Advanced Autonomous Systems and Artificial Intelligence Stage

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 19085

Special Issue Editors


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Guest Editor

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Guest Editor
Department of Mechanisms and Robots Theory, National University of Science and Technology Polytechnic Bucharest, Splaiul Independentei Street 313, 060042 Bucharest, Romania
Interests: machines; bioengineering; automation; robotics

Special Issue Information

Dear Colleagues,

We invite you to participate in this Special Issue to share your latest findings in the field of automation and autonomous systems. Today, automation has become not only extremely useful, but even indispensable, in certain key areas, in all modern industrial areas. Sometimes, even intelligent automation is not enough, but it is needed to achieve a certain level of autonomy for industrial systems, giving them the ability to increase the quality of their work, the degree of safety offered, and the possibility of making decisions in real-time.

Those prepared for the future have the determination to face any situation, and will welcome change everywhere, while those who cannot tolerate change will bury their heads in the ground, believing wrongly that change will not happen if they choose not to see it. Machines, or robots, as they are often called today, are meant to help us in our work, ease our burdens, and work hard for us, so that we can all live better, more beautifully, and more freely. As they are man-made and progressive creations, they are not to be treated like slaves, but rather as a tool for bringing about a better future. Young people play an essential role here, even if society wants to marginalize them and teach them their old ways. Young people have a lot of energy, respond well to training, and have significant capability. They have high hopes and adapt more easily to the new. Young people know how to dream beautifully, and how to fulfill those dreams. Together with them, we will build a new, better, safer, more peaceful, and more modern society. Only then will robots be applicable for their ultimate humanitarian task and will help us conquer outer space in our attempt to expand into the universe.

We welcome the submission of articles or reviews in, but not limited to, the following fields:

  1. Mechanical and mechatronic engineering;
  2. Mechanisms analysis and synthesis;
  3. Dynamics of mechanisms and machines;
  4. Mechanical transmissions;
  5. Biomechanics;
  6. Precision mechanics;
  7. Design, analysis, and optimization of mechanical and mechatronic systems and mechanical mechanisms, including electrically operated ones;
  8. Automation and control;
  9. New technologies and control methods in mechanical and mechatronic systems;
  10. Intelligent control systems, adaptive control algorithms, and their application in mechatronic mechanisms/assemblies;
  11. Industrial robotics and autonomous mobility;
  12. Recent developments in industrial robotics;
  13. Serial and parallel robots;
  14. Mobile robots;
  15. Collaborative robots;
  16. Micro and nano robots;
  17. Medical robots;
  18. Teleoperation, haptics, and virtual reality;
  19. Handling objects;
  20. Navigation and autonomous mobility systems (electrically operated);
  21. Energy and power sources and mechatronic and electro-mechanical systems;
  22. New energy storage technologies;
  23. Renewable energy sources and electric propulsion systems in cars and other vehicles;
  24. Sensors and actuators;
  25. Artificial intelligence;
  26. Ways to improve the performance and efficiency of sensors and actuators in electrically operated mechanisms;
  27. Computer-aided design and manufacturing;
  28. Computer simulations and computer-aided design;
  29. Computational and experimental methods;
  30. CAD in mechanism and machine design;
  31. 3D printing and other emerging technologies;
  32. Artificial intelligence in mechatronics;
  33. Artificial intelligence in mechanical and mechatronic systems;
  34. Computer vision;
  35. Motion planning in the context of electrically operated mechanisms;
  36. Human–machine interaction and user interfaces;
  37. Intuitive user interfaces;
  38. Virtual/augmented reality technologies;
  39. MEMS/NEMS;
  40. Micromechanisms and microactuators;
  41. Microprocessing;
  42. Micro–nano characterizations of thin films.

Dr. Florian Ion Tiberiu Petrescu
Dr. Liviu Marian Ungureanu
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. Technologies 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 1600 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

  • mechanics
  • robotics
  • artificial intelligence
  • computer vision
  • renewable energy
  • sensors and actuators
  • human–machine interaction
  • virtual/augmented reality technologies
  • MEMS/NEMS

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Published Papers (6 papers)

