ROS (Robot Operating System)

ROS (Robot Operating System)
Open Source Robotics Foundation · CC BY 3.0 · source
Name	ROS (Robot Operating System)
Developer	Open Source Robotics Foundation
Released	2007
Programming language	C++, Python
Operating system	Linux
License	BSD

ROS (Robot Operating System) is an open-source framework for robot software development that provides libraries, tools, and conventions to simplify building complex robotic systems. It was originated to support research and development across academic and industrial institutions, enabling rapid prototyping and integration among diverse hardware and software projects. ROS facilitates communication, sensor processing, motion planning, and simulation through a modular architecture widely adopted by laboratories, startups, and multinational corporations.

Overview

ROS emerged from research at Willow Garage, a robotics research lab associated with Stanford University and the Personal Robotics Program, and later stewardship by the Open Source Robotics Foundation alongside contributors from institutions like Massachusetts Institute of Technology, Carnegie Mellon University, and ETH Zurich. The project influenced and interacted with initiatives such as DARPA Robotics Challenge, European Robotics Forum, and the IEEE Robotics and Automation Society. ROS has been used in projects involving NASA, Toyota Research Institute, Bosch, Google, Amazon Robotics, and the European Space Agency. ROS's design reflects patterns from UNIX, Berkeley Software Distribution, and middleware like CORBA and ZeroMQ while aligning with software engineering practices promoted by organizations such as the Linux Foundation and Apache Software Foundation.

Architecture and Core Concepts

The ROS architecture uses a peer-to-peer communication model with nodes, topics, services, actions, and parameters. Nodes correspond to processes similar to those in projects from Stanford Artificial Intelligence Laboratory and MIT Computer Science and Artificial Intelligence Laboratory, while topics implement publish/subscribe patterns found in technologies like MQTT, Apache Kafka, and ROS's contemporaries such as Player/Stage and YARP. Services provide synchronous request/response semantics analogous to gRPC and SOAP used by corporations including Microsoft, IBM, and Oracle. ROS packages and stacks mirror packaging systems from Debian, Ubuntu, Gentoo, and Fedora implemented by organizations like Canonical, Red Hat, and SUSE. Core libraries are written in C++ and Python, influenced by Boost, Eigen, and NumPy, with build tools inspired by CMake and Catkin which draw lineage from Kitware and KDE. The parameter server and launch system reflect designs similar to those from X Window System and systemd concepts used by GNOME and KDE projects.

Tools and Ecosystem

ROS integrates with simulators and visualization tools such as Gazebo, RViz, and Webots, developed by teams at Open Robotics, Stanford, and Cyberbotics, and used alongside physics engines like ODE, Bullet, and PhysX from organizations such as Sony, NVIDIA, and Intel. Development workflows commonly use version control systems and platforms including GitHub, GitLab, and Bitbucket; continuous integration systems like Jenkins, Travis CI, and CircleCI; and containerization technologies like Docker and Kubernetes from companies such as Google and Red Hat. Perception stacks interoperate with OpenCV, PCL, and TensorFlow from Willow Garage collaborations and contributors from Google Brain, Intel Labs, and Facebook AI Research. Motion planning leverages MoveIt! and OMPL with academic roots at University of Pennsylvania and technical contributions from Brown University and Rice University researchers. Hardware interfaces and drivers in the ecosystem support platforms from Clearpath Robotics, Boston Dynamics, KUKA, ABB, FANUC, and Universal Robots.

Distributions and Versions

ROS development progressed through major releases such as the early groovy, hydro, indigo, kinetic, melodic, and noetic series, coordinated with releases of Ubuntu from companies like Canonical and Linux distributions influenced by Debian maintainers. The transition to a new generation led to a redesigned release, successor architectures and distributions managed by Open Robotics with contributions from academia including University of Michigan, Georgia Tech, and University of Oxford. Versioning strategies echo semantic versioning used by projects such as Node.js, Django, and Ruby on Rails supported by communities at MITRE and Eclipse Foundation. Release cadence and long-term support align with enterprise practices found at Red Hat Enterprise Linux and Ubuntu LTS programs.

Applications and Use Cases

ROS is used in mobile manipulation, autonomous vehicles, aerial robotics, and industrial automation. Autonomous vehicle projects at Stanford Racing Team, ETH Zurich's Autonomous Systems Lab, and Carnegie Mellon University's Navlab incorporated ROS alongside autopilot systems from ArduPilot, PX4, and Apollo by Baidu. Aerial robotics projects employed ROS with platforms from DJI, Parrot, and Blue Origin research teams. Service robots built by companies such as Savioke, SoftBank Robotics, and iRobot used ROS stacks for navigation and human-robot interaction with speech systems from Nuance and Microsoft Research. In manufacturing, integrators combined ROS with PLC systems from Siemens and Rockwell Automation and vision systems from Cognex and Basler. Research applications spanned surgical robotics at Johns Hopkins and robotic swarms in projects by Harvard's Wyss Institute and MIT CSAIL.

Community, Governance, and Development Practices

The ROS community is an amalgam of academic labs, industry players, and open-source contributors coordinated through the Open Source Robotics Foundation, conferences such as ICRA, IROS, and ROSCon, and mailing lists, forums, and Q&A sites including Stack Overflow. Governance involves foundations, steering committees, and working groups akin to models used by Linux Foundation, Apache Software Foundation, and Eclipse Foundation. Development practices emphasize continuous integration, code review workflows on GitHub and GitLab, licensing under BSD and AGPL for certain components, and quality assurance using unit testing frameworks from Google Test and pytest. Education and outreach occur via university curricula at MIT, Stanford, University of Tokyo, and ETH Zurich, online courses from Coursera and edX, and community meetups supported by robotics hubs in Boston, Silicon Valley, and Zurich.

Category:Robotics software