Publications

A selected list of publications from the IRTG researchers.

2021

  1. Graz, I. M., & Rosset, S. (2021). Stretchable electrodes for highly flexible electronics. Organic Flexible Electronics, 479--500. https://doi.org/10.1016/b978-0-12-818890-3.00016-3
  2. Maier, B., Stach, M., & Mehl, M. (2021). Real-Time, Dynamic Simulation of Deformable Linear Objects with Friction on a 2D Surface. In J. Billingsley & P. Brett (Eds.), Mechatronics and Machine Vision in Practice 4 (pp. 217--231). Springer International Publishing. https://doi.org/10.1007/978-3-030-43703-9_18
  3. Totounferoush, A., Simonis, F., Uekermann, B., & Schulte, M. (2021). Efficient and Scalable Initialization of Partitioned Coupled Simulations with preCICE. Algorithms, 14(6), Article 6. https://doi.org/10.3390/a14060166
  4. Wnuk, M., Jaensch, F., Tomzik, D. A., Chen, Z., Terfurth, J., Kandasamy, S., Shahabi, J., Garrett, A., Mahmoudinezhad, M. H., Csiszar, A., Xu, W. L., Röhrle, O., & Verl, A. (2021). Challenges in Robotic Soft Tissue Manipulation -- Problem Identification Based on an Interdisciplinary Case Study of a Teleoperated Drawing Robot in Practice. In J. Billingsley & P. Brett (Eds.), Mechatronics and Machine Vision in Practice 4 (pp. 245--262). Springer International Publishing. https://doi.org/10.1007/978-3-030-43703-9_20
  5. Wnuk, M., Hinze, C., Zürn, M., Pan, Q., Lechler, A., & Verl, A. (2021, November). Tracking Branched Deformable Linear Objects With Structure Preserved Registration by Branch-wise Probability Modification. 2021 IEEE 27th International Conference on Machatronics and Machine Vision in Practice.
  6. Zhang, Q., Xu, P., Zhang, Z. S., Strommel, M., & Verl, A. (2021). Discussion of Soft Tissue Manipulation for the Harvesting of Ovine Offal (J. Billingsley, Ed.). Springer, Germany.
  7. Zürn, M., Wnuk, M., Hinze, C., Lechler, A., Verl, A., & Xu, W. (2021). Kinematic Trajectory Following Control For Constrained Deformable Linear Objects. 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 1701–1707. https://doi.org/10.1109/CASE49439.2021.9551613

2020

  1. Ali, S. J. V., Cheng, L. K., & Xu, P. (2020). Soft Medical Robots-Revamping the Diagnostics and Therapeutics Technologies. Journal of Engineering and Science in Medical Diagnostics and Therapy, 3(3), Article 3. https://doi.org/10.1115/1.4047285
  2. Alla, A., Haasdonk, B., & Schmidt, A. (2020). Feedback control of parametrized PDEs via model order reduction and dynamic programming principle. Advances in Computational Mathematics, 46(1), Article 1. https://doi.org/10.1007/s10444-020-09744-8
  3. Ates, F., Brandenburg, J. E., & Kaufman, K. R. (2020). Effects of Selective Dorsal Rhizotomy on Ankle Joint Function in Patients With Cerebral Palsy. Frontiers in Pediatrics, 8(75), Article 75. https://doi.org/10.3389/fped.2020.00075
  4. Berberich, J., Köhler, J., Müller, M. A., & Allgöwer, F. (2020). Data-driven model predictive control with stability and robustness guarantees. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.2020.3000182
  5. Berberich, J., Köhler, J., Müller, M. A., & Allgöwer, F. (2020). Data-Driven Tracking MPC for Changing Setpoints. Proc.\ 21st IFAC World Congress.
