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
Gkournelos, C.; Konstantinou, C.; Angelakis, P.; Michalos, G.; Makris, S.
Enabling Seamless Human-Robot Collaboration in Manufacturing Using LLMs Proceedings Article
In: A., Wagner; K., Alexopoulos; S., Makris (Ed.): Lect. Notes Mech. Eng., pp. 81–89, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 21954356 (ISSN); 978-303157495-5 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Collaboration capabilities, Computational Linguistics, Human operator, Human-Robot Collaboration, Industrial research, Industrial robots, Intelligent robots, Language Model, Large language model, large language models, Manufacturing environments, Programming robots, Reality interface, Research papers, Robot programming, User friendly
@inproceedings{gkournelos_enabling_2024,
title = {Enabling Seamless Human-Robot Collaboration in Manufacturing Using LLMs},
author = {C. Gkournelos and C. Konstantinou and P. Angelakis and G. Michalos and S. Makris},
editor = {Wagner A. and Alexopoulos K. and Makris S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199196139&doi=10.1007%2f978-3-031-57496-2_9&partnerID=40&md5=cd0b33b3c9e9f9e53f1e99882945e134},
doi = {10.1007/978-3-031-57496-2_9},
isbn = {21954356 (ISSN); 978-303157495-5 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Lect. Notes Mech. Eng.},
pages = {81–89},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In the era of Industry 5.0, there is a growing interest in harnessing the potential of human-robot collaboration (HRC) in manufacturing environments. This research paper focuses on the integration of Large Language Models (LLMs) to augment HRC capabilities, particularly in addressing configuration issues when programming robots to collaborate with human operators. By harnessing the capabilities of LLMs in combination with a user-friendly augmented reality (AR) interface, the proposed approach empowers human operators to seamlessly collaborate with robots, facilitating smooth and efficient assembly processes. This research introduces the CollabAI an AI assistant for task management and natural communication based on a fine-tuned GPT model focusing on collaborative manufacturing. Real-world experiments conducted in two manufacturing settings coming from the automotive and machinery industries. The findings have implications for various industries seeking to increase productivity and foster a new era of efficient and effective collaboration in manufacturing environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Artificial intelligence, Augmented Reality, Collaboration capabilities, Computational Linguistics, Human operator, Human-Robot Collaboration, Industrial research, Industrial robots, Intelligent robots, Language Model, Large language model, large language models, Manufacturing environments, Programming robots, Reality interface, Research papers, Robot programming, User friendly},
pubstate = {published},
tppubtype = {inproceedings}
}
In the era of Industry 5.0, there is a growing interest in harnessing the potential of human-robot collaboration (HRC) in manufacturing environments. This research paper focuses on the integration of Large Language Models (LLMs) to augment HRC capabilities, particularly in addressing configuration issues when programming robots to collaborate with human operators. By harnessing the capabilities of LLMs in combination with a user-friendly augmented reality (AR) interface, the proposed approach empowers human operators to seamlessly collaborate with robots, facilitating smooth and efficient assembly processes. This research introduces the CollabAI an AI assistant for task management and natural communication based on a fine-tuned GPT model focusing on collaborative manufacturing. Real-world experiments conducted in two manufacturing settings coming from the automotive and machinery industries. The findings have implications for various industries seeking to increase productivity and foster a new era of efficient and effective collaboration in manufacturing environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Xi, M.; Perera, M.; Matthews, B.; Wang, R.; Weiley, V.; Somarathna, R.; Maqbool, H.; Chen, J.; Engelke, U.; Anderson, S.; Adcock, M.; Thomas, B. H.
Towards Immersive AI Proceedings Article
In: Eck, U.; Sra, M.; Stefanucci, J.; Sugimoto, M.; Tatzgern, M.; Williams, I. (Ed.): Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct, pp. 260–264, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 9798331506919 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Data visualization, Decision making, Heterogenous data, Immersive, Immersive analytic, Immersive analytics, Industrial research, Mixed reality, Neuro-symbolic system, Real- time, Scientific paradigm, Situated imaging., Time-interleaved, Visual analytics, Work-flows
@inproceedings{xi_towards_2024,
title = {Towards Immersive AI},
author = {M. Xi and M. Perera and B. Matthews and R. Wang and V. Weiley and R. Somarathna and H. Maqbool and J. Chen and U. Engelke and S. Anderson and M. Adcock and B. H. Thomas},
editor = {U. Eck and M. Sra and J. Stefanucci and M. Sugimoto and M. Tatzgern and I. Williams},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214375967&doi=10.1109%2FISMAR-Adjunct64951.2024.00062&partnerID=40&md5=f654c0b188420a6e81288aa842a7e88b},
doi = {10.1109/ISMAR-Adjunct64951.2024.00062},
isbn = {9798331506919 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real. Adjunct, ISMAR-Adjunct},
pages = {260–264},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {With every shift in scientific paradigms comes not only a new way of seeing the world, but as Kunh argues, new tools for seeing [13]. Today, generative AI and neuro-symbolic systems show signs of changing how science functions, making it possible to synthesise complex heterogenous data in real time, interleaved with complex and situated workflows. But the new tools are not yet fully formed. To realise the opportunities and meet the challenges posed by the growth of generative AI for science and other knowledge work requires us to look beyond improvements in algorithms. The decision-making landscape for information workers has drastically changed, and the pressing need for analysts and experts to collaborate with AI in complex, high-tempo data environments has never been more evident.To bring strategic focus to these challenges in ways that will enable social, environmental and economic benefits for all, CSIRO's Data61 (the data and digital specialist arm of the Commonwealth Scientific and Industrial Research Organisation - Australia's national science agency) has established the Immersive AI Research Cluster. The cluster allows more than 30 research scientists and engineers to focus on defining a broad range of scientific disciplines for people to work with and understand the information provided by AI, such as data visualisation, visual analytics, connecting remote people, through immersive technologies like virtual and augmented reality. This workshop paper presents the trending research directions and challenges that emerged from this research cluster, which are closely linked to the scientific domains and illustrated through use cases. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Artificial intelligence, Augmented Reality, Data visualization, Decision making, Heterogenous data, Immersive, Immersive analytic, Immersive analytics, Industrial research, Mixed reality, Neuro-symbolic system, Real- time, Scientific paradigm, Situated imaging., Time-interleaved, Visual analytics, Work-flows},
pubstate = {published},
tppubtype = {inproceedings}
}
With every shift in scientific paradigms comes not only a new way of seeing the world, but as Kunh argues, new tools for seeing [13]. Today, generative AI and neuro-symbolic systems show signs of changing how science functions, making it possible to synthesise complex heterogenous data in real time, interleaved with complex and situated workflows. But the new tools are not yet fully formed. To realise the opportunities and meet the challenges posed by the growth of generative AI for science and other knowledge work requires us to look beyond improvements in algorithms. The decision-making landscape for information workers has drastically changed, and the pressing need for analysts and experts to collaborate with AI in complex, high-tempo data environments has never been more evident.To bring strategic focus to these challenges in ways that will enable social, environmental and economic benefits for all, CSIRO's Data61 (the data and digital specialist arm of the Commonwealth Scientific and Industrial Research Organisation - Australia's national science agency) has established the Immersive AI Research Cluster. The cluster allows more than 30 research scientists and engineers to focus on defining a broad range of scientific disciplines for people to work with and understand the information provided by AI, such as data visualisation, visual analytics, connecting remote people, through immersive technologies like virtual and augmented reality. This workshop paper presents the trending research directions and challenges that emerged from this research cluster, which are closely linked to the scientific domains and illustrated through use cases. © 2025 Elsevier B.V., All rights reserved.