Dr.-Ing.

Gruna, Robin

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  • Publication
    Particle-Specific Deflection Windows for Optical Sorting by Uncertainty Quantification
    ( 2024)
    Reith-Braun, Marcel
    ;
    Liang, Kevin
    ;
    Pfaff, Florian
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Bauer, Albert
    ;
    Kruggel-Emden, Harald
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    ;
    Hanebeck, Uwe D.
    In current state of the art sensor-based sorting systems, the length of the deflection windows, i.e., the period of nozzle activation and the number of nozzles to be activated, is commonly determined solely by the size of the particles. However, this comes at the cost of the sorting process not accounting for model discrepancies between actual and presumed particle motion, as well as for situations where the available information does not allow for precise determination of nozzle activations. To achieve a desired sorting accuracy, in practice, one is therefore usually forced to enlarge the deflection window to a certain degree, which increases the number of falsely co-deflected particles and compressed air consumption. In this paper, we propose incorporating the uncertainty of the prediction of particle motion of each individual particle into the determination of the deflection windows. The method is based on the predictive tracking approach for optical sorting, which tracks the particles while they move toward the nozzle array based on images of an area-scan camera. Given the extracted motion information from the tracking, we propose an approximation for the distribution of arrival time and location of the particle at the nozzle array assuming nearly constant-velocity or nearly constantacceleration particle motion behavior. By evaluating the quantile function of both distributions, we obtain a confidence interval for the arrival time and location based on prediction uncertainty, which we then combine with the particle size to form the final deflection window. We apply our method to a real sorting task using a pilot-scale chute sorter. Our results obtained from extensive sorting trials show that sorting accuracies can be remarkably improved compared with state-of-the-art industrial sorters and enhanced even further compared with predictive tracking while having the potential to reduce compressed air consumption.
  • Publication
    Multi-Modal Image Acquisition for AI-Based Bulky Waste Sorting (Incl. Terahertz Synthetic Aperture Radar)
    ( 2023)
    Cibiraite-Lukenskiene, Dovile
    ;
    Gundacker, Dominik
    ;
    Bihler, M.
    ;
    Heizmann, M.
    ;
    Schlüter, Friedrich
    ;
    Aderhold, Jochen
    ;
    Roming, Lukas
    ;
    Gruna, Robin
    ;
    Jonuscheit, Joachim
    ;
    Friederich, Fabian
    This work presents the results of the initial acquisition of a multi-modal dataset that will be utilized to train and test a neural network for wood sorting. The aim of the project is to improve wood recycling from bulky waste by using four complementary sensing systems: visual, infrared, terahertz, and thermography. The four systems were combined to capture 57 multi-modal images of bulky waste samples moving on the conveyor belt at a speed of 10 cm/s. Early fusion results on THz show 0.77 accuracy, whereas the best multi-modal data fusion accuracy equals 0.921.
  • Publication
    Improving Accuracy of Optical Sorters Using Closed-Loop Control of Material Recirculation
    ( 2023)
    Vieth, Jonathan
    ;
    Reith-Braun, Marcel
    ;
    Bauer, Albert
    ;
    Pfaff, Florian
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Kruggel-Emden, Harald
    ;
    Hanebeck, Uwe D.
    Optical sorting is a key technology for the circular economy and is widely applied in the food, mineral, and recycling industries. Despite its widespread use, one typically resorts to expensive means of adjusting the accuracy, e.g., by reducing the mass flow or changing mechanical or software parameters, which typically requires manual tuning in a lengthy, iterative process. To circumvent these drawbacks, we propose a new layout for optical sorters along with a controller that allows re-feeding of controlled fractions of the sorted mass flows. To this end, we build a dynamic model of the sorter, analyze its static behavior, and show how material recirculation affects the sorting accuracy. Furthermore, we build a model predictive controller (MPC) employing the model and evaluate the closed-loop sorting system using a coupled discrete element–computational fluid dynamics (DEM-CFD) simulation, demonstrating improved accuracy.
  • Publication
    Multi-sensor data fusion using deep learning for bulky waste image classification
    ( 2023)
    Bihler, Manuel
    ;
    Roming, Lukas
    ;
    Jiang, Yifan
    ;
    Afifi, Ahmed J.
    ;
    Aderhold, Jochen
    ;
    Cibiraite-Lukenskiene, Dovile
    ;
    Lorenz, Sandra
    ;
    Gloaguen, Richard
    ;
    Gruna, Robin
    ;
    Heizmann, Michael
    Deep learning techniques are commonly utilized to tackle various computer vision problems, including recognition, segmentation, and classification from RGB images. With the availability of a diverse range of sensors, industry-specific datasets are acquired to address specific challenges. These collected datasets have varied modalities, indicating that the images possess distinct channel numbers and pixel values that have different interpretations. Implementing deep learning methods to attain optimal outcomes on such multimodal data is a complicated procedure. To enhance the performance of classification tasks in this scenario, one feasible approach is to employ a data fusion technique. Data fusion aims to use all the available information from all sensors and integrate them to obtain an optimal outcome. This paper investigates early fusion, intermediate fusion, and late fusion in deep learning models for bulky waste image classification. For training and evaluation of the models, a multimodal dataset is used. The dataset consists of RGB, hyperspectral Near Infrared (NIR), Thermography, and Terahertz images of bulky waste. The results of this work show that multimodal sensor fusion can enhance classification accuracy compared to a single-sensor approach for the used dataset. Hereby, late fusion performed the best with an accuracy of 0.921 compared to intermediate and early fusion, on our test data.
