Wissenschaft & Publikationen

TWIN4TRUCKS steht für den Transfer wissenschaftlicher Forschungsergebnisse in die industrielle Praxis. Die folgenden Publikationen sind rund um das Projekt TWIN4TRUCKS entstanden.

12.05.2025

Comparative Analysis of Synthetic Data Generation
for Object Detection: CAD Models vs. 3D Scans of Industrial Items and Hybrid Approaches

Abdullah Farrukh, Tatjana Legler, Achim Wagner, Martin Ruskowski

Deep learning techniques, particularly in object detection, are becoming increasingly common in industrial settings. However, the challenges posed by industrial objects — such as intricate surface textures and complex geometries — often require the creation of custom training datasets. Publicly available datasets typically do not provide sufficient coverage for these unique characteristics. In many cases, producing real-world datasets for low-volume, high-variability production scenarios is both time-consuming and costly. In this paper, we evaluate the use of synthetic data generated using NVIDIA’s Isaac-Sim as an efficient alternative. We compare the use of CAD models and 3D scans of real assets, reconstructed using state-of-the-art 3D reconstructions methods, e.g. structured light scans and Neural Radiance Fields (NeRFs). For this, we utilize pre-existing hardware and software tools and set the focus on the usability in an Industry 4.0 environment. The generated synthetic datasets are used to train a YOLO-based object detection model for a worker assistance system that provides context-based assembly instructions. The model is tested with real image data of two objects with distinct surface and texture properties. Initial results demonstrate performance that exceeded expectations.

Published in: 8th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS)

22.11.2024

Enhancing flexibility in intralogistics 4.0 by using Services, Capabilities

Benjamin Blumhofer, Philipp Richard, Tatjana Legler, Martin Ruskowski

Manufacturing companies face a dynamic environment shaped by various factors that profoundly affect their production capacities. These factors encompass trends like shortened product life cycles, a surge in product variants, and subsequent reductions in batch sizes. Consequently, companies must adapt their operational capabilities, ensuring existing machinery remains versatile while seamlessly integrating new equipment into their production facilities, following the ”plug and produce” approach. These shifts also reverberate through intralogistics, altering flexibility requirements and methods for individualized goods handling. Despite significant progress in modeling production information using the Capability-Skill-Service model, its application in intralogistics is relatively limited to date. However, given the potential benefits, the integration of this model into both intralogistics and production promises to address one of the key challenges of Industry 4.0: the harmonization of planning and execution processes in production and intralogistics. Closing this gap, this paper proposes an architectural framework that includes core components and information models for a Capability-Skill-Service-based Intralogistics 4.0 application. This framework not only facilitates the seamless integration of intralogistics with production planning and execution, but also lays the foundation for greater flexibility and efficiency in manufacturing. With an implementation the framework and the information models are validated.

Published in: 6th International Conference on Industry 4.0 and Smart Manufacturing

27. Juni 2024

Model Predictive Control Based Reference Generation for Optimal Proportional Integral Derivative Control

Fatos Gashi, Khalil Abuibaid, Martin Ruskowski, Achim Wagner

We introduce an alternative approach towards optimal proportional integral derivative (PID) control, consisting of model predictive control (MPC) based reference generation. To this end, we have integrated the reference as part of optimization variables of the resulting problem, where a deliberate sequence of errors is induced to obtain an optimal PID control action. In addition, the desired behavior of the PID controller is achieved without the need for internal modification of the PID gains. To better highlight the ability of coping with poor PID tuning, several test cases consisting of progressively degraded PID gains are presented. Validation of the proposed strategy is displayed by comprehensive simulations using two different plants.

Published in: 2024 32nd Mediterranean Conference on Control and Automation (MED)

11. August 2023

U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds

Yan Di, Chenyangguang Zhang, Ruida Zhang, Fabian Manhardt, Yongzhi Su, Jason Rambach, Didier Stricker, Xiangyang Ji, Federico Tombari

In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target. Considering existing methods typically fail to handle noisy partial observations, U-RED is designed to address this issue from two aspects. First, since one partial shape may correspond to multiple potential full shapes, the retrieval method must allow such an ambiguous one-to-many relationship. Thereby U-RED learns to project all possible full shapes of a partial target onto the surface of a unit sphere. Then during inference, each sampling on the sphere will yield a feasible retrieval. Second, since real-world partial observations usually contain noticeable noise, a reliable learned metric that measures the similarity between shapes is necessary for stable retrieval. In U-RED, we design a novel point-wise residual-guided metric that allows noise-robust comparison. Extensive experiments on the synthetic datasets PartNet, ComplementMe and the real-world dataset Scan2CAD demonstrate that U-RED surpasses existing state-of-the-art approaches by 47.3%, 16.7% and 31.6% respectively under Chamfer Distance.

01. August 2023

Skill-basierte Intralogistik: Transport von Produkten an Produktionsmodule durch mobile Roboter

Benjamin Blumhofer, Alexandra Ritter, Jesko Hermann, Martin Ruskowski

There is a trend towards developing individualized solutions in the context of product exchange between production modules and autonomous mobile robots (AMR). These solutions are typically implemented via a central controller, utilizing pre-programmed processes and a fixed physical positioning of the AMR. Unfortunately, such solutions can be expensive and difficult to transfer to other implementations. Skill-based production offers a promising alternative, enabling a transport that is vendorindependent and resilient, by utilizing horizontal communication.

Published in: atp magazin (08/2023).