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Applied Sciences
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Integrating AI into Mechatronics and Robotics: Innovations and Applications
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Integrating AI into Mechatronics and Robotics: Innovations and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 18556

Special Issue Editors

Prof. Dr. Gómez-Escudero Gaizka

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Plaza Torres Quevedo s/n, 48013 Bilbao, Spain
Interests: mechanical engineering; path planning algorithms; additive manufacturing; sensing
Dr. Amaia Calleja-Ochoa

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Interests: machining processes control and optimization; additive manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Haizea González-Barrio

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Alameda de Urquijo S/N, 48013 Bilbao, Spain
Interests: manufacturing engineering; industrial engineering; mechanical engineering; hybrid manufacturing; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) into Mechatronics and Robotics is revolutionizing these fields by enabling innovative applications and significant advancements. This Special Issue will present cutting-edge research and experimental results in this rapidly evolving area, covering a broad range of topics from AI algorithms and intelligent control systems to practical applications in various industries.

Key areas of interest include, but are not limited to, the development and application of deep learning and machine learning algorithms for improving robot control and decision-making. Innovations in computer vision and pattern recognition are also crucial for enhancing the perception and interaction capabilities of robots in dynamic environments. The design and implementation of autonomous systems and collaborative robots (cobots) in industrial settings present unique challenges and opportunities, particularly for ensuring safety and efficiency in human–robot interactions.

Further topics include the optimization and intelligent control of mechatronic systems through the integration of smart sensors and AI-based feedback mechanisms. Case studies demonstrating the practical applications of AI in manufacturing, medical robotics, autonomous transportation, and logistics will provide valuable insights into the current state and potential of these technologies.

The development of tools and platforms for AI in robotics, including simulation environments and standardization efforts, is essential for fostering innovation and ensuring the reliability of AI-driven systems. Additionally, research on predictive maintenance and fault detection using AI in mechatronic systems highlights the importance of integrating advanced data analytics into traditional engineering disciplines.

This Special Issue will gather contributions from researchers offering comprehensive overviews of the latest innovations and applications related to integrating AI into Mechatronics and Robotics, paving the way for future advancements in these transformative technologies.

This Special Issue will publish high-quality, original research papers in the following overlapping fields:

  1. Innovations in Artificial Intelligence Applied to Robotics
  • Deep learning algorithms for improving robot control;
  • Advanced computer vision systems for robot navigation and manipulation;
  • Artificial intelligence for autonomous decision-making in robots.
  1. Autonomous Systems and Collaborative Robots
  • Design and development of collaborative robots (cobots) in industrial settings;
  • Implementation of AI for cooperation between robots and humans;
  • Safety and efficiency in human–robot interaction through AI.
  1. Optimization and Intelligent Control in Mechatronic Systems
  • Intelligent controllers for mechatronic systems;
  • Optimization algorithms for enhancing the performance of mechatronic systems;
  • Integration of smart sensors and AI-based feedback systems.
  1. Practical Applications of AI in Mechatronics and Robotics
  • Case studies of AI implementation in manufacturing industries;
  • Use of AI in medical and rehabilitation robotics;
  • AI in autonomous transportation systems and logistics.
  1. Development of Tools and Platforms for AI in Robotics
  • Simulation platforms and testing environments for AI-based robotics;
  • Development tools for implementing AI in mechatronic systems;

Standardization and best practices in AI software development for robotics

Prof. Dr. Gómez-Escudero Gaizka
Dr. Amaia Calleja-Ochoa
Dr. Haizea González-Barrio
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. Applied Sciences 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

  • collaborative robots
  • autonomous systems
  • reinforcement learning
  • predictive maintenance
  • smart sensors
  • AI-based decision-making
  • robotic vision systems
  • AI and robotics integration

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

Published Papers (5 papers)

