@inproceedings{NiemuellerNeumannHenkeetal.2017, author = {Niemueller, Tim and Neumann, Tobias and Henke, Christoph and Sch{\"o}nitz, Sebastian and Reuter, Sebastian and Ferrein, Alexander and Jeschke, Sabina and Lakemeyer, Gerhard}, title = {Improvements for a robust production in the RoboCup logistics league 2016}, series = {RoboCup 2016: Robot World Cup XX. RoboCup 2016.}, booktitle = {RoboCup 2016: Robot World Cup XX. RoboCup 2016.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-68792-6}, doi = {10.1007/978-3-319-68792-6_49}, pages = {589 -- 600}, year = {2017}, language = {en} } @article{NiemuellerKarrasFerrein2017, author = {Niemueller, Tim and Karras, Ulrich and Ferrein, Alexander}, title = {Meisterschaft der Maschinen: Die Industrial Logistic Liga}, series = {C´t Magazin f{\"u}r Computertechnik}, journal = {C´t Magazin f{\"u}r Computertechnik}, number = {26}, year = {2017}, language = {de} } @article{FerreinSteinbauer2016, author = {Ferrein, Alexander and Steinbauer, Gerald}, title = {The Interplay of Aldebaran and RoboCup}, series = {KI - K{\"u}nstliche Intelligenz}, volume = {30}, journal = {KI - K{\"u}nstliche Intelligenz}, number = {3-4}, publisher = {Springer}, address = {Berlin}, issn = {1610-1987}, doi = {10.1007/s13218-016-0440-1}, pages = {325 -- 326}, year = {2016}, language = {en} } @article{SteinbauerFerrein2016, author = {Steinbauer, Gerald and Ferrein, Alexander}, title = {20 Years of RoboCup}, series = {KI - K{\"u}nstliche Intelligenz}, volume = {30}, journal = {KI - K{\"u}nstliche Intelligenz}, number = {3-4}, publisher = {Springer}, address = {Berlin}, issn = {1610-1987}, doi = {10.1007/s13218-016-0442-z}, pages = {221 -- 224}, year = {2016}, language = {en} } @article{FerreinSteinbauer2016, author = {Ferrein, Alexander and Steinbauer, Gerald}, title = {Looking back on 20 Years of RoboCup}, series = {KI - K{\"u}nstliche Intelligenz}, volume = {30}, journal = {KI - K{\"u}nstliche Intelligenz}, number = {3-4}, publisher = {Springer}, address = {Berlin}, issn = {1610-1987}, doi = {10.1007/s13218-016-0443-y}, pages = {321 -- 323}, year = {2016}, language = {en} } @inproceedings{MarcoFerrein2017, author = {Marco, Heather G. and Ferrein, Alexander}, title = {AGNES: The African-German Network of Excellence in Science}, series = {Proceedings of the 2nd Developing World Robotics Forum, Workshop at IEEE AFRICON 2017}, booktitle = {Proceedings of the 2nd Developing World Robotics Forum, Workshop at IEEE AFRICON 2017}, pages = {1 -- 2}, year = {2017}, language = {en} } @inproceedings{SchifferFerrein2017, author = {Schiffer, Stefan and Ferrein, Alexander}, title = {A System Layout for Cognitive Service Robots}, series = {Cognitive Robot Architectures. Proceedings of EUCognition 2016}, booktitle = {Cognitive Robot Architectures. Proceedings of EUCognition 2016}, issn = {1613-0073}, pages = {44 -- 45}, year = {2017}, language = {en} } @inproceedings{StopforthDavrajhFerrein2017, author = {Stopforth, Riaan and Davrajh, Shaniel and Ferrein, Alexander}, title = {Design considerations of the duo fugam dual rotor UAV}, series = {2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)}, booktitle = {2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)}, isbn = {978-1-5386-2314-5}, doi = {10.1109/RoboMech.2017.8261115}, pages = {7 -- 13}, year = {2017}, language = {en} } @inproceedings{FerreinSchollNeumannetal.2019, author = {Ferrein, Alexander and Scholl, Ingrid and Neumann, Tobias and Kr{\"u}ckel, Kai and Schiffer, Stefan}, title = {A system for continuous underground site mapping and exploration}, doi = {10.5772/intechopen.85859}, pages = {16 Seiten}, year = {2019}, language = {en} } @inproceedings{FerreinBharatheeshaSchifferetal.2019, author = {Ferrein, Alexander and Bharatheesha, Mukunda and Schiffer, Stefan and Corbato, Carlos Hernandez}, title = {TRROS 2018 : Teaching Robotics with ROS Workshop at ERF 2018; Proceedings of the Workshop on Teaching Robotics with ROS (held at ERF 2018), co-located