On the role of Artificial Intelligence methods in modern force-controlled manufacturing robotic tasks

• URL: http://arxiv.org/abs/2409.16828v1
• Date: Wed, 25 Sep 2024 11:29:26 GMT
• Title: On the role of Artificial Intelligence methods in modern force-controlled manufacturing robotic tasks
• Authors: Vincenzo Petrone, Enrico Ferrentino, Pasquale Chiacchio,
• Abstract summary: AI's role in enhancing robotic manipulators is rapidly leading to significant innovations in smart manufacturing. This article is to frame these innovations in practical force-controlled applications, highlighting their necessity for maintaining high-quality production standards. The analysis concludes with a perspective on future research directions, emphasizing the need for common performance metrics to validate AI techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This position paper explores the integration of Artificial Intelligence (AI) into force-controlled robotic tasks within the scope of advanced manufacturing, a cornerstone of Industry 4.0. AI's role in enhancing robotic manipulators - key drivers in the Fourth Industrial Revolution - is rapidly leading to significant innovations in smart manufacturing. The objective of this article is to frame these innovations in practical force-controlled applications - e.g. deburring, polishing, and assembly tasks like peg-in-hole (PiH) - highlighting their necessity for maintaining high-quality production standards. By reporting on recent AI-based methodologies, this article contrasts them and identifies current challenges to be addressed in future research. The analysis concludes with a perspective on future research directions, emphasizing the need for common performance metrics to validate AI techniques, integration of various enhancements for performance optimization, and the importance of validating them in relevant scenarios. These future directions aim to provide consistency with already adopted approaches, so as to be compatible with manufacturing standards, increasing the relevance of AI-driven methods in both academic and industrial contexts.

Related papers

• AI Horizon Scanning -- White Paper p3395, IEEE-SA. Part III: Technology Watch: a selection of key developments, emerging technologies, and industry trends in Artificial Intelligence [0.3277163122167434]
  Generative Artificial Intelligence (AI) technologies are in a phase of unprecedented rapid development following the landmark release of Chat-GPT. As the deployment of AI products rises geometrically, considerable attention is being given to the threats and opportunities that AI technologies offer. This manuscript is the third of a series of White Papers informing the development of IEEE-SA's p3995 it Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence Models'
  arXiv  Detail & Related papers  (2024-11-05T19:04:42Z)
• Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report [12.204470166456561]
  Generative AI shows significant potential in health economics and outcomes research (HEOR) Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration.
  arXiv  Detail & Related papers  (2024-10-26T15:42:50Z)
• Generative AI Application for Building Industry [10.154329382433213]
  This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs) in the building industry. The research highlights how LLMs can automate labor-intensive processes, significantly improving efficiency, accuracy, and safety in building practices.
  arXiv  Detail & Related papers  (2024-10-01T21:59:08Z)
• Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process [15.471884798655063]
  The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR) This survey paper explores the integration of AI within the OR process (AI4OR) to enhance its effectiveness and efficiency across multiple stages. The synergy between AI and OR is poised to drive significant advancements and novel solutions in a multitude of domains.
  arXiv  Detail & Related papers  (2024-01-06T15:55:14Z)
• Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review [1.6006550105523192]
  Review explores the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs) Examines both foundational and advanced methodologies of prompt engineering, including techniques such as self-consistency, chain-of-thought, and generated knowledge. Review also reflects the essential role of prompt engineering in advancing AI capabilities, providing a structured framework for future research and application.
  arXiv  Detail & Related papers  (2023-10-23T09:15:18Z)
• Machine Learning Meets Advanced Robotic Manipulation [48.6221343014126]
  The paper reviews cutting edge technologies and recent trends on machine learning methods applied to real-world manipulation tasks. The rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue.
  arXiv  Detail & Related papers  (2023-09-22T01:06:32Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Deep Active Learning for Computer Vision: Past and Future [50.19394935978135]
  Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies.
  arXiv  Detail & Related papers  (2022-11-27T13:07:14Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes [56.65379135797867]
  We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
  arXiv  Detail & Related papers  (2020-08-29T14:57:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.