Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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Learning Petri net models from sensor data of conveying systems based on the merging of prefix and postfix trees

2022 , Windmann, Stefan

Petri nets are a common modeling approach for parallel processes such as transport operations in conveying systems. In industrial applications, the Petri net models are usually created manually, which involves a lot of effort, especially if the modeled systems change frequently. This paper introduces a new learning method to automatically generate Petri nets from sensor data acquired in conveying systems. The underlying approach is to create prefix and postfix trees of possible event sequences and to merge them into a compact graph, which can be transformed into a deterministic Petri net model of the conveying system. Experimental results show that the proposed method produces realistic Petri net models even for conveying systems with ambiguous events.

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DecaWave ultra-wideband warm-up error correction

2021 , Sidorenko, Juri , Schatz, Volker , Scherer-Negenborn, Norbert , Arens, Michael , Hugentobler, Urs

In the field of indoor localization, ultra-wideband (UWB) technology is no longer dispensable. The market demands that the UWB hardware has to be cheap, precise and accurate. These requirements lead to the popularity of the DecaWave UWB system. The great majority of the publications about this system deals with the correction of the signal power, hardware delay or clock drift. It has traditionally been assumed that this error only appears at the beginning of the operation and is caused by the warm-up process of the crystal. In this article, we show that the warm-up error is influenced by the same error source as the signal power. To our knowledge, no scientific publication has explicitly examined the warm-up error before. This work aims to close this gap and, moreover, to present a solution which does not require any external measuring equipment and only has to be carried out once. It is shown that the empirically obtained warm-up correction curve increases the accuracy for the twoway- ranging (TWR) significantly.

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Optimal multispectral sensor confgurations through machine learning for cognitive agriculture

2021 , Becker, Florian , Backhaus, Andreas , Johrden, Felix , Flitter, Merle

Hyperspectral sensor systems play a key role in the automation of work processes in the farming industry. Non-invasive measurements of plants allow for an assessment of the vitality and health state and can also be used to classify weeds or infected parts of a plant. However, one major downside of hyperspectral cameras is that they are not very cost-effective. In this paper, we show, that for specific tasks, multispectral systems with only a fraction of the wavelength bands and costs of a hyperspectral system can lead to promising results for regression and classification tasks. We conclude that for the ongoing automation efforts in the context of cognitive agriculture reduced multispectral systems are a viable alternative.

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New active learning algorithms for near-infrared spectroscopy in agricultural applications

2021 , Krause, Julius , Günder, Maurice , Schulz, Daniel , Gruna, Robin

The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.

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Grundlagen des Maschinellen Lernens

2021 , Bauckhage, Christian , Hübner, Wolfgang , Hug, Ronny , Paaß, Gerhard , Rüping, Stefan

Zu definieren, was die menschliche Intelligenz sowie intelligentes Handeln – und da­mit auch die Künstliche Intelligenz – ausmacht, ist außerordentlich schwer und be­schäftigt Philosophen und Psychologen seit Jahrtausenden. Allgemein anerkannt istaber, dass die Fähigkeit zu lernen ein zentrales Merkmal vonIntelligenzist. So ist auchdas Forschungsgebiet desMaschinellen Lernens(engl.machine learning, ML) ein zen­traler Teil der Künstlichen Intelligenz, das hinter vielen aktuellen Erfolgen von KI-Sys­temen steckt.

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Smart agriculture. Editorial

2021 , Beyerer, Jürgen , Bretthauer, Georg , Längle, Thomas

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Modular and scalable automation for field robots

2021 , Osten, Julia , Weyers, Catrin , Bregler, Kevin , Emter, Thomas , Petereit, Janko

This article describes a modular and scalable charging and navigation concept for electrified field robots and other agricultural machines. The concept consists of an underbody charging system on a trailer and a modular navigation box. The underlying conductive charging process is compared to other charging techniques. Charging time in relation to charging current and mean power consumption in field use is displayed. In the navigation box, data of various sensors are combined by means of multi-sensor fusion regarding the precise time of arrival. Time synchronization is achieved by a novel method for compensating the data latency jitter by employing Kalman based timestamp filtering. Furthermore, navigation functionalities, such as motion planning and mapping, are presented.

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Soil monitoring for precision farming using hyperspectral remote sensing and soil sensors

2021 , Schreiner, Simon , Culibrk, Dubravko , Bandecchi, Michele , Gross, Wolfgang , Middelmann, Wolfgang

This work describes an approach to calculate pedological parameter maps using hyperspectral remote sensing and soil sensors. These maps serve as information basis for automated and precise agricultural treatments by tractors and field robots. Soil samples are recorded by a handheld hyperspectral sensor and analyzed in the laboratory for pedological parameters. The transfer of the correlation between these two data sets to aerial hyperspectral images leads to 2D-parameter maps of the soil surface. Additionally, rod-like soil sensors provide local3D-information of pedological parameters under the soil surface. The goal is to combine the area-covering 2D parameter maps with the local 3D-information to extrapolate large-scale 3D-parameter maps using AI approaches.

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Tiefe neuronale Netze

2021 , Bauckhage, Christian , Hübner, Wolfgang , Hug, Ronny , Paaß, Gerhard

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An approach for Industrie 4.0-compliant and data-sovereign Digital Twins

2021 , Jacoby, Michael , Volz, Friedrich , Weißenbacher, Christian , Stojanovic, Ljiljana , Usländer, Thomas

Data sharing between enterprises requires both interoperability and data sovereignty. In the application domain of industrial production an integrated approach is required that encompasses standards and technologies of both Industrie 4.0 and the International Data Spaces(IDS). This paper describes how to combine them for the concept of Digital Twins following the architectural framework given in ISO DIS 23247. Furthermore, an implementation approach is described relying upon the Fraunhofer Advanced AAS Tools for Digital Twins (FA³ST). The resulting architectural approach may be combined with further open manufacturing standards, and may be applied for data analytics and the engineering of AI-based systems.

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