ROS Navigation – Navigation

ROS Navigation Self-Study Course

Course Syllabus

Course Description

This comprehensive 4-month self-study course, “ROS Navigation: Guiding Your Robot Autonomously,” is meticulously designed for motivated beginners and intermediate learners eager to master the art and science of robot autonomous navigation using the Robot Operating System (ROS). Through a blend of theoretical foundations, practical examples, and hands-on exercises, participants will delve into the core concepts of mapping, localization, path planning, and obstacle avoidance within the ROS framework. By the end of this course, you will possess the skills and confidence to implement robust navigation solutions for a wide range of mobile robotic platforms.

Primary Learning Objectives

Upon successful completion of this course, learners will be able to:

• Understand the fundamental principles of robot navigation, including mapping, localization, and path planning.
• Configure and launch the ROS Navigation Stack on a simulated and/or physical robot.
• Create and utilize 2D occupancy grid maps for navigation.
• Implement various localization techniques, including AMCL (Adaptive Monte Carlo Localization).
• Configure and tune global and local planners for efficient and safe robot movement.
• Integrate sensors (e.g., LiDAR, depth cameras) for perception and obstacle avoidance.
• Troubleshoot common issues encountered during ROS navigation setup and operation.
• Develop and execute a complete autonomous navigation solution for a mobile robot.

Necessary Materials

• A computer with Ubuntu 18.04 (or later LTS version) installed.
• ROS Melodic or Noetic installed (consistent with Ubuntu version).
• Gazebo simulator installed.
• ROS packages for navigation (e.g., navigation, slam_gmapping, amcl, move_base).
• Optional but highly recommended: A mobile robot platform (e.g., TurtleBot3, Jackal) for real-world application of concepts.
• Access to internet for additional resources and documentation.

Course Content: 14 Weekly Lessons

Week 1: Introduction to Robot Navigation and ROS Fundamentals (Lesson 1)

Lesson Title: Setting the Stage: What is Robot Navigation and Why ROS?

Learning Objectives:

• Define robot navigation and its key components.
• Understand the role of ROS in robotic systems, particularly for navigation.
• Install and set up the necessary ROS environment for navigation.

Key Vocabulary:

• Robot Navigation: The ability of a robot to move autonomously from a starting point to a destination while avoiding obstacles.
• ROS (Robot Operating System): A flexible framework for writing robot software. It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms.
• ROS Master: The “heart” of ROS, responsible for name registration and lookup for all nodes.
• ROS Node: An executable process that performs computation.
• ROS Topic: A named bus over which nodes exchange messages.
• ROS Message: A data structure for passing information between nodes.

Lesson Content:

Robot navigation is a fascinating and crucial aspect of robotics, enabling robots to move intelligently and independently through their environments. Imagine an autonomous vacuum cleaner, a self-driving car, or a drone delivering packages – all rely heavily on sophisticated navigation capabilities. At its core, robot navigation involves answering three fundamental questions: “Where am I?”, “Where am I going?”, and “How do I get there?”. These correspond to localization, goal setting, and path planning, respectively.

ROS provides a powerful and modular framework that significantly simplifies the development of complex robotic applications, including navigation. Its distributed architecture allows different functionalities (like sensor processing, mapping, and motion control) to be developed and run as independent nodes that communicate seamlessly. This modularity makes ROS highly scalable and adaptable to various robot platforms and navigation challenges.

To begin our journey, ensure you have a fresh installation of Ubuntu (preferably 18.04 or 20.04 LTS for ROS Melodic or Noetic, respectively). ROS installation involves setting up the ROS repositories, installing core packages, and configuring your environment variables. It’s a critical first step to ensure all subsequent lessons run smoothly. We’ll specifically be focusing on the ros-melodic-desktop-full or ros-noetic-desktop-full installation, as it includes all the necessary tools and libraries for robotics development.

Practical Hands-on Examples:

1. Install ROS (Melodic or Noetic) on your Ubuntu system following the official ROS installation guides.
2. Verify your ROS installation by running basic ROS commands like roscore and rosnode list.
3. Create a simple ROS workspace and a basic ROS package.
4. Write a simple “hello world” ROS node in Python or C++ that publishes a custom message to a topic and another node that subscribes to it, demonstrating basic ROS communication.

Week 2: Representing the World: Maps and Occupancy Grids (Lesson 2)

Lesson Title: Building a Digital World: Understanding Occupancy Grid Maps

Learning Objectives:

• Explain the concept of an occupancy grid map and its importance in robot navigation.
• Understand the different states of cells in an occupancy grid.
• Learn how to visualize occupancy grid maps in RViz.

Key Vocabulary:

• Occupancy Grid Map: A probabilistic representation of the environment, dividing space into a grid of cells, each representing the probability of being occupied by an obstacle.
• Free Space: Cells with a low probability of containing an obstacle.
• Occupied Space: Cells with a high probability of containing an obstacle.
• Unknown Space: Cells whose occupancy state is undetermined due to lack of sensor information.
• Resolution: The size of each cell in the occupancy grid, determining the granularity of the map.
• RViz: A 3D visualizer for ROS, used to display sensor data, robot models, and maps.

Lesson Content:

For a robot to navigate effectively, it needs a representation of its surroundings. Occupancy grid maps are a widely used and intuitive method for this purpose. Imagine dividing the world into a grid of tiny squares or “cells.” Each cell then stores information about whether it’s free, occupied by an obstacle, or unknown. This probabilistic representation allows the robot to handle sensor noise and uncertainties in its environment.

The values within each cell typically range from 0 to 1, where 0 represents completely free space, 1 represents completely occupied space, and 0.5 often indicates unknown. Sensors like LiDARs and depth cameras provide the raw data used to update these cell probabilities. As the robot moves and collects more sensor data, the map becomes more accurate and detailed, allowing for better navigation decisions.

Visualizing these maps is crucial for understanding how the robot perceives its world. RViz, the ROS visualization tool, allows us to overlay the occupancy grid on a 3D environment, helping us to debug mapping processes and observe the robot’s understanding of its surroundings in real-time.

Practical Hands-on Examples:

1. Launch a simple Gazebo simulation with a mobile robot and a static environment.
2. Use the map_server package to load a pre-existing occupancy grid map into ROS.
3. Visualize the loaded map in RViz, exploring its properties and how different occupancy values are represented.
4. Experiment with changing the map’s resolution and observe the effect on the visualization.

Week 3: Simultaneous Localization and Mapping (SLAM) – GMapping (Lesson 3)

Lesson Title: Building Maps on the Fly: Introduction to GMapping SLAM

Learning Objectives:

• Understand the concept of Simultaneous Localization and Mapping (SLAM).
• Learn the basic principles behind the GMapping algorithm.
• Configure and run GMapping to build a map in a simulated environment.

Key Vocabulary:

• SLAM (Simultaneous Localization and Mapping): The computational problem of concurrently building a map of an unknown environment and, at the same time, localizing the robot within that map.
• Localization: Determining the robot’s pose (position and orientation) within a known map.
• GMapping: A popular ROS package that implements a Rao-Blackwellized particle filter for 2D SLAM.
• Particle Filter: A sequential Monte Carlo method used for estimating the state of a dynamic system given partial and noisy observations.
• Odometry: The process of using data from motion sensors to estimate changes in a robot’s position over time.

Lesson Content:

Imagine