AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Jayanthy, S.; Selvaganesh, M.; Kumar, S. Sakthi; Sathish, A. Manjunatha; Sabarisan, K. M.; Arasi, T. Senthamil
Generative AI Solution for CNC Machines and Robotics Code Generation Proceedings Article
In: Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331536695 (ISBN).
Abstract | Links | BibTeX | Tags: Adaptive control systems, Adversarial networks, Automated Code Generation, Automatic programming, CNC machine, CNC Machines, CNC system, Codegeneration, Computer aided instruction, Computer control, Computer control systems, E-Learning, Edge computing, Federated learning, Flow control, GANs, Generative pre-trained transformer transformer, GPT Transformers, Industrial research, Industry 4.0, Innovative approaches, Intelligent robots, Learning algorithms, Personnel training, Reinforcement Learning, Reinforcement learnings, Robotic systems, Simulation platform, Smart manufacturing, Virtual Reality
@inproceedings{jayanthy_generative_2025,
title = {Generative AI Solution for CNC Machines and Robotics Code Generation},
author = {S. Jayanthy and M. Selvaganesh and S. Sakthi Kumar and A. Manjunatha Sathish and K. M. Sabarisan and T. Senthamil Arasi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105011963078&doi=10.1109%2FICCIES63851.2025.11033032&partnerID=40&md5=fb9143cd22dc48ae6c557f722cc2d6ab},
doi = {10.1109/ICCIES63851.2025.11033032},
isbn = {9798331536695 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The advent of Industry 4.0 has revolutionized the manufacturing landscape, driving significant advancements in automation and intelligence. This study introduces an innovative approach to automated code generation for CNC and robotic systems, leveraging Generative Adversarial Networks (GANs) and GPT(Generative Pre-trained Transformer) Transformers. These AI models enable precise and optimized code creation, minimizing manual errors. Adaptive process control, achieved through Reinforcement Learning (RL), allows real-time adjustments to operational parameters, enhancing performance in dynamic environments. The incorporation of natural language processing through Transformer models facilitates intuitive operator interactions via user-friendly interfaces. Immersive Virtual Reality (VR) technologies provide high-fidelity simulation and training platforms for realistic testing and control. Additionally, collaborative learning mechanisms, achieved through Federated Learning and Edge-cloud computing, support continuous improvement and scalable deployment. Impressive outcomes were attained by the system, including 90.5% training efficiency, 98.7% coding accuracy, 95.2% adaptability, and 93.4% operator satisfaction. Experimental results validate the system's superior accuracy, adaptability, and user-centric design, showcasing its potential to revolutionize manufacturing processes. This research sets a new benchmark for intelligent, efficient, and scalable automation in the Industry 4.0 era, paving the way for transformative innovations in smart manufacturing. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Adaptive control systems, Adversarial networks, Automated Code Generation, Automatic programming, CNC machine, CNC Machines, CNC system, Codegeneration, Computer aided instruction, Computer control, Computer control systems, E-Learning, Edge computing, Federated learning, Flow control, GANs, Generative pre-trained transformer transformer, GPT Transformers, Industrial research, Industry 4.0, Innovative approaches, Intelligent robots, Learning algorithms, Personnel training, Reinforcement Learning, Reinforcement learnings, Robotic systems, Simulation platform, Smart manufacturing, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The advent of Industry 4.0 has revolutionized the manufacturing landscape, driving significant advancements in automation and intelligence. This study introduces an innovative approach to automated code generation for CNC and robotic systems, leveraging Generative Adversarial Networks (GANs) and GPT(Generative Pre-trained Transformer) Transformers. These AI models enable precise and optimized code creation, minimizing manual errors. Adaptive process control, achieved through Reinforcement Learning (RL), allows real-time adjustments to operational parameters, enhancing performance in dynamic environments. The incorporation of natural language processing through Transformer models facilitates intuitive operator interactions via user-friendly interfaces. Immersive Virtual Reality (VR) technologies provide high-fidelity simulation and training platforms for realistic testing and control. Additionally, collaborative learning mechanisms, achieved through Federated Learning and Edge-cloud computing, support continuous improvement and scalable deployment. Impressive outcomes were attained by the system, including 90.5% training efficiency, 98.7% coding accuracy, 95.2% adaptability, and 93.4% operator satisfaction. Experimental results validate the system's superior accuracy, adaptability, and user-centric design, showcasing its potential to revolutionize manufacturing processes. This research sets a new benchmark for intelligent, efficient, and scalable automation in the Industry 4.0 era, paving the way for transformative innovations in smart manufacturing. © 2025 Elsevier B.V., All rights reserved.
