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Fog robotics

From Wikipedia, the free encyclopedia

Fog robotics can be defined as an architecture which consists of storage, networking functions, control with fog computing closer to robots.[1][2]


Fog robotics mainly consists of a fog robot server and the cloud.[3] It acts as a companion to cloud by shoving the data near to the user with the help of a local server. Moreover, these servers are adaptable, consists of processing power for computation, network capability, and secured by sharing the outcomes to other robots for advanced performance with the lowest possible latency.[2]

As cloud robotics is facing issues such as bandwidth limitations, latency issues, quality of service, privacy and security - Fog robotics can be seen as a viable option for the future robotic systems.[4] It is also considered as distributed robot systems of the next generation because robots require much brain power for processing billions of computations while performing its task.[5] For instance, fog robotics can play an essential role in helping a robot to grasp spray bottle.[6]


Chand Gudi first coined the term "Fog Robotics" during the European Space Agency Competition[7] and IEEE/RSJ International Conference on Intelligent Robots and Systems in 2017, pioneering a new concept in the field.[1][8][9]


Social robots

A social robot can either connect to the cloud or fog robot server depending upon the availability of information. For instance, it can make a robot working at an airport to communicate with other robots for effective communication with the help of fog robotics.[10]

Fog robotic systems[edit]

Node-level systems: FogROS[11]

FogROS is a framework that allows existing Robot Operating System (ROS) automation applications to gain access to additional computing resources from commercial cloud-based services. With minimal porting effort, FogROS allows researchers to deploy components of their software to the cloud with high transparency.

Algorithm-level system: ElasticROS[12]

ElasticROS enhances robot systems by advancing from node to algorithm-level adaptability. It's the robot operating system integrating algorithm-level collaboration for fog and cloud robotics, deploying Elastic Collaborative Computing for real-time adjustments.


Fog Robotics[2]
This project promotes the applicability of fog robotics with regards to human-robot interaction scenarios. It utilises fog robot servers, cloud, and the robots for evaluation of fog robotics architecture.

Secure Fog Robotics Using the Global Data Plane[13]

To improve the security and performance of robotic/machine-learning applications operating in edge computing environments, this project investigates the use of data capsules. As one of the applications, it also examines the fog robot system to preserve the privacy and security of the data.

5G Coral: A 5G Convergent Virtualised Radio Access Network Living at the Edge[14]

This project particularly targets the field of radio access network at the edge. As part of this project, a real-time application of fog-assisted robotics is explored. Also, remote monitoring of robots and fleet formation for coordinated movement is being investigated.[15]

Fog Computing for Robotics and Industrial Automation[16]

This project focusses on designing novel programming models for Fog applications both hardware and operating system (OS) mechanisms including communication protocols of fog nodes. These fog nodes will be further tested real time on robots and other automation devices. Furthermore, an open-source architecture will be built on open standards, e.g., 5G, OPC Unified Architecture (UA), and Time-Sensitive Networking (TSN).

See also[edit]


  1. ^ a b Fog Robotics: An Introduction. Gudi, S.L.K.C., et al. IEEE/RSJ International Conference on Intelligent Robots and Systems. 2017
  2. ^ a b c Gudi, S. L. Krishna Chand; Ojha, S.; Johnston, B.; Clark, J.; Williams, M. (November 2018). "Fog Robotics for Efficient, Fluent and Robust Human-Robot Interaction". 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA). pp. 1–5. arXiv:1811.05578. doi:10.1109/NCA.2018.8548077. ISBN 978-1-5386-7659-2.
  3. ^ Fog Robotics: A New Approach to Achieve Efficient and Fluent Human-Robot Interaction. Ingrid Fadelli, ECN Magazine, USA 2018
  4. ^ Getting a Grip on Reality: Deep Learning and Robot Grasping Matthew Panzarino, TechCrunch, 2018
  5. ^ Robots and the return to collaborative intelligence. Ken Goldberg, Nature Machine Intelligence, 2019
  6. ^ Robots can't hold stuff very well, but you can help Matt Simon, Wired, 2018
  7. ^ "Smartwatch for Alzheimer's/Dementia Patients". Galileo Masters. Retrieved 2024-05-02.
  8. ^ Tian, Nan; Tanwani, Ajay Kummar; Chen, Jinfa; Ma, Mas; Zhang, Robert; Huang, Bill; Goldberg, Ken; Sojoudi, Somayeh (May 2019). "A Fog Robotic System for Dynamic Visual Servoing". 2019 International Conference on Robotics and Automation (ICRA). IEEE. pp. 1982–1988. arXiv:1809.06716. doi:10.1109/icra.2019.8793600. ISBN 978-1-5386-6027-0.
  9. ^ Chand, Gudi. "Fog Robotics". Fog Robotics. Chand Gudi. Retrieved 1 July 2024.
  10. ^ Fog robotics: A new approach to achieve efficient and fluent human-robot interaction. Ingrid Fadelli, Tech Xplore, UK 2018
  11. ^ Kaiyuan; Chen; Liang, Yafei; Jha, Nikhil; Ichnowski, Jeffrey; Danielczuk, Michael; Gonzalez, Joseph; Kubiatowicz, John; Goldberg, Ken (2021-08-25). "FogROS: An Adaptive Framework for Automating Fog Robotics Deployment". arXiv:2108.11355 [cs.RO].
  12. ^ Liu, Boyi; Wang, Lujia; Liu, Ming (2022-09-05). "ElasticROS: An Elastically Collaborative Robot Operation System for Fog and Cloud Robotics". arXiv:2209.01774 [cs.RO].
  13. ^ "Secure Fog Robotics Using the Global Data Plane". Retrieved 29 January 2019.
  14. ^ "5G Coral: A 5G Convergent Virtualised Radio Access Network Living at the Edge". Retrieved 29 January 2019.
  15. ^ "Fog Assisted Robotics" (PDF). Retrieved 29 January 2019.
  16. ^ "Fog Computing for Robotics and Industrial Automation". Retrieved 29 January 2019.