@inproceedings{ThenentDahmann2011,
  author    = {Thenent, N. E. and Dahmann, Peter},
  title     = {Hydrostatic propeller drive},
  series = {Proceedings of the conference : 18 - 20 May, 2011 Tampere, Finland / the Twelth Scandinavian International Conference on Fluid Power, SICFP'11. Ed.: Harri Sairiala ... Vol. 1},
  booktitle = {Proceedings of the conference : 18 - 20 May, 2011 Tampere, Finland / the Twelth Scandinavian International Conference on Fluid Power, SICFP'11. Ed.: Harri Sairiala ... Vol. 1},
  address   = {Tampere},
  isbn      = {978-952-15-2517-9},
  pages     = {217 -- 227},
  year      = {2011},
  language  = {en}
}
@inproceedings{ThomaFisherBertrandetal.2020,
  author    = {Thoma, Andreas and Fisher, Alex and Bertrand, Olivier and Braun, Carsten},
  title     = {Evaluation of possible flight strategies for close object evasion from bumblebee experiments},
  series = {Living Machines 2020: Biomimetic and Biohybrid Systems},
  booktitle = {Living Machines 2020: Biomimetic and Biohybrid Systems},
  editor    = {Vouloutsi, Vasiliki and Mura, Anna and Tauber, Falk and Speck, Thomas and Prescott, Tony J. and Verschure, Paul F. M. J.},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-64312-6},
  doi       = {10.1007/978-3-030-64313-3_34},
  pages     = {354 -- 365},
  year      = {2020},
  language  = {en}
}
@inproceedings{ThomaFisherBraun2020,
  author    = {Thoma, Andreas and Fisher, Alex and Braun, Carsten},
  title     = {Improving the px4 avoid algorithm by bio-inspired flight strategies},
  series = {DLRK2020 - „Luft- und Raumfahrt - Verantwortung in allen Dimensionen"},
  booktitle = {DLRK2020 - „Luft- und Raumfahrt - Verantwortung in allen Dimensionen"},
  doi       = {10.25967/530183},
  pages     = {10 Seiten},
  year      = {2020},
  language  = {en}
}
@article{ThomaGardiFisheretal.2024,
  author    = {Thoma, Andreas and Gardi, Alessandro and Fisher, Alex and Braun, Carsten},
  title     = {Improving local path planning for UAV flight in challenging environments by refining cost function weights},
  series = {CEAS Aeronautical Journal},
  journal   = {CEAS Aeronautical Journal},
  publisher = {Springer},
  address   = {Wien},
  issn      = {1869-5590 (eISSN)},
  doi       = {10.1007/s13272-024-00741-x},
  pages     = {12 Seiten},
  year      = {2024},
  abstract  = {Unmanned Aerial Vehicles (UAV) constantly gain in versatility. However, more reliable path planning algorithms are required until full autonomous UAV operation is possible. This work investigates the algorithm 3DVFH* and analyses its dependency on its cost function weights in 2400 environments. The analysis shows that the 3DVFH* can find a suitable path in every environment. However, a particular type of environment requires a specific choice of cost function weights. For minimal failure, probability interdependencies between the weights of the cost function have to be considered. This dependency reduces the number of control parameters and simplifies the usage of the 3DVFH*. Weights for costs associated with vertical evasion (pitch cost) and vicinity to obstacles (obstacle cost) have the highest influence on the failure probability of the local path planner. Environments with mainly very tall buildings (like large American city centres) require a preference for horizontal avoidance manoeuvres (achieved with high pitch cost weights). In contrast, environments with medium-to-low buildings (like European city centres) benefit from vertical avoidance manoeuvres (achieved with low pitch cost weights). The cost of the vicinity to obstacles also plays an essential role and must be chosen adequately for the environment. Choosing these two weights ideal is sufficient to reduce the failure probability below 10\%.},
  language  = {en}
}
@article{ThomaRavi2019,
  author    = {Thoma, Andreas and Ravi, Sridhar},
  title     = {Significance of parallel computing on the performance of Digital Image Correlation algorithms in MATLAB},
  pages     = {1 -- 17},
  year      = {2019},
  abstract  = {Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one of the undeformed reference state of a specimen and another of the deformed target state, the relative displacement between those two states is determined. DIC is well known and often used for post-processing analysis of in-plane displacements and deformation of specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and extend the field of use of this technique. Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether real-time analysis is possible with these methods. To reflect improvements in computing technology different hardware settings were also analysed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm such that it becomes practically slower than a suboptimal algorithm. The Newton-Raphson algorithm in combination with a modified Particle Swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss-Newton algorithm is superior. As expected, the Brute Force Search algorithm is the least effective method. We also found that the correct choice of parallelization tasks is crucial to achieve improvements in computing speed. A poorly chosen parallelisation approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode the correct choice of combinations of integerpixel and sub-pixel search algorithms is decisive for an efficient analysis. Using currently available hardware realtime analysis at high framerates remains an aspiration.},
  language  = {en}
}
@inproceedings{ThomaStiemerBraunetal.2023,
  author    = {Thoma, Andreas and Stiemer, Luc and Braun, Carsten and Fisher, Alex and Gardi, Alessandro G.},
  title     = {Potential of hybrid neural network local path planner for small UAV in urban environments},
