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RoBivaL data corpus

Christian Backe; Malte Wirkus; Stefan Hinck; Jonathan Babel; Vadim Riedel; Nele Reichert; Andrej Kolesnikov; Tobias Stark; Jens Hilljegerdes; Hilmi Dogu Kücüker; Emir Barcic; Eduard Klink; Arno Ruckelshausen; Frank Kirchner
Zenodo, 4/2024.


This data corpus was produced during the RoBivaL project, by robotics and agriculture researchers from DFKI (German Research Center for Artificial Intelligence, Robotics Innovation Center) and HSO (Hochschule Osnabrück, University of Applied Sciences, Agro-Technicum), between August 2021 and October 2023. The RoBivaL project compared different robot locomotion concepts from both space research and agricultural applications on the basis of experiments conducted under agricultural conditions. Four robot systems were used, two of which (ARTEMIS & SherpaTT) have their origin in futuristic space applications, while the other two (Naio Oz & BoniRob) were developed specifically for agriculture. The robots were subjected to six experiments, addressing different challenges and requirements for agricultural applications. Since real-world soil conditions usually change with the seasons and can be expected to have a crucial impact on robot performance, the experimental soil conditions were controlled and varied on the two dimensions moisture (dry, moist, wet) and density (tilled, compacted), resulting in six soil condition options. Depending on the specific objectives, each experiment was conducted either on a subset or on all available soil conditions.


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