  6. Berberich, J., Köhler, J., Müller, M. A., & Allgöwer, F. (2020). Robust constraint satisfaction in data-driven MPC. https://arxiv.org/abs/2003.06808
  7. Bhattacharya, D., Ali, S. J. V., Cheng, L. K., & Xu, P. (2020). RoSE: A Robotic Soft Esophagus for Endoprosthetic Stent Testing. Soft Robotics. https://doi.org/10.1089/soro.2019.0205
  8. Brandes, J. (2020). Simulation based control of an underactuated kinematic chain.
  9. Braun, H. (2020). Model Estimation and Model Predictive Control of Flexible Loads.
  10. Buchfink, P., Haasdonk, B., & Rave, S. (2020). PSD-Greedy Basis Generation for Structure-Preserving Model Order Reduction of Hamiltonian Systems. Algoritmy 2020 Conference Proceedings, 151--160. http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577
  11. Cheuk, M. L., Tang, E. J. L. P., HajiRassouliha, A., Han, J.-C., Nielsen, P. M. F., & Taberner, A. J. (2020). A Method for Markerless Tracking of the Strain Distribution of Actively Contracting Cardiac Muscle Preparations. Experimental Mechanics. https://doi.org/10.1007/s11340-020-00646-w
  12. Choisne, J., Fourrier, N., Handsfield, G., Signal, N., Taylor, D., Wilson, N., Stott, S., & Besier, T. F. (2020). An unsupervised data-driven model to classify gait patterns in children with cerebral palsy. Journal of Clinical Medicine, 9(5), Article 5. https://doi.org/10.3390/jcm9051432
  13. Debera, P. (2020). Comparison of path planning algorithms for a 7-axis lightweight robot.
  14. Dwivedi, A., Lara Aguayo, J. E., Cheng, L. K., Paskaranandavadivel, N., & Liarokapis, M. (2020). High-Density Electromyography Based Control of Robotic Devices: On the Execution of Dexterous Manipulation Tasks. Interfaces, 18, 19. https://doi.org/10.1109/icra40945.2020.9196629
  15. Echle, A.and Gong, Y., Terfurth, J., Schmid, M., & Parspour, N. (2020). FEA Based Comparison of BLDC and BLAC Modes for an Axial Flux Motor with Trapezoidal BEMF. 46th Annual Conference of the IEEE Industrial Electronics Society.
  16. Emamy, N., Litty, P., Klotz, T., Mehl, M., & Röhrle, O. (2020). POD-DEIM model order reduction for the monodomain reaction-diffusion sub-model of the neuro-muscular system. IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22--25, 2018, 177--190. https://doi.org/10.1007/978-3-030-21013-7_13
  17. Fernandez, J., Dickinson, A., & Hunter, P. (2020). Population based approaches to computational musculoskeletal modelling. Biomechanics and Modeling in Mechanobiology, 19(4), Article 4. https://doi.org/10.1007/s10237-020-01364-x
  18. Garrett, A. S., Loiselle, D. S., Han, J.-C., & Taberner, A. J. (2020). Compensating for changes in heart muscle resting heat production in a microcalorimeter. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2557--2560. https://doi.org/10.1109/embc44109.2020.9175474
  19. Gerez, L., Dwivedi, A., & Liarokapis, M. (2020). A Hybrid, Soft Exoskeleton Glove Equipped with a Telescopic Extra Thumb and Abduction Capabilities. IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.3389/fnbot.2019.00091
  20. Haasdonk, B., Wenzel, T., Santin, G., & Schmitt, S. (2020). Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods. CoRR, abs/2004.12670. https://arxiv.org/abs/2004.12670
  21. Handsfield, G. le, Greiner, J., Madl, J., Rog-Zielinska, E., Holville, E., Vanwanseele, B., & Shim, V. (2020). Achilles Subtendon Structure and Behavior As Evidenced from Tendon Imaging and Computational Modeling. Frontiers in Sports and Active Living, 2, 70. https://doi.org/10.3389/fspor.2020.00070
  22. Hinze, C. (2020). Guide: Get the Franka Emika Panda running in C++. https://github.com/chhinze/panda_tutorial
  23. Hinze, C., Wnuk, M., Zürn, M., Lechler, A., & Verl, A. (2020). Daten-integrierte Simulation: Lokalisierung biegeschlaffer Bauteile durch 3D-Stereovision.
