The global robotics market is projected to reach USD 178.7 Billion by 2033. The complexity of creating intelligent machines is growing exponentially, including the new emerging field of embodied AI. Developers face the daunting task of integrating sensors, actuators, navigation, manipulation, and machine intelligence into a cohesive system. Building these intricate software stacks from the ground up is not just time-consuming; it’s a barrier to innovation. This is where the Robot Operating System (ROS) enters, offering a standardized, powerful framework to streamline this process.
The market for ROS itself is quite impressive, valued at USD 566 million in 2023 and expected to grow significantly. But what exactly is ROS, and why has it become an indispensable tool for robotics engineers, researchers, and hobbyists worldwide? This article explores the core reasons why you should use ROS for your next robotics project along with some limitations you should be aware of.
Side note: whenever I mention ROS in this article, I am talking about the general ROS project, which includes ROS 2 (ROS 1 has reached end-of-life as of May, 2025).
What is the Robot Operating System (ROS)?
Modern robotics applications, from an autonomous mobile robot navigating a warehouse to collaborative robots on a manufacturing line, require a symphony of software components. These systems must handle real-time data processing, complex sensor fusion, path planning, and interaction with the physical world. Developing the underlying communication protocols, hardware drivers, and algorithms for each new project is a monumental effort that distracts from the primary goal: creating novel robotic behaviors and capabilities.
Despite its name, ROS is not a traditional operating system like Windows or Linux. Instead, it is a flexible and powerful middleware framework that runs on top of an existing OS (typically Ubuntu Linux). Think of it as a comprehensive software development kit (SDK) designed specifically for robotics. It provides a structured communication layer, low-level device control, a vast array of tools, and a rich ecosystem of libraries.
The immense popularity and utility of ROS are reflected in its adoption rates, with over half a billion ROS packages downloaded in 2023 alone! This highlights its role as a de facto standard for building complex robotics applications.
Advantages of ROS
Accelerate Development and Reduce Time-to-Market
The most compelling reason to use ROS is efficiency. It embodies the “don’t reinvent the wheel” philosophy by providing a vast collection of pre-built, community-vetted software modules called ROS packages. Need to implement navigation? There are packages for SLAM algorithms (Simultaneous Localization and Mapping). Working on a robotic arm? You can find packages for motion planning and kinematics. This allows developers to focus on unique application logic rather than foundational plumbing.
Build Robust, Scalable, and Adaptable Robotic Systems
At the heart of ROS is a powerful node-based messaging architecture. This design principle encourages you to break down a complex robotics task into small, independent, and reusable programs called “nodes.” Each node is responsible for a single function, such as controlling a wheel motor, processing camera data, or planning a path.
These nodes communicate through a distributed computing architecture using standardized message-passing mechanisms like ROS topics (a publish/subscribe model) and ROS services (a request/response model), all defined by specific message types. In ROS 1, this communication was managed by a single point of failure, the ROS master. ROS 2 revolutionizes this with the Data Distribution Service (DDS), a decentralized discovery and messaging protocol that eliminates the master, making systems far more robust, scalable, and suitable for real-time applications and multi-robot fleets.
Leverage a Powerful, Global Open-Source Ecosystem
When you adopt ROS, you are not just getting a software framework; you are joining a massive global community of developers, researchers, and companies. This ecosystem is a force multiplier for development. Stuck on a problem? Chances are someone has already solved it and shared the solution in one of the thousands of public github repositories.
The commercial world has taken note, with over 1250 companies using ROS for products ranging from industrial robots to self-driving cars. Specialized communities, such as ROS-Industrial, provide dedicated resources and ROS-Industrial wiki pages to bridge the gap between open-source innovation and the stringent demands of industrial automation.
You can view a list of companies that have adopted at least some part of the ROS ecosystem in their released products here: https://github.com/vmayoral/ros-robotics-companies.
Simulate, Test, and Visualize with Confidence
Developing on physical hardware is expensive, slow, and risky. ROS addresses this with a suite of world-class simulation tools like Gazebo, allowing you to design, test, and iterate on your robot in a realistic virtual environment before a single piece of hardware is built. Complementing this are powerful visualization tools like RViz, which provide a 3D view of your robot’s sensor data, internal states, and planned actions, making debugging complex algorithms dramatically easier.
