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
    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
    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
    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
  • Publication
    Automatic visual inspection based on trajectory data
    ( 2019)
    Maier, Georg
    ;
    Mürdter, N.
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Automatic inspection tasks have successfully been implemented in several industrial fields and are of growing importance. Visual inspection using optical sensors is wide spread due to the vast variety of different sensors, observable features and comparatively low prices. It seems obvious that corresponding systems are blind towards mechanical features and inspection of those typically requires highly specialized, inflexible and costly systems. Recently, we have shown in the context of sensor-based sorting that tracking objects over a time period allows deriving motion-based features which potentially enable discrimination of optically identical objects, although an optical sensor is used. In this paper, we take one step back from the specific application and study the classification of test objects based on their trajectories. The objects are observed while receiving a certain impulse. We further refrain from manually designing features but use raw coordinates as extracted from a series of images. The success of the method is demonstrated by discriminating spheres made of similar plastic types while bouncing off a plane.
  • Publication
    Application of area-scan sensors in sensor-based sorting
    ( 2018)
    Maier, Georg
    ;
    Pfaff, F.
    ;
    Pieper, C.
    ;
    Gruna, Robin
    ;
    Noack, B.
    ;
    Kruggel-Emden, H.
    ;
    Längle, Thomas
    ;
    Hanebeck, U.D.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    ;
    Beyerer, Jürgen
    In the field of machine vision, sensor-based sorting is an important real-time application that enables the separation of a material feed into different classes. While state-of-the-art systems utilize scanning sensors such as line-scan cameras, advances in sensor technology have made application of area scanning sensors feasible. Provided a sufficiently high frame rate, objects can be observed at multiple points in time. By applying multiobject tracking, information about the objects contained in the material stream can be fused over time. Based on this information, our approach further allows predicting the position of each object for future points in time. While conventional systems typically apply a global, rather simple motion model, our approach includes an individual motion model for each object, which in turn allows estimating the point in time as well as the position when reaching the separation stage. In this contribution, we present results from our collaborative research project and summarize the present advances by discussing the potential of the application of area-scan sensors for sensor-based sorting. Among others, we introduce our simulation-driven approach and present results for physical separation efficiency for simulation-generated data, demonstrate the potential of using motion-based features for material classification and discuss real-time related challenges.
  • Publication
    Improving multitarget tracking using orientation estimates for sorting bulk materials
    ( 2017)
    Pfaff, F.
    ;
    Kurz, G.
    ;
    Pieper, C.
    ;
    Maier, Georg
    ;
    Noack, B.
    ;
    Kruggel-Emden, H.
    ;
    Gruna, Robin
    ;
    Hanebeck, U.D.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Optical belt sorters can be used to sort a large variety of bulk materials. By the use of sophisticated algorithms, the performance of the complex machinery can be further improved. Recently, we have proposed an extension to industrial optical belt sorters that involves tracking the individual particles on the belt using an area scan camera. If the estimated behavior of the particles matches the true behavior, the reliability of the separation process can be improved. The approach relies on multitarget tracking using hard association decisions between the tracks and the measurements. In this paper, we propose to include the orientation in the assessment of the compatibility of a track and a measurement. This allows us to achieve more reliable associations, facilitating a higher accuracy of the tracking results.