  24. Hinze, C., Zürn, M., Wnuk, M., Lechler, A., & Verl, A. (2020). Nonlinear Trajectory Control for Deformable Linear Objects based on Physics Simulation. IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, 310–316. https://doi.org/10.1109/IECON43393.2020.9254923
  25. Hussan, J. R., & Hunter, P. J. (2020). Comfort Simulator: A Software Tool to Model Thermoregulation and Perception of Comfort. Journal of Open Research Software, 8. https://doi.org/10.5334/jors.288
  26. Illenberger, P. K., Rosset, S., Madawala, U. K., & Anderson, I. A. (2020). The integrated self priming circuit: an autonomous electrostatic energy harvester with voltage boosting. IEEE Transactions on Industrial Electronics, 1--1. https://doi.org/10.1109/tie.2020.3003591
  27. Jaung, R., Varghese, C., Lin, A. Y., Paskaranandavadivel, N., Du, P., Rowbotham, D., Dinning, P., O’Grady, G., & Bissett, I. (2020). High-Resolution Colonic Manometry Pressure Profiles Are Similar in Asymptomatic Diverticulosis and Controls. Digestive Diseases and Sciences. https://doi.org/10.1007/s10620-020-06320-4
  28. Jayaneththi, V., Aw, K., & McDaid, A. (2020). Wireless manipulation using magnetic polymer composites. Smart Materials and Structures, 29(3), Article 3. https://doi.org/10.1088/1361-665x/ab6691
  29. Kamat, A. A., Paskaranandavadivel, N., Alighaleh, S., Cheng, L. K., & Angeli, T. R. (2020). Effects of Electrode Diameter and Contact Material on Signal Morphology of Gastric Bioelectrical Slow Wave Recordings. Annals of Biomedical Engineering, 1--12. https://doi.org/10.1007/s10439-020-02457-5
  30. Killen, B. A., da Luz, S. B., Lloyd, D. G., Carleton, A. D., Zhang, J., Besier, T. F., & Saxby, D. J. (2020). Automated creation and tuning of personalised muscle paths for OpenSim musculoskeletal models of the knee joint. Biomechanics and Modeling in Mechanobiology. https://doi.org/10.1007/s10237-020-01398-1
  31. Köhler, J., Kötting, P., Soloperto, R., Allgöwer, F., & Müller, M. A. (2020). A robust adaptive model predictive control framework for nonlinear uncertain systems. Int. J. Robust and Nonlinear Control, 1–25.
  32. Köhler, J., Müller, M. A., & Allgöwer, F. (2020). A nonlinear tracking model predictive control scheme for unreachable dynamic target signals. Automatica, 118, 109030.
  33. Köhler, J., Müller, M. A., & Allgöwer, F. (2020). Periodic optimal control of nonlinear constrained systems using economic model predictive control. J. Proc. Contr., 92, 185–201.
  34. Köhler, J., Müller, M. A., & Allgöwer, F. (2020). A nonlinear model predictive control framework using reference generic terminal ingredients. IEEE Trans. Automat. Control, 65(8), Article 8.
  35. Köhler, J., Schwenkel, L., Koch, A., Berberich, J., Pauli, P., & Allgöwer, F. (2020). Robust and optimal predictive control of the COVID-19 outbreak. Annual Reviews in Control.
  36. Köhler, J., Soloperto, R., Müller, M. A., & Allgöwer, F. (2020). A computationally efficient robust model predictive control framework for uncertain nonlinear systems. IEEE Trans. Automat. Control.
  37. Köhler, J., Kötting, P., Soloperto, R., Allgöwer, F., & Müller, M. A. (2020). A robust adaptive model predictive control framework for nonlinear uncertain systems. International Journal of Robust and Nonlinear Control. https://doi.org/10.1002/rnc.5147
  38. Köhler, J., Müller, M. A., & Allgöwer, F. (2020). Implicit solutions to constrained nonlinear output regulation using MPC. Proc.\ 59th IEEE Conf.\ Decision and Control (CDC), 4604–4609.
  39. Köhler, J., Müller, M. A., & Allgöwer, F. (2020). Constrained nonlinear output regulation using model predictive control. IEEE Transactions on Automatic Control. https://arxiv.org/pdf/2005.12413.pdf
  40. Köhler, J., Müller, M. A., & Allgöwer, F. (2020). A nonlinear model predictive control framework using reference generic terminal ingredients. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.2019.2949350
  41. Köhler, J., Müller, M. A., & Allgöwer, F. (2020). Implicit solutions to constrained nonlinear output regulation using MPC. Proc. 59th Annual Conference on Decision and Control (CDC).
