Refine
Year of publication
Document Type
- conference proceeding (article) (21)
- Article (10)
- Part of Periodical (3)
- Part of a Book (1)
Is part of the Bibliography
- no (35)
Keywords
- Robots (3)
- Activity recognition (2)
- Algorithmus (2)
- Collaboration (2)
- FORCE (2)
- Funktionssicherheit (2)
- Manipulator (2)
- Mensch-Maschine-System (2)
- Mensch-Roboter-Kooperation (2)
- Muscles (2)
Institute
- Fakultät Maschinenbau (26)
- Fakultät Informatik und Mathematik (7)
- Fakultät Sozial- und Gesundheitswissenschaften (6)
- Institut für Sozialforschung und Technikfolgenabschätzung (IST) (6)
- Labor Empirische Sozialforschung (6)
- Labor für Technikfolgenabschätzung und Angewandte Ethik (LaTe) (6)
- Hochschulleitung/Hochschulverwaltung (3)
- Zentrum für Forschung und Transfer (ZFT ab 2024; vorher: IAFW) (3)
- Fakultät Elektro- und Informationstechnik (2)
- Research Center of Biomedical Engineering - RCBE (2)
Begutachtungsstatus
- peer-reviewed (17)
Manually defined control limits remain a common strategy for quality control in manufacturing due to their ease of deployment on the shop floor compared to more advanced data analysis approaches. Despite their continued importance, there is no systematic method of defining these control limits. However, sub-optimal control limits can lead to undetected faults or cause unnecessary interruption to production. This manuscript presents an algorithm that systematizes this manual process into an efficient search task. We conceptualized the search task as a sequence of sub-problems that are based on the conventional steps taken by process experts when defining control limits. This algorithm can be integrated into an expert tool for shop floor personnel to automate the definition of control limits in annotated time series data. We demonstrate the efficacy of the control limits found by our algorithm by comparing them to those manually defined by process experts in real-world process data from the automotive industry. Furthermore, we show that our algorithm generalizes to traditional time series classification problems and achieves state-of-the-art performance on selected benchmark datasets. Our work is the first effort in automating the otherwise manual definition of control limits for fault detection.
Forschungsbericht 2013
(2014)
Forschungsbericht 2015
(2015)
Forschungsbericht 2011 / Hochschule für Angewandte Wissenschaften - Fachhochschule Regensburg
(2011)
Smart Workbench
(2016)
Smart Workbench
(2017)
Collaboration between robots and humans requires communicative skills on both sides. The robot has to understand the conscious and unconscious activities of human workers. Many state-of-the-art activity recognition algorithms with high performance rates on existing benchmark datasets are available for this task. This paper re-evaluates appropriate architectures in light of human work activity recognition for working cells in industrial production contexts. The specific constraints of such a domain is elaborated and used as prior knowledge. We utilize state-of-the-art algorithms as spatiotemporal feature encoders and search for appropriate classification and fusion strategies. Furthermore, we combine keypoint-based with appearance-based approaches to a multi-stream recognition system. Due to data protection rules and the high effort of data annotation within industrial domains only small datasets are available that reflect production aspects. Therefore, we use transfer learning approaches to reduce the dependency on data volume and variance in the target domain. The resulting recognition system achieves high performance for both singular person action and human-object interaction.
Virtual reality is a powerful tool for industrial applications. The article at hand addresses designers of industrial virtual environments. It summarizes key aspects to design immersive and coherent virtual environments. Furthermore, relevant influencing factors for a high quality virtual environment and tools to quantify this quality are presented. So far, a methodology to design, evaluate, and transfer knowledge from virtual environments into reality has been missing and is of high value for industrial applications. The proposed methodical approach includes the steps application analysis, technology selection and integration, design of virtual environment, evaluation of simulator quality, as well as discussion of the real-world validity. The method is shown on the example of a virtual human-robot working cell used to analyze the human perception of robot behavior during mutual assembly processes. The quality of the virtual environment is evaluated to be adequate for those purposes and the transfer of knowledge gained in virtuality on a corresponding real-world application is discussed. To the best of our knowing a system like the presented one, including full-body tracking, finger tracking, a virtual avatar and a head-mounted display has not been used for industrial use cases and human-robot cooperation before.
The ability to synchronize expectations among human-robot teams and understand discrepancies between expectations and reality is essential for human-robot collaboration scenarios. To ensure this, human activities and intentions must be interpreted quickly and reliably by the robot using various modalities. In this paper we propose a multimodal recognition system designed to detect physical interactions as well as nonverbal gestures. Existing approaches feature high post-transfer recognition rates which, however, can only be achieved based on well-prepared and large datasets. Unfortunately, the acquisition and preparation of domain-specific samples especially in industrial context is time consuming and expensive. To reduce this effort we introduce a weakly-supervised classification approach. Therefore, we learn a latent representation of the human activities with a variational autoencoder network. Additional modalities and unlabeled samples are incorporated by a scalable product-of-expert sampling approach. The applicability in industrial context is evaluated by two domain-specific collaborative robot datasets. Our results demonstrate, that we can keep the number of labeled samples constant while increasing the network performance by providing additional unprocessed information.
Der Beitrag beschreibt den Systemaufbau für die Risikobeurteilung zur Realisierung einer sicheren Mensch-Maschine-Kollaboration und stellt die vorgeschriebene Risikobeurteilung gemäß DIN EN ISO 10218-1:2012-01 in folgende Schritten vor: Beurteilung der Erfahrenswerte und des Nutzerverhaltens, Festlegung der Grenzen des Roboters, Identifikation der Gefährdungen und Risikoeinschätzung in den dimensionen Ausmaß, Exposition, Eintrittswahrscheinlichkeit und Vermeidungsmöglichkeit. Die Umsetzung in eine Steuerung, die Steuerungsarchitektur und die eingesetzten Komponenten zur Realisierung einer sicheren Abstandsüberwachung werden beschrieben und in Blockschaltbildern grafisch dargestellt. Die Validität der Sicherheitsfunktion wird diskutiert.
