Showing results 1 - 20 out of 731
2024
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
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 (Eds.), Data Augmentation, Labelling, and Imperfections : Third MICCAI Workshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings (pp. 95-105). ( Lecture Notes in Computer Science ; Vol. 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
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 (pp. 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. Article 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
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), Article 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 IEEE Conference on Decision and Control Advance online publication. 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 (pp. 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, Article 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 (Eds.), Handbuch der Mess- und Automatisierungstechnik in der Produktion (pp. 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 (pp. 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
Jakubowitz, E., Schmidt, L., Obermeier, A., Spindeldreier, S., Windhagen, H., & Hurschler, C. (2024). Investigation of adaptive muscle synergy modulated motor responses to grasping perturbations. Scientific reports, 14(1), Article 18493. https://doi.org/10.1038/s41598-024-68386-8
Jocham, A. J., Laidig, D., Guggenberger, B., & Seel, T. (2024). Measuring highly accurate foot position and angle trajectories with foot-mounted IMUs in clinical practice. Gait and Posture, 108, 63-69. https://doi.org/10.1016/j.gaitpost.2023.11.002
Kuhlgatz, T., Ihler, S., Bonhage, M., & Seel, T. (2024). Deep Learning Based Crack Detection in Inhomogeneous X-Ray Images for High Pressure Turbine Blades in Aviation. In Controls, Diagnostics, and Instrumentation (Vol. 4). Article GT2024-123663 (Proceedings of the ASME Turbo Expo; Vol. 4). https://doi.org/10.1115/gt2024-123663
Lampe, N., Ehlers, S. F. G., Kortmann, K.-P., Westerkamp, C., & Seel, T. (2024). Model-Based Maximum Friction Coefficient Estimation for Road Surfaces with Gradient or Cross-Slope. In Conference Proceedings - 2024 35th IEEE Intelligent Vehicles Symposium (IV) (pp. 2141-2147). (IEEE Intelligent Vehicles Symposium ). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IV55156.2024.10588642
Lilge, S., Nuelle, K., Childs, J. A., Wen, K., Rucker, D. C., & Burgner-Kahrs, J. (2024). Parallel-Continuum Robots: A Survey. IEEE transactions on robotics, 40, 3252-3270. https://doi.org/10.1109/TRO.2024.3415230
Mehl, M., Bartholdt, M., Ehlers, S. F. G., Seel, T., & Schappler, M. (2024). Adaptive State Estimation with Constant-Curvature Dynamics Using Force-Torque Sensors with Application to a Soft Pneumatic Actuator. In 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 (pp. 14939-14945). (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA57147.2024.10610370