  series = {AIAA SCITECH 2023 Forum},
  booktitle = {AIAA SCITECH 2023 Forum},
  publisher = {AIAA},
  address   = {Reston, Va.},
  doi       = {10.2514/6.2023-2359},
  pages     = {13 Seiten},
  year      = {2023},
  abstract  = {This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17\%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.},
  language  = {en}
}
@article{ThomaThomessenGardietal.2023,
  author    = {Thoma, Andreas and Thomessen, Karolin and Gardi, Alessandro and Fisher, A. and Braun, Carsten},
  title     = {Prioritising paths: An improved cost function for local path planning for UAV in medical applications},
  series = {The Aeronautical Journal},
  journal   = {The Aeronautical Journal},
  number    = {First View},
  publisher = {Cambridge University Press},
  address   = {Cambridge},
  issn      = {0001-9240 (Print)},
  doi       = {10.1017/aer.2023.68},
  pages     = {1 -- 18},
  year      = {2023},
  abstract  = {Even the shortest flight through unknown, cluttered environments requires reliable local path planning algorithms to avoid unforeseen obstacles. The algorithm must evaluate alternative flight paths and identify the best path if an obstacle blocks its way. Commonly, weighted sums are used here. This work shows that weighted Chebyshev distances and factorial achievement scalarising functions are suitable alternatives to weighted sums if combined with the 3DVFH* local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in various environments. The standard 3DVFH* uses a weighted sum and has a failure probability of 50\% in the test environments. A factorial achievement scalarising function, which minimises the worst combination of two out of four objective functions, reaches a failure probability of 26\%; A weighted Chebyshev distance, which optimises the worst objective, has a failure probability of 30\%. These results show promise for further enhancements and to support broader applicability.},
  language  = {en}
}
@article{ThomessenThomaBraun2023,
  author    = {Thomessen, Karolin and Thoma, Andreas and Braun, Carsten},
  title     = {Bio-inspired altitude changing extension to the 3DVFH* local obstacle avoidance algorithm},
  series = {CEAS Aeronautical Journal},
  journal   = {CEAS Aeronautical Journal},
  publisher = {Springer},
  address   = {Wien},
  issn      = {1869-5590 (Online)},
  doi       = {10.1007/s13272-023-00691-w},
  pages     = {11 Seiten},
  year      = {2023},
  abstract  = {Obstacle avoidance is critical for unmanned aerial vehicles (UAVs) operating autonomously. Obstacle avoidance algorithms either rely on global environment data or local sensor data. Local path planners react to unforeseen objects and plan purely on local sensor information. Similarly, animals need to find feasible paths based on local information about their surroundings. Therefore, their behavior is a valuable source of inspiration for path planning. Bumblebees tend to fly vertically over far-away obstacles and horizontally around close ones, implying two zones for different flight strategies depending on the distance to obstacles. This work enhances the local path planner 3DVFH* with this bio-inspired strategy. The algorithm alters the goal-driven function of the 3DVFH* to climb-preferring if obstacles are far away. Prior experiments with bumblebees led to two definitions of flight zone limits depending on the distance to obstacles, leading to two algorithm variants. Both variants reduce the probability of not reaching the goal of a 3DVFH* implementation in Matlab/Simulink. The best variant, 3DVFH*b-b, reduces this probability from 70.7 to 18.6\% in city-like worlds using a strong vertical evasion strategy. Energy consumption is higher, and flight paths are longer compared to the algorithm version with pronounced horizontal evasion tendency. A parameter study analyzes the effect of different weighting factors in the cost function. The best parameter combination shows a failure probability of 6.9\% in city-like worlds and reduces energy consumption by 28\%. Our findings demonstrate the potential of bio-inspired approaches for improving the performance of local path planning algorithms for UAV.},
  language  = {en}
}
@article{TrillaGrossenRobinsonetal.2008,
  author    = {Trilla, Joan and Grossen, J{\"u}rgen and Robinson, Alexander and Funke, Harald and Bosschaerts, Walter and Hendrick, Patrick},
  title     = {Development of a hydrogen combustion chamber for an ultra micro gas turbine},
  series = {PowerMEMS 2008, 8th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications, microEMS 2008, 2nd Symposium on Micro Environmental Machine Systems, Sendai, JP, Nov 9-12, 2008},
  journal   = {PowerMEMS 2008, 8th International Workshop on Micro and Nanotechnology for Power Generation and Energy Conversion Applications, microEMS 2008, 2nd Symposium on Micro Environmental Machine Systems, Sendai, JP, Nov 9-12, 2008},
  pages     = {101 -- 104},
  year      = {2008},
  language  = {en}
}
@article{UlmerBraunChengetal.2023,
  author    = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg},
  title     = {A human factors-aware assistance system in manufacturing based on gamification and hardware modularisation},
  series = {International Journal of Production Research},
  journal   = {International Journal of Production Research},
  publisher = {Taylor \& Francis},
  issn      = {0020-7543 (Print)},
  doi       = {10.1080/00207543.2023.2166140},
  year      = {2023},
  abstract  = {Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers' cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.},
  language  = {en}
}