TY  - JOUR
A1  - Kunkel, Maximilian Hugo
A1  - Gebhardt, Andreas
A1  - Mpofu, Khumbulani
A1  - Kallweit, Stephan
T1  - Quality assurance in metal powder bed fusion via deep-learning-based image classification
JF  - Rapid Prototyping Journal
Y1  - 2019
U6  - http://dx.doi.org/10.1108/RPJ-03-2019-0066
SN  - 1355-2546
VL  - 26
IS  - 2
SP  - 259
EP  - 266
ER  - 
TY  - JOUR
A1  - Engemann, Heiko
A1  - Cönen, Patrick
A1  - Dawar, Harshal
A1  - Du, Shengzhi
A1  - Kallweit, Stephan
T1  - A robot-assisted large-scale inspection of wind turbine blades in manufacturing using an autonomous mobile manipulator
JF  - Applied Sciences
N2  - 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.
KW  - mobile manipulation
KW  - large-scale inspection
KW  - wind turbine production
KW  - autonomous navigation
KW  - surface-orthogonal path planning
Y1  - 2021
U6  - http://dx.doi.org/10.3390/app11199271
SN  - 2076-3417
N1  - Belongs to the Special Issue "Advances in Industrial Robotics and Intelligent Systems"
VL  - 11
IS  - 19
SP  - 1
EP  - 22
PB  - MDPI
CY  - Basel
ER  - 
TY  - CHAP
A1  - Engemann, Heiko
A1  - Badri, Sriram
A1  - Wenning, Marius
A1  - Kallweit, Stephan
T1  - Implementation of an Autonomous Tool Trolley in a Production Line
T2  - Advances in Service and Industrial Robotics. RAAD 2019. Advances in Intelligent Systems and Computing, vol 980
Y1  - 2019
SN  - 978-3-030-19648-6
U6  - http://dx.doi.org/10.1007/978-3-030-19648-6_14
SP  - 117
EP  - 125
PB  - Springer
CY  - Cham
ER  - 
TY  - JOUR
A1  - Limpert, Nicolas
A1  - Wiesen, Patrick
A1  - Ferrein, Alexander
A1  - Kallweit, Stephan
A1  - Schiffer, Stefan
T1  - The ROSIN Project and its Outreach to South Africa
JF  - R&D Journal
Y1  - 2019
VL  - 35
SP  - 1
EP  - 6
ER  - 
TY  - CHAP
A1  - Engemann, Heiko
A1  - Du, Shengzhi
A1  - Kallweit, Stephan
A1  - Ning, Chuanfang
A1  - Anwar, Saqib
T1  - AutoSynPose: Automatic Generation of Synthetic Datasets for 6D Object Pose Estimation
T2  - Machine Learning and Artificial Intelligence. Proceedings of MLIS 2020
N2  - 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.
Y1  - 2020
SN  - 978-1-64368-137-5
U6  - http://dx.doi.org/10.3233/FAIA200770
N1  - Frontiers in Artificial Intelligence and Applications. Vol 332
SP  - 89
EP  - 97
PB  - IOS Press
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Engemann, Heiko
A1  - Du, Shengzhi
A1  - Kallweit, Stephan
A1  - Cönen, Patrick
A1  - Dawar, Harshal
T1  - OMNIVIL - an autonomous mobile manipulator for flexible production
JF  - Sensors
Y1  - 2020
SN  - 1424-8220
U6  - http://dx.doi.org/10.3390/s20247249
N1  - Special issue: Sensor Networks Applications in Robotics and Mobile Systems
VL  - 20
IS  - 24, art. no. 7249
SP  - 1
EP  - 30
PB  - MDPI
CY  - Basel
ER  -