with European Robotics Forum 2018 (ERF 2018), Tampere, Finland, March 15th, 2018}, series = {CEUR Workshop Proceedings}, booktitle = {CEUR Workshop Proceedings}, number = {Vol-2329}, issn = {1613-0073}, pages = {68 Seiten}, year = {2019}, language = {en} } @article{ClaerFerreinSchiffer2019, author = {Claer, Mario and Ferrein, Alexander and Schiffer, Stefan}, title = {Calibration of a Rotating or Revolving Platform with a LiDAR Sensor}, series = {Applied Sciences}, volume = {Volume 9}, journal = {Applied Sciences}, number = {issue 11, 2238}, publisher = {MDPI}, address = {Basel}, issn = {2076-3417}, doi = {10.3390/app9112238}, pages = {18 Seiten}, year = {2019}, language = {en} } @inproceedings{SteinbauerFerrein2019, author = {Steinbauer, Gerald and Ferrein, Alexander}, title = {CogRob 2018 : Cognitive Robotics Workshop. Proceedings of the 11th Cognitive Robotics Workshop 2018 co-located with 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018). Tempe, AZ, USA, October 27th, 2018.}, series = {CEUR workshop proceedings}, booktitle = {CEUR workshop proceedings}, number = {Vol-2325}, issn = {1613-0073}, pages = {46 Seiten}, year = {2019}, language = {en} } @article{FrankoDuKallweitetal.2020, author = {Franko, Josef and Du, Shengzhi and Kallweit, Stephan and Duelberg, Enno Sebastian and Engemann, Heiko}, title = {Design of a Multi-Robot System for Wind Turbine Maintenance}, series = {Energies}, volume = {13}, journal = {Energies}, number = {10}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {10.3390/en13102552}, pages = {Article 2552}, year = {2020}, abstract = {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.}, language = {en} } @article{UlmerBraunChengetal.2022, author = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg}, title = {Gamification of virtual reality assembly training: Effects of a combined point and level system on motivation and training results}, series = {International Journal of Human-Computer Studies}, volume = {165}, journal = {International Journal of Human-Computer Studies}, number = {Art. No. 102854}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1071-5819}, doi = {10.1016/j.ijhcs.2022.102854}, year = {2022}, abstract = {Virtual Reality (VR) offers novel possibilities for remote training regardless of the availability of the actual equipment, the presence of specialists, and the training locations. Research shows that training environments that adapt to users' preferences and performance can promote more effective learning. However, the observed results can hardly be traced back to specific adaptive measures but the whole new training approach. This study analyzes the effects of a combined point and leveling VR-based gamification system on assembly training targeting specific training outcomes and users' motivations. The Gamified-VR-Group with 26 subjects received the gamified training, and the Non-Gamified-VR-Group with 27 subjects received the alternative without gamified elements. Both groups conducted their VR training at least three times before assembling the actual structure. The study found that a level system that gradually increases the difficulty and error probability in VR can significantly lower real-world error rates, self-corrections, and support usages. According to our study, a high error occurrence at the highest training level reduced the Gamified-VR-Group's feeling of competence compared to the Non-Gamified-VR-Group, but at the same time also led to lower error probabilities in real-life. It is concluded that a level system with a variable task difficulty should be combined with carefully balanced positive and negative feedback messages. This way, better learning results, and an improved self-evaluation can be achieved while not causing significant impacts on the participants' feeling of competence.