  42. Köhler, J., Müller, M. A., & Allgöwer, F. (2020). A nonlinear tracking model predictive control scheme for dynamic target signals. Automatica, 118, 109030. https://doi.org/10.1016/j.automatica.2020.109030
  43. Köhler, J., Schwenkel, L., Koch, A., Berberich, J., Pauli, P., & Allgöwer, F. (2020). Robust and optimal predictive control of the COVID-19 outbreak. https://arxiv.org/abs/2005.03580
  44. Köhler, J., Soloperto, R., Müller, M. A., & Allgöwer, F. (2020). A computationally efficient robust model predictive control framework for uncertain nonlinear systems. IEEE Transactions on Automatic Control. https://doi.org/10.1109/TAC.2020.2982585
  45. Lara Aguayo, J. E., Paskaranandavadivel, N., & Cheng, L. K. (2020). HD-EMG Electrode Count and Feature Selection Influence on Pattern-Based Movement Classification Accuracy. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4787--4790. https://doi.org/10.1109/embc44109.2020.9175210
  46. Lindner, F., Totounferoush, A., Mehl, M., Uekermann, B., Pour, N. E., Krupp, V., Roller, S., Reimann, T., C. Sternel, D., Egawa, R., Takizawa, H., & Simonis, F. (2020). ExaFSA: Parallel Fluid-Structure-Acoustic Simulation. Software for Exascale Computing - SPPEXA 2016-2019, 271--300. https://doi.org/10.1007/978-3-030-47956-5
  47. Lotz, J. (2020). Design of a flexible real-time control method for a lightweight robot based on PREEMPT\_RT-Linux.
  48. Mahmoudinezhad, M., Anderson, I. A., & Rosset, S. (2020). Interdigitated sensor based on a silicone foam for subtle robotic manipulation. Submitted to Macromolecular Rapid Communications.
  49. Mahmoudinezhad, M. H., Anderson, I., & Rosset, S. (2020). Compressible dielectric elastomer sensor for robotic application. In Second International Congress on Progress in Tissue Engineering and Regenerative medicine (Vol. 11375, p. 1137527). International Society for Optics and Photonics. https://doi.org/10.1117/12.2558386
  50. Mahmoudinezhad, M. H., Rosset, S., & Anderson, I. (2020). Interdigitated soft capacitive compression sensor for robotic Application. Soft Robotics Journal.
  51. Meinel, J., Vorobyov, V., Yavkin, B., Dasari, D., Sumiya, H., Onoda, S., Isoya, J., & Wrachtrup, J. (2020). Heterodyne Sensing of Microwaves with a Quantum Sensor. ArXiv Preprint ArXiv:2008.10068. https://arxiv.org/pdf/2008.09716.pdf
  52. Mesmer, P., Neubauer, M., Lechler, A., & Verl, A. (2020). Drive-Based Vibration Damping Control for Robot Machining. IEEE Robotics and Automation Letters, 5(2), Article 2. https://doi.org/10.1109/lra.2019.2960723
  53. Mordhorst, M. (2020). Towards a fast and stable dynamic skeletal muscle model.
  54. Moretti, G., Rosset, S., Vertechy, R., Anderson, I., & Fontana, M. (2020). A Review of Dielectric Elastomer Generator Systems. Advanced Intelligent Systems, 2(10), Article 10. https://doi.org/10.1002/aisy.202000125
  55. Mostashiri, N. (2020). An In-Vitro Study of the Temporomandibular Reaction Forces through Motion-Capturing and Robotics.
