Special Issue "Swarm Robotics in IoT Ecosystems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 May 2019

Special Issue Editor

Guest Editor
Dr. Giandomenico Spezzano

National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via Pietro Bucci, 8-9C, 87036 Rende (CS), Italy
Website | E-Mail
Interests: evolutionary computation, cognitive edge computing; IoT analytics; neuromorphic computing; fog computing

Special Issue Information

Dear Colleagues,

With over 24 billion devices expected to be installed by 2020, the IoT ecosystems will touch almost every industry, including transportation, insurance, utilities, telecom, healthcare, smart homes, oil and gas, and more, creating a massive data-driven economy and enabling a whole new range of unique services and products.

Robots, traditionally stand-alone systems, are quickly moving towards “everything connected” applications, accelerated by the availability of IoT-powered resources like big data, advancements in machine learning and the deployment of distributed cloud computing infrastructure at the network edge.

In this Special Issue we want to address recent advances in the following areas:

  • Cognitive computing
  • Multi-actor coalition forming and cooperative behaviors
  • Adaptive capability reconfiguration through distributed intelligence
  • Fog computing for smart manufacturing
  • Multi-agent robot systems
  • Deep learning and reinforcement learning for robotics
  • Building smart systems using IoT, deep machine learning, robotics, and artificial intelligence
  • Cellular learning automata for networked robots.
  • Wearable interactions for joint human–robot problem solving
  • Neuromorphic robotic control architectures and controllers
  • Cloud-assisted swarm robotics with novel communication paradigms

Prof. Dr. Giandomenico Spezzano
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 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

  • Cognitive computing 
  • Multi-actor coalition forming and cooperative behaviors 
  • Adaptive capability reconfiguration through distributed intelligence 
  • Fog computing for smart manufacturing 
  • Multi-agent robot systems
  • Deep learning and reinforcement learning for robotics 
  • Building smart systems using IoT, deep machine learning, robotics, and artificial intelligence 
  • Cellular learning automata for networked robots
  • Wearable interactions for joint human–robot problem solving 
  • Neuromorphic robotic control architectures and controllers 
  • Cloud-assisted swarm robotics with novel communication paradigms

Published Papers (1 paper)

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Research

Open AccessArticle A Multi-Robot Formation Platform based on an Indoor Global Positioning System
Appl. Sci. 2019, 9(6), 1165; https://doi.org/10.3390/app9061165
Received: 19 January 2019 / Revised: 15 March 2019 / Accepted: 17 March 2019 / Published: 19 March 2019
PDF Full-text (3591 KB)
Abstract
Aimed at the problem that experimental verifications are difficult to execute due to lacking effective experimental platforms in the research field of multi-robot formation, we design a simple multi-robot formation platform. This proposed general and low-cost multi-robot formation platform includes the indoor global-positioning [...] Read more.
Aimed at the problem that experimental verifications are difficult to execute due to lacking effective experimental platforms in the research field of multi-robot formation, we design a simple multi-robot formation platform. This proposed general and low-cost multi-robot formation platform includes the indoor global-positioning system, the multi-robot communication system, and the wheeled mobile robot hardware. For each wheeled mobile robot in our platform, its real-time position information in the centimeter‑level precise is obtained by the Marvelmind Indoor Navigation System and orientation information is obtained by the six-degree-of-freedom gyroscope. The Transmission Control Protocol/Internet Protocol (TCP/IP) wireless communication infrastructure is selected to support the communication among robots and the data collection in the process of experiments. Finally, a set of leader–follower formation experiments are performed by our platform, which include three trajectory tracking experiments of different types and numbers under deterministic environment and a formation-maintaining experiment with external disturbances. The results illustrate that our multi-robot formation platform can be effectively used as a general testbed to evaluate and verify the feasibility and correctness of the theoretical methods in the multi-robot formation. What is more, the proposed simple and general formation platform is beneficial to the development of platforms in the fields of multi-robot coordination, formation control, and search and rescue missions. Full article
(This article belongs to the Special Issue Swarm Robotics in IoT Ecosystems)
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