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2024
Krauss, H., Habich, T.-L., Bartholdt, M., Seel, T., & Schappler, M. (2024). Domain-Decoupled Physics-informed Neural Networks with Closed-Form Gradients for Fast Model Learning of Dynamical Systems. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics https://doi.org/10.5220/0012935200003822
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 (Band 4). Artikel GT2024-123663 (Proceedings of the ASME Turbo Expo; Band 4). https://doi.org/10.1115/gt2024-123663
Kuhlgatz, T., Jordine, M., Lehmann, D., & Seel, T. (2024). On Stair Walk Recognition Using a Single Magnetometer-free IMU and Deep Learning. Current Directions in Biomedical Engineering, 10(4), 404-407. https://doi.org/10.1515/cdbme-2024-2099
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) (S. 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 (S. 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
Meindl, M., Bachhuber, S., & Seel, T. (2024). AI-MOLE: Autonomous Iterative Motion Learning for unknown nonlinear dynamics with extensive experimental validation. Control engineering practice, 145, Artikel 105879. https://doi.org/10.1016/j.conengprac.2024.105879
Meindl, M., Mönkemöller, R., & Seel, T. (2024). Autonomous Iterative Motion Learning (AI-MOLE) of a SCARA Robot for Automated Myocardial Injection. IFAC-PapersOnLine, 58(24), 380-385. https://doi.org/10.48550/arXiv.2409.06361, https://doi.org/10.1016/j.ifacol.2024.11.067
Meindl, M., Bachhuber, S., & Seel, T. (2024). Reference-Adapting Iterative Learning Control for Motion Optimization in Constrained Environments. In 2024 IEEE 63rd Conference on Decision and Control, CDC 2024 (S. 4143-4150). (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC56724.2024.10886730
Meindl, M., Campe, F., Lehmann, D., & Seel, T. (2024). Solving Motion Tasks with Challenging Dynamics by Combining Kinodynamic Motion Planning and Iterative Learning Control. In 2024 European Control Conference, ECC 2024 (S. 1208-1213). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC64448.2024.10590944
Moenkemoeller, R., Budde, L., Martin, U., Martens, A., & Seel, T. (2024). Towards Less Invasive Instruments For Cardiac Tissue Stabilizing. Current Directions in Biomedical Engineering, 10(4), 445–448. https://doi.org/10.1515/cdbme-2024-2109
Mohammad, A., Muscheid, H., Schappler, M., & Seel, T. (2024). Quantifying Uncertainties of Contact Classifications in a Human-Robot Collaboration with Parallel Robots. In C. Piazza, P. Capsi-Morales, L. Figueredo, M. Keppler, & H. Schütze (Hrsg.), Human-Friendly Robotics 2023: HFR: 16th International Workshop on Human-Friendly Robotics (1. Aufl., S. 137-150). (Springer Proceedings in Advanced Robotics (SPAR); Nr. 29). Springer. https://doi.org/10.1007/978-3-031-55000-3_10
Pawluchin, A., Meindl, M., Weygers, I., Seel, T., & Boblan, I. (2024). Gaussian process-based nonlinearity compensation for pneumatic soft actuators. At-Automatisierungstechnik, 72(5), 440-448. https://doi.org/10.1515/auto-2023-0237
Rostalski, P., Schanze, T., & Seel, T. (2024). Special issue AUTOMED. At-Automatisierungstechnik, 72(5), 387-388. https://doi.org/10.1515/auto-2024-0057
Schäfke, H., Habich, T.-L., Muhmann, C., Ehlers, S., Seel, T., & Schappler, M. (2024). Learning-Based Nonlinear Model Predictive Control of Articulated Soft Robots Using Recurrent Neural Networks. IEEE Robotics and Automation Letters, 9(12), 11609-11616. Artikel 11609. https://doi.org/10.1109/LRA.2024.3495579
Seel, T., Kolditz, T., Overmeyer, L., Lukas, M., Leineweber, S., Leineweber, A., Orimi, A. G., & Kuhlgatz, T. (2024). KI im Maschinenbau: Zu den Auswirkungen und Veränderungen in Wissenschaft und Arbeitswelt. Uni-Magazin, Hannover, 1(2), 12-16. https://doi.org/10.15488/17789
Stüde, M. (2024). Environment and task modeling of long-term-autonomous service robots. [Dissertation, Gottfried Wilhelm Leibniz Universität Hannover]. Leibniz Universität Hannover. https://doi.org/10.15488/16370
Tantau, M. (2024). Strukturidentifikation und Unterscheidbarkeit von Modellen für elektromechanische Antriebsstränge: Structure identification and distinguishability for models of electromechanical drive trains. [Dissertation, Gottfried Wilhelm Leibniz Universität Hannover]. Leibniz Universität Hannover. https://doi.org/10.15488/17543
Weber, D. O. M., Gühmann, C., & Seel, T. (2024). FranSys—A Fast Non-Autoregressive Recurrent Neural Network for Multi-Step Ahead Prediction. IEEE ACCESS, 12, 145130 - 145147. https://doi.org/10.1109/ACCESS.2024.3473014
Weiss, M., Stirling, A., Pawluchin, A., Lehmann, D., Hannemann, Y., Seel, T., & Boblan, I. (2024). Achieving Velocity Tracking Despite Model Uncertainty for a Quadruped Robot with a PD-ILC Controller. In 2024 European Control Conference, ECC 2024 (S. 134-140). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC64448.2024.10590932