@inproceedings{UlmerBraunChengetal.2021,
  author    = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg},
  title     = {Adapting Augmented Reality Systems to the users' needs using Gamification and error solving methods},
  series = {Procedia CIRP},
  volume    = {104},
  booktitle = {Procedia CIRP},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2212-8271},
  doi       = {10.1016/j.procir.2021.11.024},
  pages     = {140 -- 145},
  year      = {2021},
  abstract  = {Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users' preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen.},
  language  = {en}
}
@inproceedings{HueningStuettgen2021,
  author    = {H{\"u}ning, Felix and St{\"u}ttgen, Marcel},
  title     = {Work in Progress: Interdisciplinary projects in times of COVID-19 crisis - challenges, risks and chances},
  series = {2021 IEEE Global Engineering Education Conference (EDUCON)},
  booktitle = {2021 IEEE Global Engineering Education Conference (EDUCON)},
  doi       = {10.1109/EDUCON46332.2021.9454006},
  pages     = {1175 -- 1179},
  year      = {2021},
  language  = {en}
}
@inproceedings{FerreinMeessenLimpertetal.2021,
  author    = {Ferrein, Alexander and Meeßen, Marcus and Limpert, Nicolas and Schiffer, Stefan},
  title     = {Compiling ROS Schooling Curricula via Contentual Taxonomies},
  series = {Robotics in Education},
  booktitle = {Robotics in Education},
  editor    = {Lepuschitz, Wilfried},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-67411-3},
  doi       = {10.1007/978-3-030-67411-3_5},
  pages     = {49 -- 60},
  year      = {2021},
  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{DeyElsenFerreinetal.2021,
  author    = {Dey, Thomas and Elsen, Ingo and Ferrein, Alexander and Frauenrath, Tobias and Reke, Michael and Schiffer, Stefan},
  title     = {CO2 Meter: a do-it-yourself carbon dioxide measuring device for the classroom},
  series = {PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference},
  booktitle = {PETRA 2021: The 14th PErvasive Technologies Related to Assistive Environments Conference},
  editor    = {Makedon, Fillia},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  isbn      = {9781450387927},
  doi       = {10.1145/3453892.3462697},
  pages     = {292 -- 299},
  year      = {2021},
  abstract  = {In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.},
  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},
  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}
}