Cloud Robotics

A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 30805

Special Issue Editor

Department of Information Technology, Ghent University, Ghent, ‎Belgium
Interests: deep learning, neuromorphic computing, distributed systems, robotics, Internet-of-robotic-things, cloud robotics

Special Issue Information

Dear Colleagues,

The domain of cloud robotics aims to converge robots with the elastic and on-demand computation, storage and communication resources provided by the cloud. The cloud may complement robotic resources in several ways, including crowd-sourcing knowledge databases, analysis of multi-modal context information, computational offloading or data-intensive information processing for artificial intelligence. 

Several trends are promising fields to further advance the state-of-the-art in cloud robotics. In the cloud domain, we have witnessed the emergence of more distributed infrastructure, known under various names like cloudlets, edge computing or fog computing. New cluster management techniques have emerged, such as orchestrators for container-based deployments of micro-service architectures, software-defined networking etc. Lastly, next-generation wireless and mobile network technologies bring reduced latency and increasing bandwidth.

In the robotics domain, we see a rising number of robots that move outside of the well-controlled lab environment into realistic operational conditions where other actors (including humans) are active. This requires robots to have a better perception and cognitive understanding of their environment. This can be realized by deep learning, including reinforcement learning for robotic control. Here, the cloud can be used for sharing and processing of multi-modal data, domain knowledge and experience, and for running the heavy computations involved with these machine learning techniques. Another way to improve the perception and cognition is by embedding robots into the Internet-of-Things.

The objective of this Special Issue is therefore to promote a deeper understanding of major conceptual and technical challenges in the various disciplines of robotics (e.g. perception, cognition, control) that leverage on the cloud. We are seeking for contributions in both robotic algorithms as cloud frameworks and architectures supporting robots. Lastly, we welcome articles that provide a detailed overview of concrete use cases in different application domains, such as health care, agriculture, search-and-rescue, Industry 4.0, security, etc.

Topics of interest include (but are not limited to):

  • architectures and middleware solutions for cyber-physical systems, integrating robots with the cloud, the edge and/or the Internet-of-Things
  • distributed sensing, planning and actuation
  • cloud-based control systems of robots, possibly  using deterministic wireless or wired networking
  • computational offloading of robotic processing workloads
  • cloud-based knowledge processing services for robotics
  • cloud-assisted deep learning techniques in robotic disciplines such as perception, cognition or control
  • long-term robot autonomy based on big data, knowledge sharing and IoT
  • security and liability of cloud-based/remote robot controllers, e.g., by using blockchain
  • transfer learning between tasks or across robotic systems
  • cloud robotic use cases: implementation details, performance measurements and lessons learnt
Prof. Pieter Simoens
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Robotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cloud offload
  • robotic knowledge processing services and databases
  • deep learning for robotics
  • Internet-of-Robotic-Things
  • cloud robotics
  • cloud frameworks and architectures for robotic processing workloads

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Review

36 pages, 2618 KiB  
Review
Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges
by Sarthak Bhagat, Hritwick Banerjee, Zion Tsz Ho Tse and Hongliang Ren
Robotics 2019, 8(1), 4; https://doi.org/10.3390/robotics8010004 - 18 Jan 2019
Cited by 72 | Viewed by 14850 | Correction
Abstract
The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent [...] Read more.
The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics. Full article
(This article belongs to the Special Issue Cloud Robotics)
Show Figures

Figure 1

25 pages, 7049 KiB  
Review
A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications
by Olimpiya Saha and Prithviraj Dasgupta
Robotics 2018, 7(3), 47; https://doi.org/10.3390/robotics7030047 - 30 Aug 2018
Cited by 93 | Viewed by 15416
Abstract
Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering [...] Read more.
Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions. Full article
(This article belongs to the Special Issue Cloud Robotics)
Show Figures

Figure 1

Back to TopTop