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Research

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22 pages, 13466 KB  
Article
On-Premise Multimodal AI Assistance for Operator-in-the-Loop Diagnosis in Machine Tool Mechatronic Systems
by Seongwoo Cho, Jongsu Park and Jumyung Um
Appl. Sci. 2026, 16(7), 3166; https://doi.org/10.3390/app16073166 - 25 Mar 2026
Viewed by 262
Abstract
Modern machine tools are safety-critical mechatronic systems, yet shop floor maintenance from abnormal events still relies heavily on scarce expert know-how and time-consuming manual searches across heterogeneous controller documentation. This paper presents an on-premise multimodal AI assistant. It integrates large language models with [...] Read more.
Modern machine tools are safety-critical mechatronic systems, yet shop floor maintenance from abnormal events still relies heavily on scarce expert know-how and time-consuming manual searches across heterogeneous controller documentation. This paper presents an on-premise multimodal AI assistant. It integrates large language models with retrieval augmented generation and real-time machine signals to support operator-in-the-loop fault diagnosis. The proposed system provides three tightly coupled functions: (1) alarm-grounded guidance, which answers controller alarms and recommends corrective actions by grounding generation on manuals, maintenance procedures, and historical alarm cases; (2) parameter-aware reasoning, which injects live process and health indicators (e.g., spindle temperature, vibration, and axis states) into the reasoning context through an industrial data pipeline, enabling context specific troubleshooting; and (3) vision enabled support, which retrieves similar visual cases and generates concise visual instructions when text alone is insufficient. The assistant is deployed within an intranet environment to satisfy industrial security and privacy requirements and is orchestrated via lightweight tool calling for seamless integration with existing shop floor systems. Experiments on real machine tool alarm scenarios demonstrate that the proposed system achieves 82% answer correctness for alarm Q&A and improves response consistency and time-to-resolution compared with baseline keyword search and template-based guidance. The results suggest that grounded, multimodal chatbot assistants can act as practical AI-based feedback and decision support mechanisms for mechatronic production equipment, bridging human skill gaps while enhancing reliability and maintainability. Full article
(This article belongs to the Special Issue Integrating AI into Mechatronics and Robotics: Innovations and Applications)
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17 pages, 5755 KB  
Article
A Hybrid Architecture for Safe Human–Robot Industrial Tasks
by Gaetano Lettera, Daniele Costa and Massimo Callegari
Appl. Sci. 2025, 15(3), 1158; https://doi.org/10.3390/app15031158 - 24 Jan 2025
Cited by 6 | Viewed by 2801
Abstract
In the context of Industry 5.0, human–robot collaboration (HRC) is increasingly crucial for enabling safe and efficient operations in shared industrial workspaces. This study aims to implement a hybrid robotic architecture based on the Speed and Separation Monitoring (SSM) collaborative scenario defined in [...] Read more.
In the context of Industry 5.0, human–robot collaboration (HRC) is increasingly crucial for enabling safe and efficient operations in shared industrial workspaces. This study aims to implement a hybrid robotic architecture based on the Speed and Separation Monitoring (SSM) collaborative scenario defined in ISO/TS 15066. The system calculates the minimum protective separation distance between the robot and the operators and slows down or stops the robot according to the risk assessment computed in real time. Compared to existing solutions, the approach prevents collisions and maximizes workcell production by reducing the robot speed only when the calculated safety index indicates an imminent risk of collision. The proposed distributed software architecture utilizes the ROS2 framework, integrating three modules: (1) a fast and reliable human tracking module based on the OptiTrack system that considerably reduces latency times or false positives, (2) an intention estimation (IE) module, employing a linear Kalman filter (LKF) to predict the operator’s next position and velocity, thus considering the current scenario and not the worst case, and (3) a robot control module that computes the protective separation distance and assesses the safety index by measuring the Euclidean distance between operators and the robot. This module dynamically adjusts robot speed to maintain safety while minimizing unnecessary slowdowns, ensuring the efficiency of collaborative tasks. Experimental results demonstrate that the proposed system effectively balances safety and speed, optimizing overall performance in human–robot collaborative industrial environments, with significant improvements in productivity and reduced risk of accidents. Full article
(This article belongs to the Special Issue Integrating AI into Mechatronics and Robotics: Innovations and Applications)
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22 pages, 5189 KB  
Article
Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling
by Yeonjin Park and Jungpyo Hong
Appl. Sci. 2025, 15(1), 92; https://doi.org/10.3390/app15010092 - 26 Dec 2024
Cited by 3 | Viewed by 2933
Abstract
This paper proposes a novel autoencoder-based neural network for compressing and reconstructing underwater acoustic signals collected by Directional Frequency Analysis and Recording sonobuoys. To improve both signal compression rates and reconstruction performance, we integrate Residual Vector Quantization and a Compensation Module into the [...] Read more.
This paper proposes a novel autoencoder-based neural network for compressing and reconstructing underwater acoustic signals collected by Directional Frequency Analysis and Recording sonobuoys. To improve both signal compression rates and reconstruction performance, we integrate Residual Vector Quantization and a Compensation Module into the decoding process to effectively compensate for quantization errors. Additionally, an unstructured pruning technique is applied to the encoder to minimize computational load and parameters, addressing the battery limitations of sonobuoys. Experimental results demonstrate that the proposed method reduces the data transmission size by approximately 31.25% compared to the conventional autoencoder-based method. Moreover, the spectral mean square errors are reduced by 60.58% for continuous wave signals and 55.25% for linear frequency modulation signals under realistic air channel simulations. Full article
(This article belongs to the Special Issue Integrating AI into Mechatronics and Robotics: Innovations and Applications)
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Review