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Research

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25 pages, 20254 KiB  
Article
IoT-Enhanced Decision Support System for Real-Time Greenhouse Microclimate Monitoring and Control
by Dragoș-Ioan Săcăleanu, Mihai-Gabriel Matache, Ștefan-George Roșu, Bogdan-Cristian Florea, Irina-Petra Manciu and Lucian-Andrei Perișoară
Technologies 2024, 12(11), 230; https://doi.org/10.3390/technologies12110230 - 14 Nov 2024
Viewed by 708
Abstract
Greenhouses have taken on a fundamental role in agriculture. The Internet of Things (IoT) is a key concept used in greenhouse-based precision agriculture (PA) to enhance vegetable quality and quantity while improving resource efficiency. Integrating wireless sensor networks (WSNs) into greenhouses to monitor [...] Read more.
Greenhouses have taken on a fundamental role in agriculture. The Internet of Things (IoT) is a key concept used in greenhouse-based precision agriculture (PA) to enhance vegetable quality and quantity while improving resource efficiency. Integrating wireless sensor networks (WSNs) into greenhouses to monitor environmental parameters represents a critical first step in developing a complete IoT solution. For further optimization of the results, including actuator nodes to control the microclimate is necessary. The greenhouse must also be remotely monitored and controlled via an internet-based platform. This paper proposes an IoT-based architecture as a decision support system for farmers. A web platform has been developed to acquire data from custom-developed wireless sensor nodes and send commands to custom-developed wireless actuator nodes in a greenhouse environment. The wireless sensor and actuator nodes (WSANs) utilize LoRaWAN, one of the most prominent Low-Power Wide-Area Network (LPWAN) technologies, known for its long data transmission range. A real-time end-to-end deployment of a remotely managed WSAN was conducted. The power consumption of the wireless sensor nodes and the recharge efficiency of installed solar panels were analyzed under worst-case scenarios with continuously active nodes and minimal intervals between data transmissions. Datasets were acquired from multiple sensor nodes over a month, demonstrating the system’s functionality and feasibility. Full article
(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)
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19 pages, 3846 KiB  
Article
Improving Diabetes Education and Metabolic Control in Children Using Social Robots: A Randomized Trial
by Tareq Alhmiedat, Laila A. AlBishi, Fady Alnajjar, Mohammed Alotaibi, Ashraf M. Marei and Rakan Shalayl
Technologies 2024, 12(11), 209; https://doi.org/10.3390/technologies12110209 - 23 Oct 2024
Viewed by 1067
Abstract
Robot engagement in healthcare has the potential to alleviate medical personnel workload while improving efficiency in managing various health conditions. This study evaluates the impact of robot-assisted education on knowledge acquisition and metabolic control in children with Type 1 Diabetes Mellitus (T1DM) compared [...] Read more.
Robot engagement in healthcare has the potential to alleviate medical personnel workload while improving efficiency in managing various health conditions. This study evaluates the impact of robot-assisted education on knowledge acquisition and metabolic control in children with Type 1 Diabetes Mellitus (T1DM) compared to traditional education methods. A randomized controlled trial was conducted at the pediatric diabetes clinic of the University of Tabuk Medical Center, Saudi Arabia. Thirty children aged 5–15 years with T1DM were randomly divided into two groups: the robot education (intervention) group and the control education group. Both groups participated in six weekly one-hour educational sessions, with the intervention group interacting with a Pepper robot assistant and the control group receiving education from a qualified diabetes educator nurse. Knowledge was assessed using a 12-item questionnaire before and after the intervention, while metabolic control was evaluated through weekly mean home blood glucose measurements and HbA1c levels before and three months post intervention. The intervention group demonstrated a significantly greater improvement in knowledge scores compared to the control group (p < 0.05). Weekly mean blood glucose levels were consistently lower in the intervention group throughout the study period (p < 0.05 for all samples). Both groups showed a reduction in HbA1c levels after three months, with the intervention group exhibiting a greater mean decrease. The engagement of the Pepper robot in T1DM education for children resulted in improved knowledge acquisition and better metabolic control compared to traditional education methods. This approach may establish a foundation for “learning by interacting with robots” in long-term diabetes management. Further research with larger sample sizes and longer follow-up periods is warranted to confirm these findings and explore the long-term benefits of robot-assisted education in pediatric diabetes care. Full article
(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)
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19 pages, 1471 KiB  
Article
Deep Learning-Based Vision Systems for Robot Semantic Navigation: An Experimental Study
by Albandari Alotaibi, Hanan Alatawi, Aseel Binnouh, Lamaa Duwayriat, Tareq Alhmiedat and Osama Moh’d Alia
Technologies 2024, 12(9), 157; https://doi.org/10.3390/technologies12090157 - 10 Sep 2024
Cited by 1 | Viewed by 1827
Abstract
Robot semantic navigation has received significant attention recently, as it aims to achieve reliable mapping and navigation accuracy. Object detection tasks are vital in this endeavor, as a mobile robot needs to detect and recognize the objects in the area of interest to [...] Read more.
Robot semantic navigation has received significant attention recently, as it aims to achieve reliable mapping and navigation accuracy. Object detection tasks are vital in this endeavor, as a mobile robot needs to detect and recognize the objects in the area of interest to build an effective semantic map. To achieve this goal, this paper classifies and discusses recently developed object detection approaches and then presents the available vision datasets that can be employed in robot semantic navigation applications. In addition, this paper discusses several experimental studies that have validated the efficiency of object detection algorithms, including Faster R-CNN, YOLO v5, and YOLO v8. These studies also utilized a vision dataset to design and develop efficient robot semantic navigation systems, which is also discussed. According to several experiments conducted in a Fablab area, the YOLO v8 object classification model achieved the best results in terms of classification accuracy and processing speed. Full article
(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)
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22 pages, 8307 KiB  
Article
Virtual Teleoperation System for Mobile Manipulator Robots Focused on Object Transport and Manipulation
by Fernando J. Pantusin, Christian P. Carvajal, Jessica S. Ortiz and Víctor H. Andaluz
Technologies 2024, 12(9), 146; https://doi.org/10.3390/technologies12090146 - 31 Aug 2024
Viewed by 1763
Abstract
This work describes the development of a tool for the teleoperation of robots. The tool is developed in a virtual environment using the Unity graphics engine. For the development of the application, a kinematic model and a dynamic model of a mobile manipulator [...] Read more.
This work describes the development of a tool for the teleoperation of robots. The tool is developed in a virtual environment using the Unity graphics engine. For the development of the application, a kinematic model and a dynamic model of a mobile manipulator are used. The mobile manipulator robot consists of an omnidirectional platform and an anthropomorphic robotic arm with 4 degrees of freedom (4DOF). The model is essential to emulate the movements of the robot and to facilitate the immersion in the virtual environment. In addition, the control algorithms are established and developed in MATLAB 2020 software, which improves the acquisition of knowledge to teleoperate robots and execute tasks of manipulation and transport of objects. This methodology offers a cheaper and safer alternative to real physical systems, as it reduces both the costs and risks associated with using a real robot for training. Full article
(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)
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15 pages, 7044 KiB  
Article
Fast Detection of the Stick–Slip Phenomenon Associated with Wheel-to-Rail Sliding Using Acceleration Sensors: An Experimental Study
by Gabriel Popa, Mihail Andrei, Emil Tudor, Ionuț Vasile and George Ilie
Technologies 2024, 12(8), 134; https://doi.org/10.3390/technologies12080134 - 13 Aug 2024
Viewed by 4818
Abstract
The stick–slip phenomenon, the initial stage when the traction wheel starts sliding on the rail, is a critical operation that needs to be detected quickly to control the traction drive. In this study, we have developed an experimental model that uses acceleration sensors [...] Read more.
The stick–slip phenomenon, the initial stage when the traction wheel starts sliding on the rail, is a critical operation that needs to be detected quickly to control the traction drive. In this study, we have developed an experimental model that uses acceleration sensors mounted on the wheel to evaluate the amplitude of the stick–slip phenomena. These sensors can alert the driver or assist the traction control unit when a stick–slip occurs. We propose a method to reduce the amplitude of the stick–slip phenomenon using special hydraulic dampers and viscous dampers mounted on the tractive axles of the locomotive to prevent slipping during acceleration. This practical solution, validated through numerical simulation, can be readily implemented in railway systems. The paper’s findings can be used to select the necessary sensors and corresponding vibration dampers. By implementing these sliding reducers, a locomotive can significantly improve traction, apply more torque to the wheel, and increase the load of a carrier train, instilling confidence in the efficiency of the proposed solution. Full article
(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)
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Review

