Augmenting cobots for sheet-metal SMEs with 3D object recognition and localisation

• URL: http://arxiv.org/abs/2508.13964v1
• Date: Tue, 19 Aug 2025 15:56:06 GMT
• Title: Augmenting cobots for sheet-metal SMEs with 3D object recognition and localisation
• Abstract summary: The COOCK+ ROBUST project aims to transform cobots into mobile and reconfigurable production assistants.<n>This article explores both the opportunities and challenges of enhancing cobotic systems with these technologies in an industrial setting.
• Score: 1.1999555634662633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to high-mix-low-volume production, sheet-metal workshops today are challenged by small series and varying orders. As standard automation solutions tend to fall short, SMEs resort to repetitive manual labour impacting production costs and leading to tech-skilled workforces not being used to their full potential. The COOCK+ ROBUST project aims to transform cobots into mobile and reconfigurable production assistants by integrating existing technologies, including 3D object recognition and localisation. This article explores both the opportunities and challenges of enhancing cobotic systems with these technologies in an industrial setting, outlining the key steps involved in the process. Additionally, insights from a past project, carried out by the ACRO research unit in collaboration with an industrial partner, serves as a concrete implementation example throughout.

Related papers

• Perspective: Towards sustainable exploration of chemical spaces with machine learning [49.29528647472741]
  We argue that sustainable progress will rely on open data and models, reusable datasets, and domain-specific AI systems.<n>We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning.
  arXiv  Detail & Related papers  (2026-03-31T13:29:20Z)
• Proposing a Framework for Machine Learning Adoption on Legacy Systems [1.675857332621569]
  The integration of machine learning (ML) is critical for industrial competitiveness, yet its adoption is frequently stalled by the prohibitive costs and operational disruptions of upgrading legacy systems.<n>This paper introduces a pragmatic, API-based framework designed to overcome these challenges by strategically decoupling the ML model lifecycle from the production environment.
  arXiv  Detail & Related papers  (2025-09-29T03:08:23Z)
• OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System [61.12400636463362]
  OnePiece is a unified framework that seamlessly integrates LLM-style context engineering and reasoning into both retrieval and ranking models.<n>OnePiece has been deployed in the main personalized search scenario of Shopee and achieves consistent online gains across different key business metrics.
  arXiv  Detail & Related papers  (2025-09-22T17:59:07Z)
• A Cost-Benefit Analysis of Additive Manufacturing as a Service [0.0]
  The global manufacturing landscape is undergoing a fundamental shift from resource-intensive mass production to sustainable, localised manufacturing.<n>This paper analyses a Cloud Crafting Platform that enables Manufacturing as a Service through additive manufacturing technologies.<n>We present the platform's Service-Oriented Architecture (SOA), deployment on the Microsoft Azure cloud, and integration with three different 3D printer models in a testbed environment.
  arXiv  Detail & Related papers  (2025-02-08T14:22:55Z)
• Large Language Models for Manufacturing [41.12098478080648]
  Large Language Models (LLMs) have the potential to transform manufacturing industry, offering new opportunities to optimize processes, improve efficiency, and drive innovation. This paper focuses on the integration of LLMs into the manufacturing domain, focusing on their potential to automate and enhance various aspects of manufacturing.
  arXiv  Detail & Related papers  (2024-10-28T18:13:47Z)
• Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
  We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning. Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques. Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
  arXiv  Detail & Related papers  (2024-05-30T23:20:23Z)
• Towards the implementation of Industry 4.0: A methodology-based approach oriented to the customer life cycle [0.05524804393257919]
  We present two contributions that can help the software engineers of those SMEs to incorporate Industry 4.0 technologies in the context of the customer life cycle. The first contribution is a methodology that can help those software engineers in the task of creating new software services, aligned with Industry 4.0. The second contribution is a system developed for a real manufacturing scenario, using the proposed methodology.
  arXiv  Detail & Related papers  (2024-01-31T08:31:08Z)
• Large-Scale Multi-Robot Assembly Planning for Autonomous Manufacturing [45.270764017583346]
  Mobile autonomous robots have the potential to revolutionize manufacturing processes.<n> employing large robot fleets in manufacturing requires addressing challenges including collision-free movement in a shared workspace.<n>We propose a full algorithmic stack for large-scale multi-robot assembly planning that addresses these challenges and can synthesize construction plans for complex assemblies with thousands of parts in a matter of minutes.
  arXiv  Detail & Related papers  (2023-10-31T23:42:14Z)
• 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)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT [9.477629856092218]
  This paper presents a framework called Semantic Low-Code Engineering for ML Applications (SeLoC-ML) SeLoC-ML enables non-experts to model, discover, reuse, and matchmake ML models and devices at scale. Developers can benefit from semantic application templates, called recipes, to fast prototype end-user applications.
  arXiv  Detail & Related papers  (2022-07-18T13:06:21Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)

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.