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
    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
    Numerical investigation of optical sorting using the discrete element method
    ( 2017)
    Pieper, C.
    ;
    Kruggel-Emden, H.
    ;
    Wirtz, S.
    ;
    Scherer, V.
    ;
    Pfaff, F.
    ;
    Noack, B.
    ;
    Hanebeck, U.D.
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Automated optical sorting systems are important devices in the growing field of bulk solids handling. The initial sorter calibration and the precise optical sorting of many materials is still very time consuming and difficult. A numerical model of an automated optical belt sorter is presented in this study. The sorter and particle interaction is described with the Discrete Element Method (DEM) while the separation phase is considered in a post processing step. Different operating parameters and their influence on sorting quality are investigated. In addition, two models for detecting and predicting the particle movement between the detection point and the separation step are presented and compared, namely a conventional line scan camera model and a new approach combining an area scan camera model with particle tracking.
  • Publication
    Feature selection with a budget
    ( 2016)
    Richter, M.
    ;
    Maier, Georg
    ;
    Gruna, Robin
    ;
    Längle, Thomas
    ;
    Beyerer, Jürgen
    Feature selection is an important step in all practical applications of pattern recognition. As such, it is not surprising that during the past decades it has received a lot of attention from the research community. The topic is well understood and many methods have been put to the test. Most methods, however, overlook an aspect critical to real-time applications: limited computation time. The set of selected features must not only be suitable to solve the task, but must also ensure that the task can be solved within the available time. With this in mind, we propose a method for feature selection with a budget. We approach the problem by stating feature selection as a multi-objective optimization problem. This problem is solved using the well known NSGA-II algorithm. We evaluate our approach using one synthetic and two real-world datasets. We explore the properties of the genetic algorithm and investigate the classification performance of the resulting selections. Our results show that the selected feature sets are highly suitable, especially when considering real-time systems.