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Development and Design of Autonomous Robot
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Development and Design of Autonomous Robot

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI in Autonomous Systems".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1434

Special Issue Editors

Dr. Tayab Din Memon

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Guest Editor
Centre for Artificial Intelligence Research and Optimization, Faculty of Design and Creative Technologies, Torrens University Australia, Flinders Campus, Melbourne, VIC 3000, Australia
Interests: artificial intelligence; machine learning; signal processing; embedded systems (FPGA/SoC); fault detection; identification systems; AI in education (AIEd); learning analytics; higher education policy
Special Issues, Collections and Topics in MDPI journals
Dr. Kamran Shaukat

E-Mail Website
Guest Editor
Centre for Artificial Intelligence Research and Optimisation, Faculty of Design and Creative Technologies, Torrens University Australia, Ultimo Campus, Sydney, NSW 2000, Australia
Interests: artificial intelligence; cyber security; health informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Sufyan Ali Memon

E-Mail Website
Guest Editor
Department of Defense Systems Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: artificial intelligence; autonomous vehicles; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the advancements and innovations in the development and design of autonomous robots. The topics covered explore the latest methodologies, challenges, and solutions in robotic autonomy. It includes hardware architecture, sensor integration, real-time decision-making, machine learning, and deep learning algorithms. Key discussions highlight improvements in robot mobility, perception, and interaction within complex environments. Moreover, emphasis is placed on real-world applications across sectors such as healthcare, hospitality, manufacturing, retails, and logistics, showcasing how autonomous robots are transforming these fields. By presenting cutting-edge research and practical insights, this Issue aims to provide valuable contributions to both academic and industrial communities working on autonomous systems.

Dr. Tayab Din Memon
Dr. Kamran Shaukat
Dr. Sufyan Ali Memon
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 250 words) can be sent to the Editorial Office for assessment.

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. AI is an international peer-reviewed open access monthly 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 1800 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

  • autonomous robots
  • sensor integration
  • machine learning
  • deep learning
  • real-time decision-making
  • robotics applications
  • robot mobility
  • industrial robotics
  • robotic perception

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Published Papers (1 paper)

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22 pages, 2652 KB  
Article
Semi-Supervised Generative Adversarial Networks (GANs) for Adhesion Condition Identification in Intelligent and Autonomous Railway Systems
by Sanaullah Mehran, Khakoo Mal, Imtiaz Hussain, Dileep Kumar, Tarique Rafique Memon and Tayab Din Memon
AI 2026, 7(2), 78; https://doi.org/10.3390/ai7020078 - 18 Feb 2026
Viewed by 622
Abstract
Safe and reliable railway operation forms an integral part of autonomous transport systems and depends on accurate knowledge of the adhesion conditions. Both the underestimation and overestimation of adhesion can compromise real-time decision-making in traction and braking control, leading to accidents or excessive [...] Read more.
Safe and reliable railway operation forms an integral part of autonomous transport systems and depends on accurate knowledge of the adhesion conditions. Both the underestimation and overestimation of adhesion can compromise real-time decision-making in traction and braking control, leading to accidents or excessive wear at the wheel–rail interface. Although limited research has explored the estimation of adhesion forces using data-driven algorithms, most existing approaches lack self-reliance and fail to adequately capture low adhesion levels, which are critical to identify. Moreover, obtaining labelled experimental data remains a significant challenge in adopting data-driven solutions for domain-specific problems. This study implements self-reliant deep learning (DL) models as perception modules for intelligent railway systems, enabling low adhesion identification by training on raw time sequences. In the second phase, to address the challenge of label acquisition, a semi-supervised generative adversarial network (SGAN) is developed. Compared to the supervised algorithms, the SGAN achieved superior performance, with 98.38% accuracy, 98.42% precision, and 98.28% F1-score in identifying seven different adhesion conditions. In contrast, the MLP and 1D-CNN models achieved accuracy of 91% and 93.88%, respectively. These findings demonstrate the potential of SGAN-based data-driven perception for enhancing autonomy, adaptability, and fault diagnosis in intelligent rail and robotic mobility systems. The proposed approach offers an efficient and scalable solution for real-time railway condition monitoring and fault identification, eliminating the overhead associated with manual data labelling. Full article
(This article belongs to the Special Issue Development and Design of Autonomous Robot)
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