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2025
Dorschky, E., Nitschke, M., Mayer, M., Weygers, I., Gassner, H., Seel, T., Eskofier, B. M., & Koelewijn, A. D. (2025). Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions. Frontiers in Bioengineering and Biotechnology, 13, Artikel 1507162. https://doi.org/10.3389/fbioe.2025.1507162
Schappler, M. (2025). Dimensional Synthesis of Parallel Robots Using Bilevel Optimization for Design Optimization and Resolution of Functional Redundancy. Robotics, 14(3), Artikel 29. https://doi.org/10.3390/robotics14030029
2024
Bachhuber, S., Pawluchin, A., Pal, A., Boblan, I., & Seel, T. (2024). A Soft Robotic System Automatically Learns Precise Agile Motions Without Model Information. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (S. 11368-11373). (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS58592.2024.10801724, https://doi.org/10.48550/arXiv.2408.03754
Bachhuber, S., Weygers, I., & Seel, T. (2024). Dispelling Four Challenges in Inertial Motion Tracking with One Recurrent Inertial Graph-based Estimator (RING). IFAC-PapersOnLine, 58(24), 117-122. https://doi.org/10.48550/arXiv.2409.02502, https://doi.org/10.1016/j.ifacol.2024.11.022
Bachhuber, S., Weygers, I., Lehmann, D., Dombrowski, M., & Seel, T. (2024). Recurrent Inertial Graph-Based Estimator (RING): A Single Pluripotent Inertial Motion Tracking Solution. Transactions on Machine Learning Research, 2024, 1-30. Vorabveröffentlichung online.
Badilla Solórzano, J. A., Gellrich, N. C., Seel, T., & Ihler, S. (2024). Modular, Label-Efficient Dataset Generation for Instrument Detection for Robotic Scrub Nurses. In Y. Xue, C. Chen, C. Chen, L. Zuo, & Y. Liu (Hrsg.), Data Augmentation, Labelling, and Imperfections : Third MICCAI Workshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings (S. 95-105). ( Lecture Notes in Computer Science ; Band 14379). Springer. https://doi.org/10.1007/978-3-031-58171-7_10
Badilla-Solórzano, J., Ihler, S., & Seel, T. (2024). HybGrip: a synergistic hybrid gripper for enhanced robotic surgical instrument grasping. International journal of computer assisted radiology and surgery, 19(12), 2363-2370. https://doi.org/10.1007/s11548-024-03245-5
Bank, D., Fink, D., Ehlers, S. F. G., & Seel, T. (2024). Neural Network-Based Prediction of Vehicle Energy Consumption on Highways. In 2024 European Control Conference, ECC 2024 (S. 711-717). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC64448.2024.10590711
Bensch, M., Job, T. D., Habich, T. L., Seel, T., & Schappler, M. (2024). Physics-Informed Neural Networks for Continuum Robots: Towards Fast Approximation of Static Cosserat Rod Theory. In 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 (S. 17293-17299). (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA57147.2024.10610742
Budde, L., Hentschel, J., Ihler, S., & Seel, T. (2024). Achieving near-zero particle generation by simplicity of design—A compliant-mechanism-based gripper for clean-room environments. SLAS Technology, 29(4), 100148. Artikel 100148. https://doi.org/10.1016/j.slast.2024.100148, https://doi.org/10.15488/17840
Budde, L., Dreger, J., Egger, D., & Seel, T. (2024). Core-Shell Capsule Image Segmentation through Deep Learning with Synthetic Training Data. Current Directions in Biomedical Engineering, 10(4), 123–126. https://doi.org/10.1515/cdbme-2024-2030
Ehlers, S. F. G. (2024). Verfahren zur Zustandsschätzung im LKW-Trailer. [Dissertation, Gottfried Wilhelm Leibniz Universität Hannover]. Leibniz Universität Hannover. https://doi.org/10.15488/18100
Einfeldt, A. K., Budde, L., Ortigas-Vásquez, A., Sauer, A., Utz, M., & Jakubowitz, E. (2024). A new method called MiKneeSoTA to minimize knee soft-tissue artifacts in kinematic analysis. Scientific reports, 14(1), Artikel 20666. https://doi.org/10.1038/s41598-024-71409-z
Ewering, J.-H., Volkmann, B., Ehlers, S. F. G., Seel, T., & Meindl, M. B. (2024). Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline. In 2024 IEEE 63rd Conference on Decision and Control, CDC 2024 (S. 4157-4164). (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC56724.2024.10886241, https://doi.org/10.48550/arXiv.2409.09331
Ewering, J. H., Schwarz, C., Ehlers, S. F. G., Jacob, H. G., Seel, T., & Heckmann, A. (2024). Integrated Model Predictive Control of High-Speed Railway Running Gears with Driven Independently Rotating Wheels. IEEE Transactions on Vehicular Technology, 73(6), 7852-7865. https://doi.org/10.48550/arXiv.2309.09769, https://doi.org/10.1109/TVT.2024.3350699
Ewering, J.-H., Ziaukas, Z., Ehlers, S. F. G., & Seel, T. (2024). Reliable State Estimation in a Truck-Semitrailer Combination using an Artificial Neural Network-Aided Extended Kalman Filter. In 2024 European Control Conference, ECC 2024 (S. 456-463). IEEE. https://doi.org/10.48550/arXiv.2406.14028, https://doi.org/10.23919/ECC64448.2024.10590814
Fink, D., Maas, O., Herda, D., Ziaukas, Z., Schweers, C., Trabelsi, A., & Jacob, H. G. (2024). Data-Based Energy Demand Prediction for Hybrid Electrical Vehicles. SN Computer Science, 5, Artikel 192. https://doi.org/10.1007/s42979-023-02475-9
Habich, T.-L., Schappler, M., & Ortmaier, T. (2024). Grundlagen der Robotik. In U. Grünhaupt, & M. Ruskowski (Hrsg.), Handbuch der Mess- und Automatisierungstechnik in der Produktion (S. 1-14). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-62424-1_59-3
Habich, T. L., Haack, J., Belhadj, M., Lehmann, D., Seel, T., & Schappler, M. (2024). SPONGE: Open-Source Designs of Modular Articulated Soft Robots. IEEE Robotics and Automation Letters, 9(6), 5346-5353. https://doi.org/10.48550/arXiv.2404.10734, https://doi.org/10.1109/LRA.2024.3388855
Ihler, S., Kuhnke, F., Kuhlgatz, T., & Seel, T. (2024). Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (S. 2295-2304). (IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CVPRW63382.2024.00235