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Electronics
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Robotics: From Technologies to Applications
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Robotics: From Technologies to Applications

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 1942

Special Issue Editors

Dr. Dong Chen

E-Mail Website
Guest Editor
Environmental Institute, University of Virginia, Charlottesville 22903, VA, USA
Interests: cyber–physical systems (CPS); reinforcement learning; multi-agent systems; intelligent transportation systems; robotics; smart agriculture
Dr. Mingle Xu

E-Mail Website
Guest Editor
Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonbuk 54896, Republic of Korea
Interests: artificial intelligence; robotics; smart agriculture

Special Issue Information

Dear Colleagues,

Robotics is evolving from being a technology-driven field to being one centered on practical applications that address real-world challenges. This Special Issue, titled "Robotics: From Technologies to Applications", will showcase the latest advancements and innovative solutions in robotics, spanning various sectors such as healthcare, agriculture, manufacturing, logistics, and service industries.

We invite contributions that explore the technical challenges and breakthroughs in robotic perception, manipulation, mobility, human–robot interactions, and autonomous systems. Papers demonstrating practical applications and real-world implementations are particularly welcome.

Topics of interest include, but are not limited to, the following:

  • AI for robotic perception and decision-making;
  • Advanced sensing and actuation technologies;
  • Human–robot interaction and collaboration;
  • Robotics in healthcare and rehabilitation;
  • Agricultural robotics for precision farming and harvesting;
  • Industrial robots for manufacturing and automation;
  • Service robots for logistics, retail, and hospitality.

We look forward to your valuable contributions, which will advance the field of robotics and demonstrate its transformative impact on society.

Dr. Dong Chen
Dr. Mingle Xu
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

  • robotics
  • artificial intelligence
  • machine learning
  • human–robot interaction
  • autonomous systems
  • industrial automation
  • agricultural robotics
  • healthcare robotics
  • service robots
  • autonomous vehicles

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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21 pages, 1066 KiB  
Article
Enhancing Quadruped Robot Walking on Unstructured Terrains: A Combination of Stable Blind Gait and Deep Reinforcement Learning
by Shirelle Drori Marcus, Amir Shapiro and Chen Giladi
Electronics 2025, 14(7), 1431; https://doi.org/10.3390/electronics14071431 - 2 Apr 2025
Viewed by 430
Abstract
Legged robots, designed for high adaptability, are poised for deployment in hazardous tasks traditionally undertaken by humans, particularly in unstructured terrains where their wheeled counterparts cannot operate. Nevertheless, using them in unstructured settings necessitates advanced control techniques to maneuver without depending entirely on [...] Read more.
Legged robots, designed for high adaptability, are poised for deployment in hazardous tasks traditionally undertaken by humans, particularly in unstructured terrains where their wheeled counterparts cannot operate. Nevertheless, using them in unstructured settings necessitates advanced control techniques to maneuver without depending entirely on visual signals or pre-programmed instructions. To address these challenges, this research proposes a novel walking algorithm for quadruped robots that blends a stable blind gait without needing any visual cues with deep reinforcement learning to enhance mobility across diverse terrains. The algorithm’s effectiveness was evaluated virtually, emphasizing the ability to regulate the robot’s leg movements and posture when reaching obstacles. Our results demonstrated a success rate of over 90% in the stair-climbing task, suggesting that the algorithm improved the robot’s mobility and stability. Although emphasizing a steady blind gait reduces reliance on visual cues, incorporating the algorithm with further sensory inputs and environmental awareness may improve the robot’s functionality and versatility in practical situations. More dynamic gaits and a wider variety of static and dynamic obstacles will be the focus of future algorithm development. Furthermore, validation in the real world will aid in detecting any shortcomings or potential areas for enhancement in the algorithm, thereby improving its adaptability and resilience in diverse settings and assignments. Full article
(This article belongs to the Special Issue Robotics: From Technologies to Applications)
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Review

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12 pages, 201 KiB  
Review
Advances in Autism Spectrum Disorder (ASD) Diagnostics: From Theoretical Frameworks to AI-Driven Innovations
by Christine K. Syriopoulou-Delli
Electronics 2025, 14(5), 951; https://doi.org/10.3390/electronics14050951 - 27 Feb 2025
Viewed by 1161
Abstract
This study provides a comprehensive analysis of the evolution of Autism Spectrum Disorder (ASD) diagnostics, tracing its progression from psychoanalytic origins to the integration of advanced artificial intelligence (AI) technologies. The study explores, through scientific data bases like Pub Med, Scopus, and Google [...] Read more.
This study provides a comprehensive analysis of the evolution of Autism Spectrum Disorder (ASD) diagnostics, tracing its progression from psychoanalytic origins to the integration of advanced artificial intelligence (AI) technologies. The study explores, through scientific data bases like Pub Med, Scopus, and Google Scholar, how theoretical frameworks, including psychoanalysis, behavioral psychology, cognitive development, and neurobiological paradigms, have shaped diagnostic methodologies over time. Each paradigm’s associated assessment tools, such as the Autism Diagnostic Observation Schedule (ADOS) and the Vineland Adaptive Behavior Scales, are discussed in relation to their scientific advancements and limitations. Emerging technologies, particularly AI, are highlighted for their transformative impact on ASD diagnostics. The application of AI in areas such as video analysis, Natural Language Processing (NLP), and biodata integration demonstrates significant progress in precision, accessibility, and inclusivity. Ethical considerations, including algorithmic transparency, data security, and inclusivity for underrepresented populations, are critically examined alongside the challenges of scalability and equitable implementation. Additionally, neurodiversity- informed approaches are emphasized for their role in reframing autism as a natural variation of human cognition and behavior, advocating for strength-based, inclusive diagnostic frameworks. This synthesis underscores the interplay between evolving theoretical models, technological advancements, and the growing focus on compassionate, equitable diagnostic practices. It concludes by advocating for continued innovation, interdisciplinary collaboration, and ethical oversight to further refine ASD diagnostics and improve outcomes for individuals across the autism spectrum. Full article
(This article belongs to the Special Issue Robotics: From Technologies to Applications)
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