[robotics-worldwide] [journals] CFP: Special Issue on Cloud Robotics - an open access, journal publishes its 7th volume by MDPI

Ms. Elsie Zhao / MDPI elsie.zhao at mdpi.com
Mon Apr 16 23:49:16 PDT 2018


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
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go to the submission form. Manuscripts can be submitted until the
deadline. All papers will be 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.

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

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