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30 pages, 2023 KB  
Review
Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
by Yuval Cohen, Amir Biton and Shraga Shoval
Appl. Sci. 2025, 15(14), 7905; https://doi.org/10.3390/app15147905 - 15 Jul 2025
Cited by 12 | Viewed by 7251
Abstract
The integration of advanced computer vision and artificial intelligence (AI) techniques into collaborative robotic systems holds the potential to revolutionize human–robot interaction, productivity, and safety. Despite substantial research activity, a systematic synthesis of how vision and AI are jointly enabling context-aware, adaptive cobot [...] Read more.
The integration of advanced computer vision and artificial intelligence (AI) techniques into collaborative robotic systems holds the potential to revolutionize human–robot interaction, productivity, and safety. Despite substantial research activity, a systematic synthesis of how vision and AI are jointly enabling context-aware, adaptive cobot capabilities across perception, planning, and decision-making remains lacking (especially in recent years). Addressing this gap, our review unifies the latest advances in visual recognition, deep learning, and semantic mapping within a structured taxonomy tailored to collaborative robotics. We examine foundational technologies such as object detection, human pose estimation, and environmental modeling, as well as emerging trends including multimodal sensor fusion, explainable AI, and ethically guided autonomy. Unlike prior surveys that focus narrowly on either vision or AI, this review uniquely analyzes their integrated use for real-world human–robot collaboration. Highlighting industrial and service applications, we distill the best practices, identify critical challenges, and present key performance metrics to guide future research. We conclude by proposing strategic directions—from scalable training methods to interoperability standards—to foster safe, robust, and proactive human–robot partnerships in the years ahead. Full article
(This article belongs to the Special Issue Integrating AI into Mechatronics and Robotics: Innovations and Applications)
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Other

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36 pages, 4843 KB  
Systematic Review
Industrial Robotics and Adaptive Control Systems in STEM Education: Systematic Review of Technology Transfer from Industry to Classroom and Competency Development Framework
by Claudio Urrea
Appl. Sci. 2026, 16(4), 2026; https://doi.org/10.3390/app16042026 - 18 Feb 2026
Viewed by 517
Abstract
The Fourth Industrial Revolution reshapes manufacturing and workforce demands, yet a persistent gap remains between industry needs and engineering education. While proficiency in industrial robotics, adaptive control, and automation becomes critical, traditional education struggles to bridge the theory–practice divide. This systematic review examines [...] Read more.
The Fourth Industrial Revolution reshapes manufacturing and workforce demands, yet a persistent gap remains between industry needs and engineering education. While proficiency in industrial robotics, adaptive control, and automation becomes critical, traditional education struggles to bridge the theory–practice divide. This systematic review examines technology transfer from factory to classroom to develop authentic Industry 4.0 competencies. Following PRISMA 2020 guidelines, we synthesized 52 empirical studies (2019–2025) focusing on technology complexity, pedagogical approaches, and learning outcomes. Random-effects meta-analysis of 12 representative studies reveals large positive effects: Hedges’ g of 0.786 (95% CI: 0.726–0.846, p < 0.001) with homogeneous effects (I2 = 0.00%, p = 0.464), indicating robust generalizability. However, critical gaps emerged: only 7.7% employ actual industrial manipulators versus educational kits, adaptive control pedagogy remains limited, and fault-tolerant systems teaching receives minimal attention. Technology complexity analysis reveals clear progression from educational kits through semi-industrial platforms to industrial systems, with significant differential effects on transferable skills (r = 0.68, p < 0.001). This study proposes the ARC Framework integrating technology taxonomy, competency progression, pedagogical strategies, and assessment rubrics. Cost–effectiveness analysis demonstrates remote labs optimize impact-per-investment ratios ($45 vs. $280 per student), providing an evidence-based framework for technology transfer in engineering education. Full article
(This article belongs to the Special Issue Integrating AI into Mechatronics and Robotics: Innovations and Applications)
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