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Electronics
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Human-Robot Interaction and Collaboration for Effective Solutions to Real-World Problems
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Human-Robot Interaction and Collaboration for Effective Solutions to Real-World Problems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 August 2025) | Viewed by 3672

Special Issue Editors

Dr. Vasiliki Balaska

E-Mail Website
Guest Editor
Department of Production and Management Engineering, Democritus University of Thrace, 671 32 Xanthi, Greece
Interests: semantic mapping; navigation and self-localization
Dr. Symeon Symeonidis

E-Mail Website
Guest Editor
Department of Production and Management Engineering, Democritus University of Thrace, 69100 Komotini, Greece
Interests: data science; computer vision; information systems; business intelligence; artificial intelligence
Prof. Dr. Antonios Gasteratos

E-Mail Website
Guest Editor
Department of Production and Management Engineering, Democritus University of Thrace, 69100 Komotini, Greece
Interests: intelligent and autonomous robots; safety in mechatronic systems; design and implementation of VLSI digital systems and FPGAs; digital image processing

Special Issue Information

Dear Colleagues,

The Special Issue entitled "Human-Robot Interaction and Collaboration for Effective Solutions to Real-World Problems" focuses on innovative technologies aiming at addressing challenges arising from human and robot coexistence in various environments, namely domestic and work environments. This Special Issue will feature research on communication interfaces that allow for intelligent human-robot interactions, the deployment of adaptive robots in close cooperation with humans in sensitive environments, and the role of autonomous systems that work in proximity with humans in critical scenarios. Articles will include case studies, technological innovations, and methodologies that demonstrate the significant impact of human-robot partnerships in enhancing operational efficiency, safety, and problem-solving in real-world applications.

Dr. Vasiliki Balaska
Dr. Symeon Symeonidis
Prof. Dr. Antonios Gasteratos
Guest Editors

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • human-robot interaction (HRI)
  • collaborative robotics
  • adaptive and autonomous robots
  • communication interfaces
  • conversational AI
  • social navigation
  • LLM-powered robots

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Published Papers (3 papers)

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Research

22 pages, 4937 KB  
Article
Multimodal AI for UAV: Vision–Language Models in Human– Machine Collaboration
by Maroš Krupáš, Ľubomír Urblík and Iveta Zolotová
Electronics 2025, 14(17), 3548; https://doi.org/10.3390/electronics14173548 - 6 Sep 2025
Viewed by 353
Abstract
Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments, where adaptive autonomy and intuitive interaction are essential. [...] Read more.
Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments, where adaptive autonomy and intuitive interaction are essential. Traditional UAV autonomy has relied mainly on visual perception or preprogrammed planning, offering limited adaptability and explainability. This study introduces a novel reference architecture, the multimodal AI–HMC system, based on which a dedicated UAV use case architecture was instantiated and experimentally validated in a controlled laboratory environment. The architecture integrates VLM-powered reasoning, real-time depth estimation, and natural-language interfaces, enabling UAVs to perform context-aware actions while providing transparent explanations. Unlike prior approaches, the system generates navigation commands while also communicating the underlying rationale and associated confidence levels, thereby enhancing situational awareness and fostering user trust. The architecture was implemented in a real-time UAV navigation platform and evaluated through laboratory trials. Quantitative results showed a 70% task success rate in single-obstacle navigation and 50% in a cluttered scenario, with safe obstacle avoidance at flight speeds of up to 0.6 m/s. Users approved 90% of the generated instructions and rated explanations as significantly clearer and more informative when confidence visualization was included. These findings demonstrate the novelty and feasibility of embedding VLMs into UAV systems, advancing explainable, human-centric autonomy and establishing a foundation for future multimodal AI applications in HMC, including robotics. Full article
(This article belongs to the Special Issue Human-Robot Interaction and Collaboration for Effective Solutions to Real-World Problems)
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42 pages, 2745 KB  
Article
Machine Vision in Human-Centric Manufacturing: A Review from the Perspective of the Frozen Dough Industry
by Vasiliki Balaska, Anestis Tserkezis, Fotios Konstantinidis, Vasileios Sevetlidis, Symeon Symeonidis, Theoklitos Karakatsanis and Antonios Gasteratos
Electronics 2025, 14(17), 3361; https://doi.org/10.3390/electronics14173361 - 24 Aug 2025
Viewed by 500
Abstract
Machine vision technologies play a critical role in the advancement of modern human-centric manufacturing systems. This study investigates their practical applications in improving both safety and productivity within industrial environments. Particular attention is given to areas such as quality assurance, worker protection, and [...] Read more.
Machine vision technologies play a critical role in the advancement of modern human-centric manufacturing systems. This study investigates their practical applications in improving both safety and productivity within industrial environments. Particular attention is given to areas such as quality assurance, worker protection, and process optimization, illustrating how intelligent visual inspection systems and real-time data analysis contribute to increased operational efficiency and higher safety standards. The research methodology combines an in-depth analysis of industrial case studies, including one from the frozen dough industry, with a systematic review of the current literature on machine vision technologies in manufacturing. The findings highlight the potential of such systems to reduce human error, maintain consistent product quality, minimize material waste, and promote safer and more adaptable work environments. This study offers valuable insights into the integration of advanced visual technologies within human-centered production environments, while also addressing key challenges and future opportunities for innovation and technological evolution. Full article
(This article belongs to the Special Issue Human-Robot Interaction and Collaboration for Effective Solutions to Real-World Problems)
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15 pages, 3000 KB  
Article
Enabling Self-Practice of Digital Audio–Tactile Maps for Visually Impaired People by Large Language Models
by Chanh Minh Tran, Nguyen Gia Bach, Phan Xuan Tan, Eiji Kamioka and Manami Kanamaru
Electronics 2024, 13(12), 2395; https://doi.org/10.3390/electronics13122395 - 19 Jun 2024
Cited by 3 | Viewed by 2024
Abstract
Digital audio–tactile maps (DATMs) on touchscreen devices provide valuable opportunities for people who are visually impaired (PVIs) to explore the spatial environment for engaging in travel activities. Existing solutions for DATMs usually require extensive training for the PVIs to understand the feedback mechanism. [...] Read more.
Digital audio–tactile maps (DATMs) on touchscreen devices provide valuable opportunities for people who are visually impaired (PVIs) to explore the spatial environment for engaging in travel activities. Existing solutions for DATMs usually require extensive training for the PVIs to understand the feedback mechanism. Due to the shortage of human resources for training specialists, as well as PVIs’ desire for frequent practice to maintain their usage skills, it has become challenging to widely adopt DATMs in real life. This paper discusses the use of large language models (LLMs) to provide a verbal evaluation of the PVIs’ perception, which is crucial for the independent practice of DATM usage. A smartphone-based prototype providing DATMs of simple floor plans was developed for a preliminary investigation. The evaluation results have proven that the interaction with the LLM could help the participants better understand the DATMs’ content and could vividly replicate them by drawings. Full article
(This article belongs to the Special Issue Human-Robot Interaction and Collaboration for Effective Solutions to Real-World Problems)
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