}, language = {en} } @inproceedings{ChajanSchulteTiggesRekeetal.2021, author = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas}, title = {GPU based model-predictive path control for self-driving vehicles}, series = {IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {New York, NY}, isbn = {978-1-7281-5394-0}, doi = {10.1109/IV48863.2021.9575619}, pages = {1243 -- 1248}, year = {2021}, abstract = {One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.}, language = {en} } @article{BraunChengDoweyetal.2021, author = {Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg}, title = {Performance evaluation of skill-based order-assignment in production environments with multi-agent systems}, series = {IEEE Journal of Emerging and Selected Topics in Industrial Electronics}, journal = {IEEE Journal of Emerging and Selected Topics in Industrial Electronics}, number = {Early Access}, publisher = {IEEE}, address = {New York}, issn = {2687-9735}, doi = {10.1109/JESTIE.2021.3108524}, year = {2021}, abstract = {The fourth industrial revolution introduces disruptive technologies to production environments. One of these technologies are multi-agent systems (MASs), where agents virtualize machines. However, the agent's actual performances in production environments can hardly be estimated as most research has been focusing on isolated projects and specific scenarios. We address this gap by implementing a highly connected and configurable reference model with quantifiable key performance indicators (KPIs) for production scheduling and routing in single-piece workflows. Furthermore, we propose an algorithm to optimize the search of extrema in highly connected distributed systems. The benefits, limits, and drawbacks of MASs and their performances are evaluated extensively by event-based simulations against the introduced model, which acts as a benchmark. Even though the performance of the proposed MAS is, on average, slightly lower than the reference system, the increased flexibility allows it to find new solutions and deliver improved factory-planning outcomes. Our MAS shows an emerging behavior by using flexible production techniques to correct errors and compensate for bottlenecks. This increased flexibility offers substantial improvement potential. The general model in this paper allows the transfer of the results to estimate real systems or other models.}, language = {en} } @article{FerreinSteinbauer2016, author = {Ferrein, Alexander and Steinbauer, Gerald}, title = {20 Years of RoboCup - A Subjective Retrospection}, series = {KI - K{\"u}nstliche Intelligenz}, volume = {30}, journal = {KI - K{\"u}nstliche Intelligenz}, number = {3}, publisher = {Springer}, address = {Berlin}, issn = {1610-1987}, doi = {10.1007/s13218-016-0449-5}, pages = {225 -- 232}, year = {2016}, abstract = {This summer, RoboCup competitions were held for the 20th time in Leipzig, Germany. It was the second time that RoboCup took place in Germany, 10 years after the 2006 RoboCup in Bremen. In this article, we give an overview on the latest developments of RoboCup and what happened in the different leagues over the last decade. With its 20th edition, RoboCup clearly is a success story and a role model for robotics competitions. From our personal view point, we acknowledge this by giving a retrospection about what makes RoboCup such a success.}, language = {en} } @inproceedings{SchleupenEngemannBagherietal.2017, author = {Schleupen, Josef and Engemann, Heiko and Bagheri, Mohsen and Kallweit, Stephan and Dahmann, Peter}, title = {Developing a climbing maintenance robot for tower and rotor blade service of wind turbines}, series = {Advances in Robot Design and Intelligent Control : Proceedings of the 25th Conference on Robotics in Alpe-Adria-Danube Region (RAAD16)}, booktitle = {Advances in Robot Design and Intelligent Control : Proceedings of the 25th Conference on Robotics in Alpe-Adria-Danube Region (RAAD16)}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-49058-8}, doi = {10.1007/978-3-319-49058-8_34}, pages = {310 -- 319}, year = {2017}, 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} }