Innovations in the Internet of Robotic Things (IoRT)

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Intelligent Robots and Mechatronics".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 1009

Special Issue Editor


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Guest Editor
Assistive Robotics Laboratory, Department of Mechanical Engineering, Faculty of Science and Engineering, Hosei University, Kajino-cho 3-7-2, Koganei-shi, Tokyo 184-8584, Japan
Interests: intelligent robots with a focus on brain-machine interface; evolutionary robotics; map building; multi-robot systems; humanoid robot
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Special Issue Information

Dear Colleagues,

The Internet of Things enables devices to communicate with each other, facilitating data flow across conventional networks. The millions of gadgets connected to this network generate massive quantities of data, while also still allowing access to items themselves. The IoT has found applications in various areas over the last few years, including transportation, manufacturing, healthcare, and agriculture. One of the most promising domains within the Internet of Things is robotic applications, also known as the Internet of Robotic Things (IoRT). The ability of the Internet of Things to offer linked robots remote control and context sharing has spurred new research in robotics.

The goal of this Special Issue is to bring together recent works on a wide range of topics concerning the application of learning and evolution in robotics.

The scope of the Special Issue includes but is not limited:

  • IoRT in in assistive robotics;
  • Cloud robotic intelligence;
  • IoR in manufacturing;
  • IoR for human–robot interaction;
  • Cloud robotics;
  • Biologically motivated IoR;
  • Remote control of robotic systems;
  • Learning and evolution in IoR systems.

Prof. Dr. Genci Capi
Guest Editor

Manuscript Submission Information

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Keywords

  • Internet of Robotic Things
  • cloud robotics
  • remote control
  • intelligent robotics

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Published Papers (1 paper)

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Review

44 pages, 823 KiB  
Review
A Systematic Literature Review of DDS Middleware in Robotic Systems
by Muhammad Liman Gambo, Abubakar Danasabe, Basem Almadani, Farouq Aliyu, Abdulrahman Aliyu and Esam Al-Nahari
Robotics 2025, 14(5), 63; https://doi.org/10.3390/robotics14050063 - 14 May 2025
Viewed by 321
Abstract
The increasing demand for automation has led to the complexity of the design and operation of robotic systems. This paper presents a systematic literature review (SLR) focused on the applications and challenges of Data Distribution Service (DDS)-based middleware in robotics from 2006 to [...] Read more.
The increasing demand for automation has led to the complexity of the design and operation of robotic systems. This paper presents a systematic literature review (SLR) focused on the applications and challenges of Data Distribution Service (DDS)-based middleware in robotics from 2006 to 2024. We explore the pivotal role of DDS in facilitating efficient communication across heterogeneous robotic systems, enabling seamless integration of actuators, sensors, and computational elements. Our review identifies key applications of DDS in various robotic domains, including multi-robot coordination, real-time data processing, and cloud–edge–end fusion architectures, which collectively enhance the performance and scalability of robotic operations. Furthermore, we identify several challenges associated with implementing DDS in robotic systems, such as security vulnerabilities, performance and scalability requirements, and the complexities of real-time data transmission. By analyzing recent advancements and case studies, we provide insights into the potential of DDS to overcome these challenges while ensuring robust and reliable communication in dynamic environments. This paper aims to contribute to the transformative impact of DDS-based middleware in robotics, offering a comprehensive overview of its benefits, applications, and security implications. Our findings underscore the necessity for continued research and development in this area, paving the way for more resilient and intelligent robotic systems that operate effectively in real-world scenarios. This review not only fills existing gaps in the literature but also serves as a foundational resource for researchers and practitioners seeking to leverage DDS in the design and implementation of next-generation robotic solutions. Full article
(This article belongs to the Special Issue Innovations in the Internet of Robotic Things (IoRT))
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