2024
Peretti, A.; Mazzola, M.; Capra, L.; Piazzola, M.; Carlevaro, C.
Seamless Human-Robot Interaction Through a Distributed Zero-Trust Architecture and Advanced User Interfaces Proceedings Article
In: C., Secchi; L., Marconi (Ed.): Springer. Proc. Adv. Robot., pp. 92–95, Springer Nature, 2024, ISBN: 25111256 (ISSN); 978-303176427-1 (ISBN).
Abstract | Links | BibTeX | Tags: Advanced user interfaces, Digital Twins, HRC, Human Robot Interaction, Human-Robot Collaboration, Humans-robot interactions, Industrial robots, Industry 4.0, Intelligent robots, Interaction platform, Language Model, Large language model, LLM, Problem oriented languages, Robot Operating System, Robot operating system 2, Robot-robot collaboration, ROS2, RRC, Wages, XR, ZTA
@inproceedings{peretti_seamless_2024,
title = {Seamless Human-Robot Interaction Through a Distributed Zero-Trust Architecture and Advanced User Interfaces},
author = {A. Peretti and M. Mazzola and L. Capra and M. Piazzola and C. Carlevaro},
editor = {Secchi C. and Marconi L.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216090556&doi=10.1007%2f978-3-031-76428-8_18&partnerID=40&md5=9f58281f8a8c034fb45fed610ce64bd2},
doi = {10.1007/978-3-031-76428-8_18},
isbn = {25111256 (ISSN); 978-303176427-1 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Springer. Proc. Adv. Robot.},
volume = {33 SPAR},
pages = {92–95},
publisher = {Springer Nature},
abstract = {The proposed work presents a novel interaction platform designed to address the shortage of skilled workers in the labor market, facilitating the seamless integration of robotics and advanced user interfaces such as eXtended Reality (XR) to optimize Human-Robot Collaboration (HRC) as well as Robot-Robot Collaboration (RRC) in an Industry 4.0 scenario. One of the most challenging situations is to optimize and simplify the collaborations of humans and robots to decrease or avoid system slowdowns, blocks, or dangerous situations for both users and robots. The advent of the LLMs (Large Language Model) have been breakthrough the whole IT environment because they perform well in different scenario from human text generation to autonomous systems management. Due to their malleability, LLMs have a primary role for Human-Robot collaboration processes. For this reason, the platform comprises three key technical components: a distributed zero-trust architecture, a virtual avatar, and digital twins of robots powered by the Robot Operating System 2 (ROS2) platform. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Advanced user interfaces, Digital Twins, HRC, Human Robot Interaction, Human-Robot Collaboration, Humans-robot interactions, Industrial robots, Industry 4.0, Intelligent robots, Interaction platform, Language Model, Large language model, LLM, Problem oriented languages, Robot Operating System, Robot operating system 2, Robot-robot collaboration, ROS2, RRC, Wages, XR, ZTA},
pubstate = {published},
tppubtype = {inproceedings}
}
The proposed work presents a novel interaction platform designed to address the shortage of skilled workers in the labor market, facilitating the seamless integration of robotics and advanced user interfaces such as eXtended Reality (XR) to optimize Human-Robot Collaboration (HRC) as well as Robot-Robot Collaboration (RRC) in an Industry 4.0 scenario. One of the most challenging situations is to optimize and simplify the collaborations of humans and robots to decrease or avoid system slowdowns, blocks, or dangerous situations for both users and robots. The advent of the LLMs (Large Language Model) have been breakthrough the whole IT environment because they perform well in different scenario from human text generation to autonomous systems management. Due to their malleability, LLMs have a primary role for Human-Robot collaboration processes. For this reason, the platform comprises three key technical components: a distributed zero-trust architecture, a virtual avatar, and digital twins of robots powered by the Robot Operating System 2 (ROS2) platform. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.