Towards an Autonomous Compost Turner: Current State of Research

This preprint presents the current status of research into the development and application of an autonomous, self-driving compost turner. The aim is to overcome challenges in the composting industry, such as adverse working conditions, by automating the composting process. The preprint provides a comprehensive overview of the overall concept of the self-driving compost turner, including the hardware architecture with sensors, navigation module and control module. In addition, the methodical development of the architecture of concepts, models and their subsequent software integration in ROS using model-based systems engineering is described. The validation and verification of the overall system is carried out in an industrial environment using three scenarios. The capabilities of the compost turner are demonstrated by autonomously following predefined trajectories in the composting plant and performing the required composting tasks. The results show that the autonomous compost turner is capable of performing the required activities. In addition, the compost turner has intelligent processing capabilities for compost data as well as its transmission, visualization and storage in a cloud server. It is important to note that this work is a preprint that represents the current state of research. The authors aim to publish the full paper in a peer-reviewed journal in the near future

Bridging GNSS Outages with IMU and Odometry: A Case Study for Agricultural Vehicles

Nowadays, many precision farming applications rely on the use of GNSS-RTK. However, when it comes to autonomous agricultural vehicles, GNSS cannot be used as a stand-alone system for positioning. To ensure high availability and robustness of the positioning solution, GNSS-RTK must be fused with additional sensors. This paper presents a novel sensor fusion algorithm tailored to tracked agricultural vehicles. GNSS-RTK, an IMU and wheel speed sensors are fused in an error-state Kalman filter to estimate position and attitude of the vehicle. An odometry model for tracked vehicles is introduced which is used to propagate the filter state. By using both IMU and wheel speed sensors, specific motion characteristics of tracked vehicles such as slippage can be included in the dynamic model. The presented sensor fusion algorithm is tested at a composting site using a tracked compost turner. The sensor measurements are recorded using the Robot Operating System (ROS). To analyze the achievable accuracies for position and attitude of the vehicle, a precise reference trajectory is measured using two robotic total stations. The resulting trajectory of the error-state filter is then compared to the reference trajectory. To analyze how well the proposed error-state filter is suited to bridge GNSS outages, GNSS outages of 30 s are simulated in post-processing. During these outages, the vehicle’s state is propagated using the wheel speed sensors, IMU, and the dynamic model for tracked vehicles. The results show that after 30 s of GNSS outage, the estimated horizontal position of the vehicle still has a sub-decimetre accuracy

"Die neue Schule ist schon gut" - Rekonstruktion des Übergangs von der Volksschule in die Sekundarstufe 1 aus der Sicht von SchülerInnen

Der Wechsel von der Volksschule in die weiterführende Schule ist für alle Kinder ein wichtiges Lebensereignis. Nichtsdestotrotz ist nur wenig darüber bekannt, wie Kinder diesen Übergang erleben. Durch den biografischen Zugang der hier vorgestellten Arbeit werden Einblicke in die Wahrnehmungen und die Gefühlswelt der Kinder eröffnet und ihre Erwartungen, Freuden und Ängste dokumentiert. Der Grossteil der SchülerInnen sieht dem Schulwechsel freudig entgegen. Diese Freude bleibt auch am Beginn der Sekundarstufe I bestehen. Die überwiegend positiven Einstellungen schliessen aber nicht aus, dass sich Kinder um bestimmte Themen in der neuen Schule sorgen

Bridging GNSS Outages with IMU and Odometry: A Case Study for Agricultural Vehicles

Nowadays, many precision farming applications rely on the use of GNSS-RTK. However, when it comes to autonomous agricultural vehicles, GNSS cannot be used as a stand-alone system for positioning. To ensure high availability and robustness of the positioning solution, GNSS-RTK must be fused with additional sensors. This paper presents a novel sensor fusion algorithm tailored to tracked agricultural vehicles. GNSS-RTK, an IMU and wheel speed sensors are fused in an error-state Kalman filter to estimate position and attitude of the vehicle. An odometry model for tracked vehicles is introduced which is used to propagate the filter state. By using both IMU and wheel speed sensors, specific motion characteristics of tracked vehicles such as slippage can be included in the dynamic model. The presented sensor fusion algorithm is tested at a composting site using a tracked compost turner. The sensor measurements are recorded using the Robot Operating System (ROS). To analyze the achievable accuracies for position and attitude of the vehicle, a precise reference trajectory is measured using two robotic total stations. The resulting trajectory of the error-state filter is then compared to the reference trajectory. To analyze how well the proposed error-state filter is suited to bridge GNSS outages, GNSS outages of 30 s are simulated in post-processing. During these outages, the vehicle’s state is propagated using the wheel speed sensors, IMU, and the dynamic model for tracked vehicles. The results show that after 30 s of GNSS outage, the estimated horizontal position of the vehicle still has a sub-decimetre accuracy

Freudeerleben in der Grundschule

Das Freudeerleben im Schulalltag kann unterschiedliche Ursachen haben. Die Autorinnen des Basisartikels "Freude" führen diesbezüglich aus, dass in der Grundschule Lern- und Leistungserfolge zu positiven Emotionen führen und darüber hinaus soziale Emotionen, das heißt Wertschätzung von Mitschülern und Lehrpersonen, zum Freudeerleben führen. In diesem Kontext werden drei psychologische Grundbedürfnisse erfüllt: das Bedürfnis nach Kompetenz, das Bedürfnis nach sozialer Eingebundenheit sowie das Bedürfnis nach Autonomie. Im zweiten Teil geben die Verfasserinnen einige Praxistipps, um Freudeerleben zu fördern: Ausgestaltung einer positiven Lehrer-Schüler-Beziehung, Schaffung eines lernförderlichen Klimas in der Klasse, Einsetzen von differenzierenden Unterrichtsformen, angemessene Bezugsnormorientierung sowie Anbieten von relevanten Unterrichtsinhalten mit Mitbestimmungsmöglichkeiten. Dem Beitrag sind zwei Selbstreflexionsmaterialien (Freude-Karten, Freude-Tagebuch) angefügt

Automated Route Planning from LiDAR Point Clouds for Agricultural Applications

This paper develops an algorithm to compute optimal routes for an autonomous compost turner. In commercial composting, the material to be composted is piled up in large heaps called windrows and turned regularly by compost turners. The environment at the composting site is constantly changing, as the locations of the windrows change with each turning procedure. Therefore, we propose a novel method that automatically computes routes on a composting plant from LiDAR data. The LiDAR is mounted on the compost turner together with a dual-antenna GNSS receiver, an IMU, and rotary encoders. An extended Kalman filter is used to obtain the vehicle’s pose. Through direct georeferencing, a global point cloud is obtained. The routing algorithm crops, segments, and filters the point cloud until the points along the ridge of each windrow remain. These points are used to compute the optimal routes along each windrow. Furthermore, a user can select the windrows which need to be turned and the algorithm then computes the most efficient path for the compost turner, which also includes the passages between the windrows. The method was tested within a simulation environment using a 3D model of the composting site. The results show that the algorithm detects the windrows and computes the routes with sufficient accuracy for autonomous compost turning