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46 pages, 6754 KiB  
Review
Enhancing Solar Plant Efficiency: A Review of Vision-Based Monitoring and Fault Detection Techniques
by Ioannis Polymeropoulos, Stavros Bezyrgiannidis, Eleni Vrochidou and George A. Papakostas
Technologies 2024, 12(10), 175; https://doi.org/10.3390/technologies12100175 - 26 Sep 2024
Viewed by 2184
Abstract
Over the last decades, environmental awareness has provoked scientific interest in green energy, produced, among others, from solar sources. However, for the efficient operation and longevity of green solar plants, regular inspection and maintenance are required. This work aims to review vision-based monitoring [...] Read more.
Over the last decades, environmental awareness has provoked scientific interest in green energy, produced, among others, from solar sources. However, for the efficient operation and longevity of green solar plants, regular inspection and maintenance are required. This work aims to review vision-based monitoring techniques for the fault detection of photovoltaic (PV) plants, i.e., solar panels. Practical implications of such systems include timely fault identification based on data-driven insights and problem resolution, resulting in enhanced energy outputs, extended lifetime spans for PV panels, cost savings, as well as safe and scalable inspections. Details regarding the main components of PV systems, operation principles and key non-destructive fault detection technologies are included. Advancements in unmanned aerial vehicles (UAVs), as well as in artificial intelligence (AI), machine learning (ML) and deep learning (DL) methods, offering enhanced monitoring opportunities, are in focus. A comparative analysis and an overall evaluation of state-of-the-art vision-based methods for detecting specific types of defects on PVs is conducted. The current performance and failures of vision-based algorithms for solar panel fault detection are identified, raising their capabilities, limitations and research gaps, towards effectively guiding future research. The results indicate that shading anomalies significantly impact the performance of PV units, while the top five fault detection methodologies, according to preset evaluation criteria, involve deep learning methods, such as CNNs and YOLO variations. Full article
(This article belongs to the Special Issue Advanced Autonomous Systems and Artificial Intelligence Stage)
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