Universität Stuttgart

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Institute: search.filters.UBSInstitut.Institut für Industrielle Fertigung und FabrikbetriebInstitute: search.filters.UBSInstitut.Fraunhofer Institut für Produktionstechnik und Automatisierung (IPA)Author: search.filters.author.Huber, Marco F.Author: search.filters.author.Kraus, Werner

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Now showing 1 - 3 of 3
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    Towards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm
    (2024) Albus, Marcel; Hornek, Timothée; Kraus, Werner; Huber, Marco F.
    Assembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies’ competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to ∼2.75 ×, reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.
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    A survey on learning-based robotic grasping
    (2020) Kleeberger, Kilian; Bormann, Richard; Kraus, Werner; Huber, Marco F.
    This review provides a comprehensive overview of machine learning approaches for vision-based robotic grasping and manipulation. Current trends and developments as well as various criteria for categorization of approaches are provided. Model-free approaches are attractive due to their generalization capabilities to novel objects, but are mostly limited to top-down grasps and do not allow a precise object placement which can limit their applicability. In contrast, model-based methods allow a precise placement and aim for an automatic configuration without any human intervention to enable a fast and easy deployment. Both approaches to robotic grasping and manipulation with and without object-specific knowledge are discussed. Due to the large amount of data required to train AI-based approaches, simulations are an attractive choice for robot learning. This article also gives an overview of techniques and achievements in transfers from simulations to the real world.
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    ItemOpen Access
    Deep reinforcement learning based approach for the search and engagement phase of the robotic screw unfastening process
    (2025) Al Assadi, Anwar; Wang, Yandong; Nägele, Frank; Kraus, Werner; Huber, Marco F.
    Fasteners such as screws or nuts play an essential role in product design due to their non-permanent joint behavior. In this context, the search and engagement phase during the automated disassembly of fasteners is crucial since position errors due to the inaccuracy of a fully automated robot cell must be compensated. Additional inaccuracy appears in product-specific mounting situation fasteners, where computer vision cannot capture the situation. This might leads to a time-consuming spiral search. In this regard, a deep reinforcement learning (DRL) approach is proposed to solve the search and engagement task. A deep Q-network (DQN) agent has been trained by using external force-torque sensor values and the intrinsic motor currents of the robot itself. The training has been conducted virtually and continued on the robot to improve the success rate. To find the optimal DQN architecture, we have combined a multilayer perceptron with convolutional neural networks with long short-term memory as feature extractors. Our proposed DRL-based approach is 2 s faster to engage than a spiral search for an initial error of 3 mm.