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  - 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  - 
TY  - JOUR
A1  - Franko, Josef
A1  - Du, Shengzhi
A1  - Kallweit, Stephan
A1  - Duelberg, Enno Sebastian
A1  - Engemann, Heiko
T1  - Design of a Multi-Robot System for Wind Turbine Maintenance
JF  - Energies
N2  - The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.
Y1  - 2020
U6  - http://dx.doi.org/10.3390/en13102552
SN  - 1996-1073
VL  - 13
IS  - 10
SP  - Article 2552
PB  - MDPI
CY  - Basel
ER  -