The Robot Operating System (ROS) is a flexible framework for writing robot software. It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms. Why? Because creating truly robust, general-purpose robot software is hard. From the robot’s perspective, problems that seem trivial to humans often vary wildly between instances of tasks and environments. Dealing with these variations is so hard that no single individual, laboratory, or institution can hope to do it on their own. As a result, ROS was built from the ground up to encourage collaborative robotics software development. For example, one laboratory might have experts in mapping indoor environments, and could contribute a world-class system for producing maps. Another group might have experts at using maps to navigate, and yet another group might have discovered a computer vision approach that works well for recognizing small objects in clutter. ROS was designed specifically for groups like these to collaborate and build upon each other’s work, as is described throughout this site

References in zbMATH (referenced in 19 articles )

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  1. Bersani, Marcello M.; Soldo, Matteo; Menghi, Claudio; Pelliccione, Patrizio; Rossi, Matteo: PuRSUE -- from specification of robotic environments to synthesis of controllers (2020)
  2. Krivic, Senka; Cashmore, Michael; Magazzeni, Daniele; Szedmak, Sandor; Piater, Justus: Using machine learning for decreasing state uncertainty in planning (2020)
  3. Savino, Heitor J.; Pimenta, Luciano C. A.; Shah, Julie A.; Adorno, Bruno V.: Pose consensus based on dual quaternion algebra with application to decentralized formation control of mobile manipulators (2020)
  4. Alcaina, J.; Cuenca, A.; Salt, J.; Casanova, V.; Pizá, R.: Delay-independent dual-rate PID controller for a packet-based networked control system (2019)
  5. Fan Fei, Zhan Tu, Yilun Yang, Jian Zhang, Xinyan Deng: Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals (2019) arXiv
  6. Ozsoyeller, Deniz; Beveridge, Andrew; Isler, Volkan: Rendezvous in planar environments with obstacles and unknown initial distance (2019)
  7. Rego, Brenner S.; Raffo, Guilherme V.: Suspended load path tracking control using a tilt-rotor UAV based on zonotopic state estimation (2019)
  8. Hester, Todd; Stone, Peter: Intrinsically motivated model learning for developing curious robots (2017)
  9. Sabattini, Lorenzo; Secchi, Cristian; Fantuzzi, Cesare: Multi-robot systems implementing complex behaviors under time-varying topologies (2017)
  10. Huang, Wanrong; Wang, Yanzhen; Yang, Hai; Yi, Xiaodong; Yang, Xuejun: Distributed control for formation switch of fixed wing MAVs (2016)
  11. Wilkowski, Artur; Kornuta, Tomasz; Stefańczyk, Maciej; Kasprzak, Włodzimierz: Efficient generation of 3D surfel maps using RGB-D sensors (2016)
  12. Zuo, Jiaxin; Wang, Chaoli: Path following control for nonholonomic mobile robots with a distance between the mass center and the geometrical center (2016)
  13. Henikl, J.; Kemmetmüller, W.; Bader, M.; Kugi, A.: Modelling, simulation and identification of a mobile concrete pump (2015)
  14. Drevelle, Vincent; Nicola, Jeremy: VIBes: a visualizer for intervals and boxes (2014) ioport
  15. Hester, Todd; Stone, Peter: Real-time sample-efficient reinforcement learning for robots (2013) ioport
  16. Tao, Chongben; Liu, Guodong: A multilayer hidden Markov models-based method for human-robot interaction (2013) ioport
  17. Da Silva Simões, Alexandre; Colombini, Esther Luna; Matsuura, Jackson Paul; Franchin, Marcelo Nicoletti: TORP: the open robot project. A framework for module-based robots (2012) ioport
  18. Elkady, Ayssam; Sobh, Tarek: Robotics middleware: a comprehensive literature survey and attribute-based bibliography (2012) ioport
  19. Korpela, Christopher M.; Danko, Todd W.; Oh, Paul Y.: MM-UAV: mobile manipulating unmanned aerial vehicle (2012) ioport