Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Towards High-Payload Admittance Control for Manual Guidance with Environmental Contact
    ( 2022)
    Haninger, Kevin
    ;
    Radke, Marcel
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    Vick, Axel
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    Krüger, Jörg
    Force control enables hands-on teaching and physical collaboration, with the potential to improve ergonomics and flexibility of automation. Established methods for the design of compliance, impedance control, and collision response can achieve free-space stability and acceptable peak contact force on lightweight, lower payload robots. Scaling collaboration to higher payloads can allow new applications, but introduces challenges due to the more significant payload dynamics and the use of higher-payload industrial robots. To achieve high-payload manual guidance with contact, this paper proposes and validates new mechatronic design methods: standard admittance control is extended with damping feedback, compliant structures are integrated to the environment, and a contact response method which allows continuous admittance control is proposed. These methods are compared with respect to free-space stability, contact stability, and peak contact force. The resulting methods are then applied to realize two contact-rich tasks on a 16 kg payload (peg in hole and slot assembly) and free-space co-manipulation of a 50 kg payload.
  • Publication
    Redundancy Concepts for Real-Time Cloud- and Edge-based Control of Autonomous Mobile Robots
    ( 2022)
    Nouruzi-Pur, Jan
    ;
    Lambrecht, Jens
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    Nguyen, The Duy
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    Vick, Axel
    ;
    Krüger, Jörg
    Deploying navigation algorithms on an edge or cloud server according to the Software-as-a-Service paradigm has many advantages for autonomous mobile robots in indus-trial environments, e.g. cooperative planning and less onboard energy consumption. However, outsourcing corresponding real-time critical control functions requires a high level of reliability, which cannot be guaranteed either by modern wireless networks nor by the outsourced computing infrastructure. This work introduces redundancy concepts, which enable real-time capability within these uncertain infrastructures by providing redundant computation nodes, as well as robot-controlled switching between them. Redundancies can vary regarding their physical location, robot behavior during the switchover process and degree of activeness while quality of service concerning the primary controller is sufficient. In the case that fallback redun-dancies are not continuously active, when a disturbance occurs an initial state estimation of the robot pose has to be provided and an activation time has to be anticipated. To gain some insights on expected behavior, redundant computation nodes are deployed locally on the robot and on an outsourced computation node and consequently evaluated empirically. Quantitative and qualitative results in simulation and a real environment show that redun-dancies help to significantly improve the robot-trajectory within an unreliable network. Moreover, resource-saving redundancies, which are not continuously active, can robustly take over control by using an estimated state.
  • Publication
    Steigerung der Energieeffizienz mittels Energiekennzahlen am Beispiel der Metallverarbeitung
    ( 2022)
    Sigg, Stefan
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    Kühn, Armin
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    Schmidt, Alexander
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    Roder, Sven
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    Thiele, Gregor
    ;
    Krüger, Jörg
    Die Energiewende und die einhergehende Forderung nach Effizienzsteigerungen stellen produzierende Unternehmen vor betriebliche und technische Herausforderungen. Methoden und Technologien des Energiemanagements gewinnen damit auch im Mittelstand an Relevanz. Dabei sind Kennzahlen ein Schlüssel, um die aktuelle Energieeffizienz einzuschätzen und Potenziale für Einsparungen zu identifizieren. Der Artikel dokumentiert Erfahrungen der Anwender mit Bezug auf Konzepte aus der Wissenschaft, um Interessierten aus der Industrie den Einstieg in das Thema zu erleichtern.
  • Publication
    Low-Cost Embedded Vision for Industrial Robots: A Modular End-of-Arm Concept
    ( 2020)
    Kroeger, Ole
    ;
    Wollschläger, Felix
    ;
    Krüger, Jörg
    In this work we present our first prototype of a modular, low-cost end-of-arm concept for industrial robot applications. The goal is a faster, more flexible and cost-effective alternative compared to current industrial solutions. We use an embedded single-board computer and three cameras as a sensor base for the new system. The scope of robot application is supposed to as wide as possible (e.g. object detection, bin picking, assembly tasks and quality control tasks). We discuss some industry and low-cost-hardware solutions, introduce our system and deliver a proof of concept. Furthermore we use the system to accomplish a vision based pick and place task.
