AutoNav-ROS

Autonomous Navigation ROS Platform

📚 Course Project: ECG711 - Embedded Systems for Automation

An autonomous mobile robot platform built with ROS (Robot Operating System), featuring differential drive navigation, SLAM mapping, PID control, and real-world hardware implementation on Jetson Nano.

Yahboom ROSMASTER X1 Robot with RPLidar A3 and Jetson Nano

Author

Tarek Z YouTube Playlist: ECG 711 Course Videos

🎯 Project Highlights

This repository demonstrates practical applications of embedded systems concepts in robotics, including:

• ROS Architecture - Multi-node communication and topic-based messaging
• Differential Drive Kinematics - Mathematical modeling and simulation
• PID Control Systems - Autonomous goal-seeking navigation
• 3D Robot Modeling - URDF/XACRO for Gazebo and RViz
• Sensor Fusion - IMU and LiDAR integration
• SLAM Implementation - Real-time mapping with gmapping/Hector SLAM
• Embedded Hardware - Jetson Nano + STM32 motor control

📸 Visual Showcase

RViz Robot Model Visualization URDF modeling with sensor integration	PID Controller Navigation Autonomous goal-seeking behavior
Real-time SLAM Mapping Gmapping with RPLidar A3	Environment Map Output Occupancy grid from SLAM

🏗️ System Architecture

graph TD
    A[Driver Node] -->|cmd_vel| B[Robot Base Controller]
    C[Goal Position] -->|target_pose| D[PID Controller Node]
    D -->|cmd_vel| B
    B -->|wheel_velocities| E[Differential Drive Kinematics]
    E -->|pose| F[Localization]
    G[RPLidar A3] -->|scan| H[SLAM Node<br/>gmapping/hector]
    I[IMU Sensor] -->|imu_data| F
    H -->|map| J[Navigation Stack]
    F -->|odom| H
    J -->|visualization| K[RViz]
    H -->|map| K

    style A fill:#4CAF50
    style D fill:#2196F3
    style H fill:#FF9800
    style B fill:#9C27B0
    style K fill:#F44336

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🛠️ Technologies Used

Category	Technology
Framework	ROS Melodic
Programming	Python 3
Simulation	Gazebo, RViz
Operating System	Ubuntu 18.04
Computing Platform	NVIDIA Jetson Nano
Microcontroller	STM32 (Motor Control)
Sensors	RPLidar A3, IMU
Algorithms	Gmapping SLAM, Hector SLAM, PID Control

📚 Project Modules

🎮 Assignment 1: Differential Drive Simulation

Built the foundation for robot control with a driver-simulator architecture.

Implementation:

• Driver Node: Publishes differential wheel velocity commands via cmd_vel topic
• Simulator Node: Real-time pose calculation using differential drive kinematics

Skills: ROS topics, publishers/subscribers, differential kinematics, pose estimation

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🎯 Assignment 2: PID Controller for Goal Navigation

Developed intelligent autonomous navigation using closed-loop PID control.

Implementation:

• Driver Node: Publishes target destinations as Pose2D messages
• Controller Node: PID-based trajectory planning with error minimization

Skills: PID tuning, feedback control systems, autonomous navigation algorithms

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🤖 Assignment 3: URDF Modeling and RViz Visualization

Created accurate 3D robot models for simulation and visualization.

Implementation:

• URDF/XACRO robot description with sensor integration
• RPLidar A3 sensor model and mounting configuration
• Gazebo physics simulation and RViz real-time visualization

Skills: Robot modeling (URDF/XACRO), sensor integration, simulation environments

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⚙️ Assignment 4: Physical Robot Setup and Calibration

Deployed the complete system on real hardware with full sensor integration.

Implementation:

• Hardware assembly: Jetson Nano + STM32 + motors + LiDAR + power system
• Ubuntu 18.04 and ROS Melodic installation on embedded platform
• Multi-stage calibration: IMU, linear velocity, angular velocity
• Real-world SLAM mapping using gmapping algorithm

Skills: Embedded Linux, hardware-software integration, sensor calibration, production robotics

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✨ Key Features

• ✅ Multi-Node ROS Architecture - Distributed system design with topic-based communication
• ✅ Differential Drive Control - Mathematical modeling and real-time kinematics
• ✅ PID-based Navigation - Autonomous goal-seeking with tuned controllers
• ✅ SLAM Mapping - Real-time environment mapping using LiDAR
• ✅ Sensor Fusion - IMU and odometry integration for accurate localization
• ✅ Hardware Integration - Complete embedded system deployment on Jetson Nano
• ✅ Simulation & Visualization - Gazebo physics simulation and RViz 3D visualization
• ✅ Production-Ready Calibration - Systematic sensor and actuator tuning procedures

Getting Started

Prerequisites

• Ubuntu 18.04 (or compatible)
• ROS Melodic
• Python 3
• Catkin build system

Basic Setup

1. Clone this repository:

   git clone https://github.com/tarekbzahid/ECG711-Embedded-Systems-for-Automation.git
   cd ECG711-Embedded-Systems-for-Automation

2. Navigate to the specific assignment directory:

   cd "Assignment 1"  # or Assignment 2, 3, 4

3. Follow the instructions in each assignment's README for detailed setup and execution steps.

Building a Catkin Workspace (General)

mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/
catkin_make
source devel/setup.bash

Project Structure

ECG711-Embedded-Systems-for-Automation/
├── Assignment 1/          # Differential drive simulation
│   ├── catkin_ws/
│   ├── images/
│   └── README.md
├── Assignment 2/          # PID controller navigation
│   ├── catkin_ws/
│   ├── images/
│   └── README.md
├── Assignment 3/          # URDF modeling and visualization
│   ├── catkin_ws/
│   ├── images/
│   └── README.md
├── Assignment 4/          # Physical robot setup
│   ├── images/
│   └── README.md
└── README.md              # This file

Resources

• Course Videos: YouTube Playlist
• ROS Documentation: http://wiki.ros.org/
• Yahboom ROS Controller: http://www.yahboom.net/study/ROSMASTER-X1

📄 License

This project was developed as part of ECG711 (Embedded Systems for Automation) coursework and is intended for educational purposes.

Acknowledgments

• Course instructor and teaching assistants
• Yahboom for robot hardware and documentation
• ROS community for excellent documentation and packages