  56. Mostashiri, N., Dhupia, J. S., Verl, A. W., & Xu, P. (2020). Disturbance Observer-Based Controller for Mimicking Mandibular Motion and Studying Temporomandibular Joint Reaction Forces by a Chewing Robot. 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 1042--1047. https://doi.org/10.1109/aim43001.2020.9158891
  57. Nejati, M., Penhall, N., Williams, H., Bell, J., Lim, J., Ahn, H. S., & MacDonald, B. (2020). Kiwifruit detection in challenging conditions. https://arxiv.org/pdf/2006.11729.pdf
  58. Nickerson, D. P., Lundeng\aard, K., Watts, J., Porter, S., Yu, T., Nielsen, P., & Hunter, P. (2020). Physiome: Encouraging Reproducible and FAIR Computational Modelling. The FASEB Journal, 34(S1), Article S1. https://doi.org/10.1096/fasebj.2020.34.s1.04897
  59. Noble, D., & Hunter, P. (2020). How to link genomics to physiology through epigenomics. Epigenomics, 12(4), Article 4. https://doi.org/10.2217/epi-2020-0012
  60. Nubert, J., Köhler, J., Berenz, V., Allgöwer, F., & Trimpe, S. (2020). Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control. IEEE Robotics and Automation Letters, 5(2), Article 2.
  61. Nurbert, J., Köhler, J., Berenz, V., Allgöwer, F., & Trimpe, S. (2020). Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2020.2975727
  62. Pace, F. D., Gorjup, G., Bai, H., Sanna, A., Liarokapis, M., & Billinghurst, M. (2020, November). Assessing the Suitability and Effectiveness of Mixed Reality Interfaces for Accurate Robot Teleoperation. 26th ACM Symposium on Virtual Reality Software and Technology. https://doi.org/10.1145/3385956.3422092
  63. Pan, Q. (2020). Method for localization and state estimation of branched deformable linear objects in real time (working title).
  64. Paskaranandavadivel, N., Avci, R., Nagahawatte, N., Farajidavar, A., & Cheng, L. K. (2020). Electroceutical approaches for Gastroparesis in ‘Obesity and Diabetes: New Surgical and Nonsurgical Approaches.’
  65. Quigan, S. (2020). Real-time capable method for estimating the state of linear deformable objects from point cloud data.
  66. Rosenfelder, M., Köhler, J., & Allgöwer, F. (2020). Stability and performance in transient average constrained economic MPC without terminal constraints. Proc.\ 21st IFAC World Congress.
  67. Rosenfelder, M., Köhler, J., & Allgöwer, F. (2020). Stability and performance in transient average constrained economic MPC without terminal constraints. Proc.\ 21st IFAC World Congress.
  68. Saini, H., Ackland, D. C., Gong, L., Cheng, L. K., & Röhrle, O. (2020). Occlusal load modelling significantly impacts the predicted tooth stress response during biting: a simulation study. Computer Methods in Biomechanics and Biomedical Engineering, 1--10. https://doi.org/10.1080/10255842.2020.1711886
  69. Santin, G., & Haasdonk, B. (2020). Kernel Methods for Surrogate Modelling. Model Order Reduction, 1.
  70. Sawodny, J. (2020). Identification of dynamics parameters of deformable linear objects for simulation driven robotic manipulation.
  71. Scheufele, K., Subramanian, S., Mang, A., Biros, G., & Mehl, M. (2020). Image-driven biophysical tumor growth model calibration. SIAM Journal on Scientific Computing, 42(3), Article 3. https://doi.org/10.1137/19m1275280
  72. Schlatter, S., Grasso, G., Rosset, S., & Shea, H. (2020). Inkjet Printing of Complex Soft Machines with Densely Integrated Electrostatic Actuators. Advanced Intelligent Systems, 2000136. https://doi.org/10.1002/aisy.202000136
  73. Schlüter, H., & Allgöwer, F. (2020). A Constraint-Tightening Approach to Nonlinear Stochastic Model Predictive Control under General Bounded Disturbances. Proc. 21th IFAC World Congress, 53(2), Article 2. https://doi.org/10.1016/j.ifacol.2020.12.518
  74. Schlüter, H., & Allgöwer, F. (2020). A Constraint-Tightening Approach to Nonlinear Stochastic Model Predictive Control for Systems under General Disturbances. In Proc. 21st IFAC World Congress. IFAC. https://arxiv.org/abs/1912.01946
  75. Soloperto, R., Köhler, J., & Allgöwer, F. (2020). Augmenting MPC schemes with active learning: Intuitive tuning and guaranteed performance. IEEE Control Systems Letters, 4(3), Article 3. https://doi.org/10.1109/lcsys.2020.2983384
  76. Terfurth, J., Schmid, M., & Parspour, N. (2020). Planar Aligned Transverse Flux Machine with Integrated Reduction Gear. In 2020 IEEE International Conference on Electrical Machines (ICEM).
