS2 publisher and subscriber.

3. Autonomous Navigation

   • Implement algorithms for path planning and obstacle avoidance.
   • Develop computer vision techniques for track detection and following.

4. System Integration and Testing

   • Integrate hardware and software components into a cohesive system.
   • Test and validate the autonomous vehicle on various track configurations to assess performance and reliability.

Core Features

1. Sensor Fusion

   • Combine data from multiple sensors to create an accurate representation of the vehicle's environment.

2. Real-Time Processing

   • Process sensor data and execute control algorithms in real-time to enable responsive navigation.

3. Modular ROS2 Architecture

   • Utilize a modular design with ROS2 nodes to ensure scalability and ease of maintenance.

4. Camera-Based Track Detection

   • Implement computer vision algorithms to detect and follow the track using camera input.

5. Obstacle Detection and Avoidance

   • Develop mechanisms to detect obstacles and adjust the vehicle's path accordingly.

Technologies Used

• Programming Languages: Python, C++
• Frameworks: ROS2 (Robot Operating System 2)
• Hardware: Raspberry Pi 4, Arduino boards, cameras, various sensors
• Operating System: Ubuntu
• Version Control: Git & GitHub

Getting Started

Prerequisites

• Raspberry Pi 4 with Ubuntu installed
• Arduino boards
• ROS2 Foxy Fitzroy installed on the Raspberry Pi
• Cameras and sensors as specified in the hardware setup

Installation

1. Clone the repository to your Raspberry Pi:

   git clone https://github.com/aakiev/Robotics-AutonomousDriving.git

2. Navigate to the workspace directory:

   cd WS_AlMa

3. Build the ROS2 workspace:

   colcon build

4. Source the setup file:

   source install/setup.bash

5. Upload the Arduino code to the respective boards using the Arduino IDE.

Usage

1. Launch ROS2 Nodes:

   • Start the necessary ROS2 nodes for sensor data processing and control.

2. Initialize the Vehicle:

   • Ensure all hardware components are properly connected and powered.

3. Start Autonomous Mode:

   • Execute the main launch file to begin autonomous navigation.

4. Monitor Performance:

   • Use ROS2 tools to monitor topics and node statuses during operation.

Future Enhancements

• Implement advanced path planning algorithms for more complex track layouts.
• Integrate additional sensors for improved environmental perception.
• Enhance computer vision capabilities for better track detection under varying lighting conditions.
• Optimize system performance for faster processing and response times.