Introduction to Robot Operating System (ROS)

ROS is an open-source robotic middleware that has been widely adopted in research, education, and industrial applications worldwide. You might be wondering what ROS’s inclusion within robotics all means, and that is where this guide comes in handy. In this guide, we’ll walk you through the basics of robotics and teach you how to create your own ROS-enabled robots.

By the end of this guide, you will have a better understanding of what ROS is, the advantages of using ROS, how to set up a ROS workspace, how to write ROS programs, and the steps involved in building your own ROS robot.

It’s not all theory here. You will have the opportunity to delve into the practical applications of robotics. With the use of ROS-enabled robots, areas such as SLAM, navigation, and perception become more accessible and will feel like you’re watching science fiction become real.

Understanding the Robot Operating System (ROS)
Setting up the ROS Environment
ROS Communication and Messaging
Writing ROS Programs
ROS Application Development
Conclusion – The Future of Robotics with ROS

Understanding the Robot Operating System (ROS)

ROS has been around since 2007, developed at Willow Garage as a robotic middleware framework for building modular and distributed robotic systems. Since then, ROS has witnessed significant growth and is now widely used in the robotics industry, including academia, research, and industrial applications.

The architecture of ROS comprises of Nodes, Topics, Messages, Services, Actions, and Parameters. These components allow for seamless communication between different nodes in a ROS network. ROS provides robust communication and messaging services, enabling nodes in a robotics system to share data efficiently, regardless of their physical location.

One of the distinct advantages of ROS is its open-source nature. ROS has a vast and active community where anyone can contribute to the development of the software. This community has developed numerous packages ranging from simple tools to complex algorithms, allowing beginners to progressive users to customize their ROS systems according to their use cases.

Another advantage of ROS is its modularity. ROS has a plugin-based system that allows users to modify and extend their systems quickly. The plugins are subject to specific dependencies, making it easier to manage complex systems by breaking them down into simpler, more manageable parts.

There is much more to ROS that we’ll delve into as we progress in the guide. But right now, you should know that ROS is a widely adopted and robust middleware for developing robotic systems. It provides a solid foundation for robotics systems, making the development of such systems quicker, more manageable, and fun.

Setting up the ROS Environment

First of all, you need to identify the version of ROS that suits your needs. As of 2021, the current long-term version of ROS is Noetic Ninjemys, with several other versions such as Melodic and Kinetic. Each of these versions has its specific features, and you can choose depending on the functionalities you need for your robot.

After selecting your ROS version, you can proceed to download and install it. ROS installation varies depending on your specific platform, so be sure to follow the official installation guides to avoid any hitches.

Once ROS is installed, you can start configuring your development workspace. A ROS workspace contains packages, which are the units of ROS software. Packages can contain binaries, libraries, datasets, or configuration files, depending on their primary purpose.

To configure your workspace, you need to create a catkin workspace that contains your projects and packages. You can use the catkin build tool to manage your workspace, install dependencies, compile and build your packages.

ROS Communication and Messaging

In ROS, communication between nodes occurs through a publish-subscribe model, where one node publishes data to a topic and one or more nodes subscribe to that topic to receive the data. Nodes can also send and receive data using services, similar to function calls, or actions, similar to services but with the added concept of feedback and cancellation.

Topics are named data buses through which nodes communicate with each other. Whenever a node publishes data on a topic, every node that has subscribed to that topic receives the published data. The messaging system used by topics is called a message. Messages are the data transmitted between nodes on topics, and they do not have a return value.

ROS services are used to send messages, just like function calls in software development. A node can invoke a service for another node to perform a specific task, and the node that provides the service sends a response, providing feedback to the node that invoked the service.

ROS actions extend services with an extra feature, feedback. Feedback makes it possible for the service to send continuous feedback to the indexing node, enabling the client node to monitor a long-running task’s progress.

By now, you understand that ROS messaging and communication are fundamental concepts to enable the complex behavior of the robotic systems we’re creating with ROS. Whether it’s sending data between nodes or invoking services and actions, data transfer and handling are crucial to building effective robotic systems.

Writing ROS Programs

A ROS node is a process that performs computation and communicates with other nodes in a ROS system. Nodes can be written in any programming language, but Python and C++ are the most common ones used in ROS development.

To create a node, you have to specify a unique name, a description for the node, and its functionality. Once the node is created, you can publish data on to the topics, subscribe to messages on a topic, or provide services to other nodes.

The ROS libraries offer several tools to facilitate programming, such as roscpp for C++ programming, rospy for Python programming, and roslaunch, which is a tool for managing multiple nodes and running them simultaneously.

One of the essential ROS libraries is the ROS messages library, which provides the message types used to communicate between nodes. The message types define the structure of the data that is transmitted between nodes, and they are generated from the message definition files using the ROS message generation tool.

Another useful ROS library is the ROS services library, which enables nodes to communicate through service requests and responses. The service definition file is used to define the service request and response message types, just like it is done in message definition files.

Now that you understand the basics of ROS programming, you can start writing your ROS nodes to bring your robotic system to life. There are several online resources, such as tutorials and sample codes, to help you get started with programming in ROS.

ROS Application Development

SLAM is a technique used to create a map of an unknown environment while simultaneously keeping track of the robot’s position within it. SLAM uses sensors such as LiDAR or cameras to gather data and create a map of the environment in real-time. With ROS, we can use packages such as GMapping or Cartographer to perform SLAM.

Navigation is the process by which a robot moves from one point to another in an environment. ROS makes it simple to implement robot navigation using the Navigation Stack library that provides path planning, localization, and obstacle avoidance. With these features in place, your robot can navigate its environment smoothly.

Perception, on the other hand, makes it possible for robots to perceive their environment using sensors such as cameras, depth sensors, or LiDAR. Perception enables robots to understand their environment, differentiate between objects, and make decisions based on the data acquired. ROS has packages such as OpenCV, PCL, and many others that provide the tools necessary for perception tasks.

All these applications, SLAM, navigation, and perception, showcase the power and flexibility of ROS in enabling the development of advanced robotic systems. They demonstrate the seamless integration and communication between the nodes in a ROS system, making it easy to implement the necessary functionalities for your robot.

In summary, by using ROS for applications such as SLAM, navigation, and perception, you can create robots that can explore their environment, understand their surroundings, and make intelligent decisions based on incoming data.

Conclusion – The Future of Robotics with ROS

We’ve explored the many advantages of using ROS for building robotic systems, including its open-source nature, modularity, communication and messaging facilities, and the availability of numerous libraries and tools.

We’ve also dived into the practical applications of ROS, starting from Simultaneous Localization and Mapping (SLAM), navigation, perception, and many others. With these applications, we’ve seen how ROS can power the development of advanced robots that can perceive their environment, plan their movements, and make intelligent decisions.

The future of the robotics industry seems bright with the increasing use of ROS in research, education, and industrial applications. ROS enables developers to create modular and distributed systems that are adaptable, extensible, and efficient.

With a large and growing community behind it, ROS will only continue to improve and evolve, offering developers more opportunities to create even more sophisticated robotics systems. Whether you’re designing industrial robots or creating robots for fun, ROS makes it possible to build advanced systems that are flexible and scalable.

So go out there and create, play, experiment and harness the power of ROS to create the robots of the future. The possibilities are endless, and the only limit is your imagination.