  77. Tomalka, A., Weidner, S., Hahn, D., Seiberl, W., & Siebert, T. (2020). Cross-Bridges and Sarcomeric Non-cross-bridge Structures Contribute to Increased Work in Stretch-Shortening Cycles. Frontiers in Physiology, 11, 921. https://doi.org/10.3389/fphys.2020.00921
  78. Venkatasubramanian, J., Köhler, J., Berberich, J., & Allgöwer, F. (2020). Robust Dual Control based on Gain Scheduling. Proc. 59th Annual Conference on Decision and Control (CDC).
  79. Vorobyov, V., Zaiser, S., Abt, N., Meinel, J., Dasari, D., Neumann, P., & Wrachtrup, J. (2020). Quantum Fourier transform for quantum sensing. https://arxiv.org/abs/2008.09716
  80. Walter, J., Günther, M., Haeufle, D. F. B., & Schmitt, S. (2020). Synthesising biological movement of muscle-driven systems: a geometry- and actuator-based control architecture. Biological Cybernetics, (Submitted).
  81. Walter, J. R., Saini, H., Maier, B., Mostashiri, N., Aguayo, J. L., Zarshenas, H., Hinze, C., Shuva, S., Köhler, J., Sahrmann, A. S., Chang, C., Csiszar, A., Galliani, S., Cheng, L. K., & Röhrle, O. (2020). Comparative Study of a Biomechanical Model-based and Black-box Approach for Subject-Specific Movement Prediction.
  82. Wang, L., Malik, A., Roop, P. S., Cheng, L. K., Paskaranandavadivel, N., & Ai, W. (2020). A novel approach for model-based design of gastric pacemakers. Computers in Biology and Medicine, 116, 103576. https://doi.org/10.1016/j.compbiomed.2019.103576
  83. Wirzberger, M., Borst, J. P., Krems, J. F., & Rey, G. D. (2020). Memory-related cognitive load effects in an interrupted learning task: A model-based explanation. Trends in Neuroscience and Education. https://doi.org/10.1016/j.tine.2020.100139
  84. Wnuk, M. (2020). Roboterprogrammierung für weiche Bauteile. https://www.konstruktion-entwicklung.de/roboterprogrammierung-fuer-weiche-bauteile
  85. Wnuk, M. (2020). grk2198 Soft Tissue Robotics: Introduction to Dynamics Animation and Robotics Toolbox (DART). https://github.com/markuswnuk91/tutorial_on_DART
  86. Wnuk, M., Hinze, C., Lechler, A., & Verl, A. (2020). Kinematic Multibody Model Generation of Deformable Linear Objects from Point Clouds. In 2020 International Conference on Intelligent Robots and Systems (IROS).
