Dr.-Ing.

Gruna, Robin

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A survey of the state of the art in sensor-based sorting technology and research

2024 , Maier, Georg , Gruna, Robin , Längle, Thomas , Beyerer, Jürgen

Sensor-based sorting describes a family of systems that enable the removal of individual objects from a material stream. The technology is widely used in various industries such as agriculture, food, mining, and recycling. Examples of sorting tasks include the removal of fungus-infested grains, the enrichment of copper content in copper mining or the sorting of plastic waste according to the type of plastic. Sorting decisions are made based on information acquired by one or more sensors. A particular strength of the technology is the flexibility in sorting decisions, which is achieved by using various sensors and programming the data analysis. However, a comprehensive understanding of the process is necessary for the development of new sorting systems that can address previously unresolved tasks. This survey is aimed at innovative researchers and practitioners who are unfamiliar with sensor-based sorting or have only encountered certain aspects of the overall process. The references provided serve as starting points for further exploration of specific topics.

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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.

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Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking

2021 , Maier, Georg , Pfaff, Florian , Pieper, Christoph , Gruna, Robin , Noack, Benjamin , Kruggel-Emden, Harald , Längle, Thomas , Hanebeck, Uwe D. , Wirtz, Siegmar , Scherer, Viktor , Beyerer, Jürgen

Sensor-based sorting is a machine vision application that has found industrial application in various fields. An accept-or-reject task is executed by separating a material stream into two fractions. Current systems use line-scanning sensors, which is convenient as the material is perceived during transportation. However, line-scanning sensors yield a single observation of each object and no information about their movement. Due to a delay between localization and separation, assumptions regarding the location and point in time for separation need to be made based on the prior localization. Hence, it is necessary to ensure that all objects are transported at uniform velocities. This is often a complex and costly solution. In this paper, we propose a new method for reliably separating particles at non-uniform velocities. The problem is transferred from a mechanical to an algorithmic level. Our novel advanced image processing approach includes equipping the sorter with an area-scan camera in combination with a real-time multiobject tracking system, which enables predictions of the location of individual objects for separation. For the experimental validation of our approach, we present a modular sorting system, which allows comparing sorting results using a line-scan and area-scan camera. Results show that our approach performs reliable separation and hence increases sorting efficiency.

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Numerical modelling of an optical belt sorter using a DEM-CFD approach coupled with particle tracking and comparison with experiments

2018 , Pieper, C. , Pfaff, F. , Maier, Georg , Kruggel-Emden, H. , Wirtz, S. , Noack, B. , Gruna, Robin , Scherer, V. , Hanebeck, U.D. , Längle, Thomas , Beyerer, Jürgen

State-of-the-art optical sorting systems suffer from delays between the particle detection and separation stage, during which the material movement is not accounted for. Commonly line scan cameras, using simple assumptions to predict the future particle movement, are employed. In this study, a novel prediction approach is presented, where an area scan camera records the particle movement over multiple time steps and a tracking algorithm is used to reconstruct the corresponding paths to determine the time and position at which the material reaches the separation stage. In order to assess the benefit of such a model at different operating parameters, an automated optical belt sorter is numerically modelled and coupled with the tracking procedure. The Discrete Element Method (DEM) is used to describe the particle-particle as well as particle-wall interactions, while the air nozzles required for deflecting undesired material fractions are described with Computational Fluid Dynamics (CFD). The accuracy of the employed numerical approach is ensured by comparing the separation results of a predefined sorting task with experimental investigations. The quality of the aforementioned prediction models is compared when utilizing different belt lengths, nozzle activation durations, particle types, sampling frequencies and detection windows. Results show that the numerical model of the optical belt sorter is able to accurately describe the sorting system and is suitable for detailed investigation of various operational parameters. The proposed tracking prediction model was found to be superior to the common line scan camera method in all investigated scenarios. Its advantage is especially profound when difficult sorting conditions, e.g. short conveyor belt lengths or uncooperative moving bulk solids, apply.

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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.

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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.