  • Publication
    Detecting Tar Contaminated Samples in Road-rubble using Hyperspectral Imaging and Texture Analysis
    ( 2023)
    Bäcker, Paul
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Polycyclic aromatic hydrocarbons (PAH) containing tar-mixtures pose a challenge for recycling road rubble, as the tar containing elements have to be extracted and decontaminated for recycling. In this preliminary study, tar, bitumen and minerals are discriminated using a combination of color (RGB) and Hyperspectral Short Wave Infrared (SWIR) cameras. Further, the use of an autoencoder for detecting minerals embedded inside tar- and bitumen mixtures is proposed. Features are extracted from the spectra of the SWIR camera and the texture of the RGB images. For classification, linear discriminant analysis combined with a k-nearest neighbor classification is used. First results show a reliable detection of minerals and positive signs for separability of tar and bitumen. This work is a foundation for developing a sensor-based sorting system for physical separation of tar contaminated samples in road rubble.
  • Publication
    Increasing the reuse of wood in bulky waste using artificial intelligence and imaging in the VIS, IR, and terahertz ranges
    ( 2023)
    Roming, Lukas
    ;
    Gruna, Robin
    ;
    Aderhold, Jochen
    ;
    Schlüter, Friedrich
    ;
    Cibiraite-Lukenskiene, Dovile
    ;
    Gundacker, Dominik
    ;
    Friedrich, Fabian
    ;
    Bihler, Manuel
    ;
    Heizmann, Michael
    Bulky waste contains valuable raw materials, especially wood, which accounts for around 50% of the volume. Sorting is very time-consuming in view of the volume and variety of bulky waste and is often still done manually. Therefore, only about half of the available wood is used as a material, while the rest is burned with unsorted waste. In order to improve the material recycling of wood from bulky waste, the project ASKIVIT aims to develop a solution for the automated sorting of bulky waste. For that, a multi-sensor approach is proposed including: (i) Conventional imaging in the visible spectral range; (ii) Nearinfrared hyperspectral imaging; (iii) Active heat flow thermography; (iv) Terahertz imaging. This paper presents a demonstrator used to obtain images with the aforementioned sensors. Differences between the imaging systems are discussed and promising results on common problems like painted materials or black plastic are presented. Besides that, pre-examinations show the importance of near-infrared hyperspectral imaging for the characterization of bulky waste.
  • Publication
    Problem-Specific Optimized Multispectral Sensing for Improved Quantification of Plant Biochemical Constituents
    ( 2022)
    Schumacher, Petra
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Multispectral cameras are gaining popularity in the field of smart farming and plant phenotyping. They are more cost-effective than hyperspectral cameras and frame-based imaging allows easier operation and processing, while at the same time the limited spectral resolution still allows the retrieval of relevant information about the plant status. Typically, multispectral cameras are available with equidistant channels spanning a defined spectral range. We propose a design approach based on Bayesian optimization to define problem-specific spectral channels for multispectral cameras in the plant phenotyping domain. Compared to established wavelength selection algorithms, our approach considers physical constraints of optical filters such as feasible filter function shape and width. The filter functions are optimized and tested to predict plant pigment concentration and Equivalent Water Thickness of simulated spectra generated with the PROSPECT-D leaf radiative transfer model. Problem-specific multispectral camera design could potentially enhance prediction performance of automated plant status monitoring.
  • Publication
    Systematic Determination of the Influence of Factors Relevant to Operating Costs of Sensor-based Sorting Systems
    ( 2022)
    Ludwig, Jan Niklas
    ;
    Flitter, Merle
    ;
    Maier, Georg
    ;
    Bauer, Albert
    ;
    Reith-Braun, Marcel
    ;
    Gruna, Robin
    ;
    Kruggel-Emden, Harald
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Within the next decade, the recycling rates of all waste streams in the European Union are to be consistently increased (EU, 2018). Sensor-based sorting plays a crucial role in achieving those aims. However, the composition of the operating costs of sensor-based sorting systems (SBS), which are made up of compressed air and electricity costs, for example, has not yet been adequately investigated. The main cause is the massive experimental effort required to investigate these costs. In this paper, we pro-pose a systematic approach for determining the operating costs of SBS systems by using Design of Experiments (DoE). For this purpose, experiments are carried out to investigate whether the methodology of DoE is applicable to the use case of SBS. The resulting models are validated with statistical measures and additional experimental runs. For comparability of the results, two different materials, namely construction and demolition waste as well as plastic flakes, with grain sizes between 0 - 10 mm are investigated. With the presented approach high coefficients of determination of the regression models are reached. Consequently, the results show that precise regres-sion models can be derived with reduced effort.
  • Publication
    Machine learning-based multiobject tracking for sensor-based sorting
    ( 2022)
    Maier, Georg
    ;
    Reith-Braun, Marcel
    ;
    Bauer, Albert
    ;
    Gruna, Robin
    ;
    Pfaff, Florian
    ;
    Kruggel-Emden, Harald
    ;
    Längle, Thomas
    ;
    Hanebeck, Uwe
    ;
    Beyerer, Jürgen