@article{KunkelGebhardtMpofuetal.2019,
  author    = {Kunkel, Maximilian Hugo and Gebhardt, Andreas and Mpofu, Khumbulani and Kallweit, Stephan},
  title     = {Quality assurance in metal powder bed fusion via deep-learning-based image classification},
  series = {Rapid Prototyping Journal},
  volume    = {26},
  journal   = {Rapid Prototyping Journal},
  number    = {2},
  issn      = {1355-2546},
  doi       = {10.1108/RPJ-03-2019-0066},
  pages     = {259 -- 266},
  year      = {2019},
  language  = {en}
}
@article{EngemannCoenenDawaretal.2021,
  author    = {Engemann, Heiko and C{\"o}nen, Patrick and Dawar, Harshal and Du, Shengzhi and Kallweit, Stephan},
  title     = {A robot-assisted large-scale inspection of wind turbine blades in manufacturing using an autonomous mobile manipulator},
  series = {Applied Sciences},
  volume    = {11},
  journal   = {Applied Sciences},
  number    = {19},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2076-3417},
  doi       = {10.3390/app11199271},
  pages     = {1 -- 22},
  year      = {2021},
  abstract  = {Wind energy represents the dominant share of renewable energies. The rotor blades of a wind turbine are typically made from composite material, which withstands high forces during rotation. The huge dimensions of the rotor blades complicate the inspection processes in manufacturing. The automation of inspection processes has a great potential to increase the overall productivity and to create a consistent reliable database for each individual rotor blade. The focus of this paper is set on the process of rotor blade inspection automation by utilizing an autonomous mobile manipulator. The main innovations include a novel path planning strategy for zone-based navigation, which enables an intuitive right-hand or left-hand driving behavior in a shared human-robot workspace. In addition, we introduce a new method for surface orthogonal motion planning in connection with large-scale structures. An overall execution strategy controls the navigation and manipulation processes of the long-running inspection task. The implemented concepts are evaluated in simulation and applied in a real-use case including the tip of a rotor blade form.},
  language  = {en}
}
@inproceedings{EngemannBadriWenningetal.2019,
  author    = {Engemann, Heiko and Badri, Sriram and Wenning, Marius and Kallweit, Stephan},
  title     = {Implementation of an Autonomous Tool Trolley in a Production Line},
  series = {Advances in Service and Industrial Robotics. RAAD 2019. Advances in Intelligent Systems and Computing, vol 980},
  booktitle = {Advances in Service and Industrial Robotics. RAAD 2019. Advances in Intelligent Systems and Computing, vol 980},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-19648-6},
  doi       = {10.1007/978-3-030-19648-6_14},
  pages     = {117 -- 125},
  year      = {2019},
  language  = {en}
}
@article{LimpertWiesenFerreinetal.2019,
  author    = {Limpert, Nicolas and Wiesen, Patrick and Ferrein, Alexander and Kallweit, Stephan and Schiffer, Stefan},
  title     = {The ROSIN Project and its Outreach to South Africa},
  series = {R\&D Journal},
  volume    = {35},
  journal   = {R\&D Journal},
  pages     = {1 -- 6},
  year      = {2019},
  language  = {en}
}
@incollection{EngemannDuKallweitetal.2020,
  author    = {Engemann, Heiko and Du, Shengzhi and Kallweit, Stephan and Ning, Chuanfang and Anwar, Saqib},
  title     = {AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation},
  series = {Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020},
  booktitle = {Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020},
  publisher = {IOS Press},
  address   = {Amsterdam},
  isbn      = {978-1-64368-137-5},
  doi       = {10.3233/FAIA200770},
  pages     = {89 -- 97},
  year      = {2020},
  abstract  = {We present an automated pipeline for the generation of synthetic datasets for six-dimension (6D) object pose estimation. Therefore, a completely automated generation process based on predefined settings is developed, which enables the user to create large datasets with a minimum of interaction and which is feasible for applications with a high object variance. The pipeline is based on the Unreal 4 (UE4) game engine and provides a high variation for domain randomization, such as object appearance, ambient lighting, camera-object transformation and distractor density. In addition to the object pose and bounding box, the metadata includes all randomization parameters, which enables further studies on randomization parameter tuning. The developed workflow is adaptable to other 3D objects and UE4 environments. An exemplary dataset is provided including five objects of the Yale-CMU-Berkeley (YCB) object set. The datasets consist of 6 million subsegments using 97 rendering locations in 12 different UE4 environments. Each dataset subsegment includes one RGB image, one depth image and one class segmentation image at pixel-level.},
  language  = {en}
}
@article{EngemannDuKallweitetal.2020,
  author    = {Engemann, Heiko and Du, Shengzhi and Kallweit, Stephan and C{\"o}nen, Patrick and Dawar, Harshal},
  title     = {OMNIVIL - an autonomous mobile manipulator for flexible production},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {24, art. no. 7249},
  publisher = {MDPI},
  address   = {Basel},
  isbn      = {1424-8220},
  doi       = {10.3390/s20247249},
  pages     = {1 -- 30},
  year      = {2020},
  language  = {en}
}