  87. Wnuk, M., Hinze, C., Zürn, M., Lechler, A., & Verl, A. (2020). Demonstrator zur Handhabung biegeschlaffer Objekte.
  88. Yeung, S., Fernandez, J., Handsfield, G., Walker, C., Besier, T., & Zhang, J. (2020). Rapid muscle volume prediction using anthropometric measurements and population-derived statistical models. Biomechanics and Modeling in Mechanobiology, 19(4), Article 4. https://doi.org/10.1007/s10237-019-01243-0
  89. Zarshenas, H., Ruddy, B., Kempa-Liehr, A., & Besier, T. (2020). Ankle torque forecasting using time-delayed neural networks. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/embc44109.2020.9175376

2019

  1. Aghababaie, Z., Chan, C. A., Paskaranandavadivel, N., Beyder, A., Farrugia, G., Asirvatham, S., O’Grady, G., Cheng, L. K., & Angeli, T. R. (2019). Feasibility of High-Resolution Electrical Mapping for Characterizing Conduction Blocks Created by Gastric Ablation. Conf Proc IEEE Eng Med Biol Soc, 2019, 170–173. https://doi.org/10.1109/EMBC.2019.8856406
  2. Alighaleh, S., Cheng, L. K., Angeli, T. R., Amiri, M., Sathar, S., O’Grady, G., & Paskaranandavadivel, N. (2019). A Novel Gastric Pacing Device to Modulate Slow Waves and Assessment by High-Resolution Mapping. IEEE Trans Biomed Eng, 66(10), Article 10. https://doi.org/10.1109/TBME.2019.2896624
  3. Avci, R., Paskaranandavadivel, N., Calder, S., Du, P., Bradshaw, L. A., & Cheng, L. K. (2019). Source localization for gastric electrical activity using simulated magnetogastrographic data. Conf Proc IEEE Eng Med Biol Soc, 2019, 2336–2339. https://doi.org/10.1109/EMBC.2019.8857384
  4. Bannwarth, J. X., Jeremy Chen, Z., Stol, K. A., MacDonald, B. A., & Richards, P. J. (2019). Aerodynamic Force Modeling of Multirotor Unmanned Aerial Vehicles. AIAA Journal, 57(3), Article 3. https://doi.org/10.2514/1.j057165
  5. Brunner, F. D., Heemels, W. P. M. H., & Allgöwer, F. (2019). Event-triggered and self-triggered control for linear systems based on reachable sets. Automatica, 101, 15--26. https://doi.org/10.1016/j.automatica.2018.11.035
  6. Buchfink, P., Bhatt, A., & Haasdonk, B. (2019). Symplectic Model Order Reduction with Non-Orthonormal Bases. Mathematical and Computational Applications, 24(2), Article 2. https://doi.org/10.3390/mca24020043
  7. Chan, C. A., Aghababaie, Z., Paskaranandavadivel, N., Cheng, L. K., & Angeli, T. R. (2019). Methods for Visualization of Gastric Endoscopic Mapping Data From Three-Dimensional, Non-Uniform Electrode Arrays. Conf Proc IEEE Eng Med Biol Soc, 2019, 2222–2225. https://doi.org/10.1109/EMBC.2019.8857158
  8. Chang, C. M., Gerez, L., Elangovan, N., Zisimatos, A., & Liarokapis, M. (2019). On Alternative Uses of Structural Compliance for the Development of Adaptive Robot Grippers and Hands. Frontiers in Neurorobotics, 13, 91. https://doi.org/10.3389/fnbot.2019.00091
  9. Chang, C.-M., Gerez, L., Elangovan, N., Zisimatos, A., & Liarokapis, M. (2019). Unconventional Uses of Structural Compliance in Adaptive Hands. 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 1--7. https://doi.org/10.1109/ro-man46459.2019.8956340
  10. Cherian Abraham, A., Cheng, L. K., Angeli, T. R., Alighaleh, S., & Paskaranandavadivel, N. (2019). Dynamic slow-wave interactions in the rabbit small intestine defined using high-resolution mapping. Neurogastroenterol Motil, 31(9), Article 9. https://doi.org/10.1111/nmo.13670
  11. Chigateri, N. G., Kerse, N., & MacDonald, B. (2019, June). Novel Algorithm for Identification and Differentiation of Shuffling from Walking. 2019 IEEE International Conference on Healthcare Informatics (ICHI). https://doi.org/10.1109/ichi.2019.8904831
  12. Csiszar, A. (2019). AI for Control Technology. In AI Forum Stuttgart.
  13. Csiszar, A., & Verl, A. (2019). Industrielle Steuerungen. In Handbuch Mensch-Roboter-Kollaboration (pp. 117--124). Carl Hanser Verlag München.