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Motion-based visual inspection of optically indiscernible defects on the example of hazelnuts

2021 , Maier, Georg , Shevchyk, Anja , Flitter, Merle , Gruna, Robin , Längle, Thomas , Hanebeck, Uwe D. , Beyerer, Jürgen

Automatic quality control has long been an integral part of the processing of food and agricultural products. Visual inspection offers solutions for many issues in this context and can be employed in the form of sensor-based sorting to automatically remove foreign and low quality entities from a product stream. However, these methods are limited to defects that can be made visible by the employed sensor, which usually restricts the system to defects appearing on the surface. An alternative non-visual solution lies in impact-acoustic methods, which do not suffer from this constraint. However, these are strongly limited in terms of material throughput and consequently not suitable for large scale industrial application. In this paper, we present a novel approach that performs inspection based on optically acquired motion data. A high-speed camera captures image sequences of test objects during a transportation process on a chute with a specific structured surface. The trajectory data is then used to classify test objects based on their motion behavior. The approach is evaluated experimentally on the example of distinguishing defect-free hazelnuts from ones that suffer from insect damage. Results show that by merely utilizing the motion data, a recognition rate of up to for undamaged hazelnuts can be achieved. A major advantage of our approach is that it can be integrated in sensor-based sorting systems and is suitable for high throughput applications.

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GridSort: Image-based Optical Bulk Material Sorting Using Convolutional LSTMs

2023 , Reith-Braun, Marcel , Bauer, Albert , Staab, Maximilian , Pfaff, Florian , Maier, Georg , Gruna, Robin , Längle, Thomas , Beyerer, Jürgen , Kruggel-Emden, Harald , Hanebeck, Uwe D.

Optical sorters separate particles of different classes by first detecting them while they are transported, e.g., on a conveyor belt, and subsequently bursting out particles of undesired classes using compressed air nozzles. Currently, the most promising results are achieved by predictive tracking, a multitarget tracking approach based on extracted midpoints from area-scan camera images that analyzes the particles’ motion and activates the nozzles accordingly. However, predictive tracking requires expert knowledge for setup and preceding object detection. Moreover, particle shapes are only considered implicitly, and the need to solve an association problem rises the computational complexity of the algorithm. In this paper, we present GridSort, an image-based approach that forecasts the scene at the nozzle array using a convolutional long short-term memory neural network and subsequently extracts nozzle activations, thus circumventing the aforementioned weaknesses. We show how GridSort can be trained in an unsupervised fashion and evaluate it using a coupled discrete element–computational fluid dynamics simulation of an optical sorter. We compare our method with predictive tracking in terms of sorting accuracy and demonstrate that it is an easy-to-apply alternative while achieving state-of-the-art results.

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SmartSpectrometer - Embedded Optical Spectroscopy for Applications in Agriculture and Industry

2021 , Krause, Julius , Grüger, Heinrich , Gebauer, Lucie , Zheng, Xiaorong , Knobbe, Jens , Pügner, Tino , Kicherer, Anna , Gruna, Robin , Längle, Thomas , Beyerer, Jürgen

The ongoing digitization of industry and agriculture can benefit significantly from optical spectroscopy. In many cases, optical spectroscopy enables the estimation of properties such as substance concentrations and compositions. Spectral data can be acquired and evaluated in real time, and the results can be integrated directly into process and automation units, saving resources and costs. Multivariate data analysis is needed to integrate optical spectrometers as sensors. Therefore, a spectrometer with integrated artificial intelligence (AI) called SmartSpectrometer and its interface is presented. The advantages of the SmartSpectrometer are exemplified by its integration into a harvesting vehicle, where quality is determined by predicting sugar and acid in grapes in the field.

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From Visual Spectrum to Millimeter Wave: A Broad Spectrum of Solutions for Food Inspection

2020 , Becker, Florian , Schwabig, Christopher , Krause, Julius , Leuchs, Sven , Krebs, Christian , Gruna, Robin , Kuter, Andries , Längle, Thomas , Nußler, Dirk , Beyerer, Jürgen

The consequences of food adulteration can be far reaching. In the past, inexpensive adulterants were used to inflate different products, leading to severe health issues. Contamination of food has many causes and can be physical(plant stems in tea), chemical (melamine in infant formula), or biological (bacterial contamination). Employing suitable sensor systems along the production process is a requirement for food safety. In this article, different approaches to food inspection are illustrated, and exemplary scenarios outline the potential of different sensor systems along the spectrum.

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