Robotics ROS

ROS

Learning Objectives

What is ROS?

• Philosophy
• Features
• ROS Wiki
• Structure

Robotic Operating System

-> Open Source Set of Libraries Let us Develop and Manage A Modular Framework

Philosophy:

The development of a new robotic system relies on:

• Modularity: using ready modules (sensors, actuators, etc.) instead of making everything from scratch.
• Distributed computation: each module (software or hardware) may need an independent computational resource.
• Robustness and Reliability: it is necessary to ensure all the modules work together consistently regardless of uncertainties or disturbances.
• Scalability: adding new features, expanding the capability domain, and even making new products based on the current design led us to consider scalability in the development process.

Features:

ROS:

• Tools Based
• Multi-Lingual
• Community Base
• Open Source Repositories
• Peer-Peer Connection

Tools

• Message Passing
• Simulation
• Real-Time Task Scheduling
• Data Logging

ROS Document(Wiki)

ROS Wiki: https://wiki.ros.org/Documentation ROS Robots: https://robots.ros.org/ ROS2 Documents: https://docs.ros.org/en/foxy/index.html

ROS Main Concepts:

Node

• Single-purposed executable programs
• Independently worked and managed
• They are written using a ROS library

Message

• Data structure for communication between nodes

Topics

• A customised message dedicated to transferer data on the network
• Nodes can subscribe/publish all the Topics on the network

Service

• Synchronous inter node transactions

• (blocking RPC): ask for something and wait for it

Action

• standardized interface for interfacing with non-interrupting tasks

Parameter Server

• A shared dictionary that is accessible via network

• Best used for static data such as configuration parameters

Master

• Provides connection information to nodes so that they can transmit messages to each other

Packages

• Software in ROS is organized into packages
• A package contains one or more nodes, documentation, messages, services, …

ROS2 Ecosystem:

• Visualisation Tools (RVIZ)
• Simulation Tools (GAZEBO)
• Available Cross-Platform libraries and community support

ROS Applications in robotics:

• Algorithms: autonomous navigation, manipulation, and swarm robotics.

• Real-world use cases: delivery robots, drones, and healthcare robots

• Advanced use cases in real-time systems (ROS2)
• Industrial applications: self-driving cars, precision agriculture, and collaborative robots

ROS2 and its advantages:

• Decentralised Architecture
• Real-TimeSupport
• Data Distribution Service
• Cross-PlatformCompatibility
• Enhanced Security
• Modular and Scalable Design
• Improved Tooling
• Support for Multi-Domain Applications

ROS/ROS2 and AI Integration:

➢ Tools and frameworks for AI integration into robotic systems for tasks like perception, decision-making, and learning

• Perception and Computer Vision
• Navigation and Decision-Making
• Machine Learning in Robotics
• AI-Powered Data Processing
• Simulation for AI Training
• real-time AI processing for embedded processors
• supports NLP frameworks such as Google Dialogflow

Summary

• Introduction to ROS
• Main ROS concept
• ROS2 features and advantages over ROS