Introduction to ROS

We are building a teleoperated lab based on the IPCbots of the bavarian company EduArt GmbH to teach ROS, the Robot Operating System, to students. 

ROS is increasingly recognized as an industry standard in robotics, particularly in areas such as autonomous vehicles, logistics robots, and service robots. Learning ROS enhances employment opportunities and opens up numerous career options. Here are some of the main reasons why ROS has become indispensable in robotics:  
a. Standardization and Modularity:  
ROS provides a standardized framework for building and integrating complex robotic systems. It promotes code reuse and modularity, allowing developers to focus on specific components without reinventing the wheel. With its package-based architecture, ROS facilitates interoperability between various robotic components and systems, making information and command exchange simpler and more efficient.  
b. Community and Ecosystem:  
ROS has a large and active community that contributes to its growth and evolution. This community offers extensive support, tutorials, forums, and a wealth of shared knowledge. The ROS ecosystem includes a wide range of packages and tools for different robotic tasks such as navigation, perception, simulation, and manipulation, enabling developers to leverage existing solutions.  
c. Robust Development Tools:  
ROS supports popular robotics simulators like Gazebo, allowing developers to test and validate algorithms in simulated environments before deploying them on real hardware. This reduces both development time and risks. Tools like RViz and rqt offer powerful visualization capabilities that are helpful in debugging and monitoring robot states and sensor data.  
d. Spatial Perception and Navigation:  
The ROS navigation stack provides a suite of tools and algorithms for mobile robot navigation, including mapping, path planning, and obstacle avoidance. This is critical for autonomous mobile robots operating in dynamic environments. ROS supports the integration of a wide variety of sensors essential for environment perception, performing SLAM (Simultaneous Localization and Mapping), and generating reactive behaviors.  
e. Scalability and Flexibility:  
ROS supports a distributed architecture, meaning that components can run on different machines or processors. This scalability is important for deploying solutions in larger systems or distributed networks of robots. ROS is flexible enough to be used in a wide range of robot types (e.g., ground vehicles, drones, manipulators) and in projects ranging from simple research initiatives to complex industrial applications.  
f. Data Distribution Service:  
The Data Distribution Service (DDS), an industry standard for network communication, forms the underlying middleware communication layer in ROS 2, enabling data exchange between nodes. DDS follows a publish-subscribe model, where data producers (publishers) and consumers (subscribers) can communicate without knowing each other directly. It offers reliable, real-time, and scalable communication by handling complex tasks such as message delivery, node discovery, quality of service (QoS) settings, and data serialization. This flexibility makes DDS ideal for distributed robotic systems running over networks where consistency, latency, and data prioritization need precise control. ROS allows for the use of different DDS implementations according to the required specifications of a robotic application.  
g. Research and Innovation:  
ROS is frequently used in both academic and industrial research, making it a valuable platform for experimenting with advanced algorithms and new technologies in mobile robotics.  
Thus, learning ROS not only provides robotics students with technical know-how but also opens up a wide range of applications in research, development, and industry.