Bridging Research, Education, and Industrial Applications
ROS provides a common language that unifies the robotics world. A student can learn ROS on a small mobile base, and those same skills are directly applicable to developing software for large-scale industrial robots or advanced collaborative robots. This seamless transition from academia to industry accelerates innovation across the board. ROS is the backbone for a vast range of robotics applications, including logistics automation, surgical assistance, agricultural robots, and consumer electronics. In fact, the healthcare and life sciences sector is projected to see a CAGR of 18.4% in ROS adoption for applications in surgical and laboratory automation.
Limitations of ROS
ROS 2 limitations and disadvantages
Despite its power, ROS is not a silver bullet. The learning curve can be steep, especially for beginners. Understanding the core concepts of nodes, topics, services, and the build system requires a significant initial investment of time. For very simple projects or deeply embedded systems with extreme resource constraints (e.g., a small microcontroller), the overhead of ROS might be unnecessary. Furthermore, while ROS 2 has made huge strides in real-time performance, achieving deterministic, hard real-time behavior can still require careful system configuration and expertise.
Additionally, the multi-node architecture of ROS is not without its challenges and drawbacks, particularly in certain use cases. Understanding these limitations is essential for effectively evaluating whether ROS is the right choice for your project.
The inherent design of ROS involves numerous nodes communicating over topics and services, which can significantly increase the complexity of the system. Managing this communication introduces latency and overhead, complicating the system’s architecture. In environments where precision and synchronization are critical, maintaining reliable inter-node communication can become a formidable technical challenge. Troubleshooting these communication pathways often requires deep detail-oriented debugging, which can be time-consuming and technically demanding.
Operating multiple nodes concurrently requires substantial system resources. On smaller platforms equipped with limited memory and processing power, such as microcontrollers or embedded systems, the resource demand from managing numerous nodes can outweigh the system’s capabilities. This resource intensity can lead to inefficiencies, including potential slowdowns or system bottlenecks, which are especially problematic in real-time applications.
Where ROS May Not Be the Best Fit
For projects focused on simple roving functions or designed for educational purposes that demand only basic operations, the complexity introduced by ROS may be unnecessary. In these scenarios, using lightweight frameworks or even directly writing code without middleware can significantly reduce development time and streamline project execution.
In applications dedicated to a single primary task, the sophisticated architecture of a multi-node ROS setup may add more complexity than necessary. For such focused systems, where the operations are straightforward and don’t benefit from modular scalability, simpler solutions can be more effective and efficient. Opting for straightforward code structures, devoid of the intricacies of ROS’s multi-node architecture, might prove optimal for maintaining system simplicity and reliability.
Comparison to Other Non-ROS Solutions
Other robotics frameworks exist to compete with ROS, but most are paid, proprietary systems that are tied to specific hardware. Some examples include:
- ABB’s RobotWare: This framework is tailored for ABB robots, offering robust performance and tightly integrated hardware control.
- KUKA’s Sunrise: Designed specifically for KUKA robots, it provides high fidelity in motion control and is typically used in industrial settings.
The other option is to develop a custom, in-house framework. This provides unparalleled customization options and potential for optimization for a given hardware platform. Features can be tailored to the exact needs of the project, and developers have complete oversight and control of the framework. However, such development can be expensive and time-consuming. It also involved “reinventing the wheel,” a problem that ROS explicitly tries to solve. This can lead to inefficiencies in the project flow as developers prioritize underlying framework issues over direct use-case applications.
Conclusion: When ROS Makes Sense
The decision to use ROS hinges on complexity and scale. If your project involves multiple sensors, actuators, and intelligent processes communicating with each other (whether on a single machine or across a network) ROS is almost certainly the right choice. It provides the hardware abstraction, communication infrastructure, and tooling needed to manage that complexity effectively. For a simple, single-purpose device, it may be overkill. But for anything from a sophisticated robotic arm to a fleet of autonomous vehicles, building on ROS is the fastest way to turn an innovative idea into a functional reality. ROS 2, with its robust, scalable, and industry-ready architecture, is the clear path forward for any serious robotics development effort today.
ROS can have a steep learning curve, and I recommend setting aside a few weeks to get started learning the basics of nodes, topics, and services before tackling more difficult concepts, like TF2, inverse kinematics, and SLAM. To get started, feel free to check out my video series (still being released at the time of writing):
If you prefer the written word, I recommend this book by Edouard Renard.