  • Publication
    Classification of Similar Objects of Different Sizes Using a Reference Object by Means of Convolutional Neural Networks
    ( 2019)
    Lehr, Jan
    ;
    Schlüter, Marian
    ;
    Krüger, Jörg
    Part identification is relevant in many industrial applications, either for direct recognition of components or assemblies, either as a fully automated process or as an assistance system. Convolutional Neural Networks (CNNs) have proven their worth in image processing, especially in classification tasks. It therefore makes sense to use them for industrial applications. There are major problems with parts that look very similar and can only be identified by their size. In this paper we have considered a subset of screws that all conform to the same norm but are of different sizes. The implicit learning of the screw size is only possible if the images are taken in a fixed distance setup and larger screws are shown larger on the images. In this paper we show that CNNs are able to implicitly measure target objects with the help of reference objects and thus to integrate the object size into the learning process.
  • Publication
    Deep learning for part identification based on inherent features
    ( 2019)
    Krüger, Jörg
    ;
    Lehr, Jan
    ;
    Schlüter, Marian
    ;
    Bischoff, Nils
    The identification of parts is essential for the efficient automation of logistic processes such as part supply in assembly and disassembly. This paper describes a new method for the optical identification of parts without explicit codes but based on inherent geometrical features with Deep Learning. The paper focusses on the improvement of training of Deep Learning systems taking into account conflicting factors such as limited training data and high variety of parts. Based on a case study in turbine industry the effects of steadily growing training data on the robustness of part classification are evaluated.
  • Publication
    Combining the Advantages of On- and Offline Industrial Robot Programming
    ( 2019)
    Guhl, Jan
    ;
    Nikoleizig, Sylvio
    ;
    Heimann, Oliver
    ;
    Hügle, Johannes
    ;
    Krüger, Jörg
    Classic off- and online programming approaches offer different advantages but cannot provide intuitive programming as it would be needed for frequent reconfiguration of industrial robotic cells and their programs. While a simulation and therewith a collision control can be used offline, highest precision can be achieved by using online teach-in. We propose a system that combines the advantages of both offline and online programming. By creating a programming framework that is independent of in- and output devices we allow the use of augmented as well as virtual reality. Thus, enabling the user to choose for a programming technique that best suits his needs during different stages of programming. Bringing together both methods allows us to not only drastically reduce programming time but simultaneously increase intuitiveness of human robot interaction.
  • Publication
    Energy Efficiency Optimization using AutomationML modeling and an EnPI methodology
    ( 2019)
    Thiele, Gregor
    ;
    Khorsandi, Niloufar
    ;
    Krüger, Jörg
    Industrial facilities are complex and heterogeneous systems in permanent technological change. The ambitions towards smart factories heighten the requirements for the flexible interconnection of various devices. These industrial entities are controlled, observed and optimized by many services. The tuning of process parameters of several linked components in order to boost the overall energy efficiency is one example of such services. AutomationML (AML) provides a hierarchical description language for industrial systems considering both structure and properties. An extension of the established standard allows for intuitive modeling of energy optimization problems. An approved energy performance indicator (EnPI) methodology was integrated in the libraries of AML in order to simplify and shorten the modeling procedure for the optimization task. The procedure is demonstrated using the example of an industrial cooling system.
  • Publication
    Development of a Fire Detection Based on the Analysis of Video Data by Means of Convolutional Neural Networks
    ( 2019)
    Lehr, Jan
    ;
    Gerson, Christian
    ;
    Ajami, Mohamad
    ;
    Krüger, Jörg
    Convolutional Neural Networks (CNNs) have proven their worth in the field of image-based object recognition and localization. In the context of this work, a fire detector based on CNNs has been developed that detects fire by analyzing video sequences. The major additions of this work will primarily be realized through the use of temporal information contained in the video sequences depicting fire. In contrast to state of the art fire detectors, a large image database with 160,000 images with an even distribution of positive and negative samples has been created. To be able to compare image-based and video-based approaches as objectively as possible, different image-based CNNs will be trained under the same conditions as the video-based networks within the scope of this work. It will be shown that video-based networks offer an advantage over conventional image-based networks and therefore benefit from the temporal information of fire. We have achieved a prediction accuracy of 96.82%.