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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: 20 August 2025 | Viewed by 3084

Special Issue Editors


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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

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Guest Editor
Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Interests: super abrasive machining; milling; 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 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. 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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Published Papers (2 papers)

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Research

17 pages, 5755 KiB  
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
Viewed by 995
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
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22 pages, 5189 KiB  
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
Viewed by 799
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
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