  14. Csiszar, A., Weiß, F., & Verl, A. (2019). Factorial Formulation of Dynamic Models for Robot Arms. Tagungsband Des 4. Kongresses Montage Handhabung Industrieroboter, 269--278. https://doi.org/10.1007/978-3-662-59317-2_27
  15. Driess, D., Schmitt, S., & Toussaint, M. (2019). Active Inverse Model Learning with Error and Reachable Set Estimates. International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros40897.2019.8967858
  16. Du, P., Grady, G. O., Paskaranandavadivel, N., Tang, S. J., Abell, T., & Cheng, L. K. (2019). High-resolution Mapping of Hyperglycemia-induced Gastric Slow Wave Dysrhythmias. J Neurogastroenterol Motil, 25(2), Article 2. https://doi.org/10.5056/jnm18192
  17. Du, P., O’Grady, G., Paskaranandavadivel, N., Tang, S., Abell, T., & Cheng, L. K. (2019). High-resolution mapping of hyperglycemia-induced gastric slow wave dysrhythmias. Journal of Neurogastroenterology and Motility, 25(2), Article 2. https://doi.org/10.5056/jnm18192
  18. Elangovan, N., Dwivedi, A., Gerez, L., Chang, C.-M., & Liarokapis, M. (2019). Employing IMU and ArUco Marker Based Tracking to Decode the Contact Forces Exerted by Adaptive Hands. 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), 525--530. https://doi.org/10.1109/humanoids43949.2019.9035051
  19. Ellwein, C., Schmidt, A., Lechler, A., & Riedel, O. (2019, October). Distributed Manufacturing. Proceedings of the 2019 3rd International Conference on Automation, Control and Robots. https://doi.org/10.1145/3365265.3365270
  20. Ens, B., Lanir, J., Tang, A., Bateman, S., Lee, G., Piumsomboon, T., & Billinghurst, M. (2019). Revisiting collaboration through mixed reality: The evolution of groupware. International Journal of Human-Computer Studies, 131, 81--98. https://doi.org/10.1016/j.ijhcs.2019.05.011
  21. Feger, M., Donovan, L., Herb, C., Handsfield, G., Blemker, S., Hart, J., Saliba, S., Abel, M., Park, J., & Hertel, J. (2019). Impairment-Based Rehabilitation Increases Lower Leg Muscle Volumes and Strength in Chronic Ankle Instability Patients: A Preliminary Study. https://doi.org/10.1123/jsr.2017-0136
  22. Garrett, A. S., Pham, T., Loiselle, D., Han, J.-C., & Taberner, A. (2019). Mechanical loading of isolated cardiac muscle with a real-time computed Windkessel model of the vasculature impedance. Physiological Reports, 7(17), Article 17. https://doi.org/10.14814/phy2.14184
  23. Gulde, R., Tuscher, M., Csiszar, A., Riedel, O., & Verl, A. (2019). Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems. 2019 Second International Conference on Artificial Intelligence for Industries (AI4I), 26--29. https://doi.org/10.1109/ai4i46381.2019.00015
  24. Han, H., Cheng, L. K., Angeli, T. R., & Paskaranandavadivel, N. (2019). Detection of Monophasic Slow-wave Activation Phase Using Wavelet Decomposition. Conf Proc IEEE Eng Med Biol Soc, 2019, 7157–7160. https://doi.org/10.1109/EMBC.2019.8856736
  25. Heinemann, T., Riedel, O., & Lechler, A. (2019). Generating Smooth Trajectories in Local Path Planning for Automated Guided Vehicles in Production. Procedia Manufacturing, 39, 98--105. https://doi.org/10.1016/j.promfg.2020.01.233
  26. Hinze, C., Tomzik, D. A., Lechler, A., Xu, X. W., & Verl, A. (2019). Control Architecture for Industrial Robotics based on Container Virtualization. Tagungsband Des 4. Kongresses Montage Handhabung Industrieroboter, 64--73. https://doi.org/10.1007/978-3-662-59317-2_7
  27. Hinze, C., Wnuk, M., Lechler, A., & Verl, A. (2019). Harte Echtzeit für weiche Materialien. Atp Magazin, 61, Article 61. https://doi.org/10.17560/atp.v61i11-12.2446
  28. Hussan, J. R., Roberts, P., Hamilton, M., Gerneke, D., & Hunter, P. J. (2019). Bimodal behavior in fabric drying kinetics: An interpretation of modes. Textile Research Journal, 89(23–24), Article 23–24. https://doi.org/10.1177/0040517519848160
  29. Ibili, E., & Billinghurst, M. (2019). Assessing the Relationship between Cognitive Load and the Usability of a Mobile Augmented Reality Tutorial System: A Study of Gender Effects. International Journal of Assessment Tools in Education, 378--395. https://doi.org/10.21449/ijate.594749
  30. Imboden, M., de Coulon, E., Poulin, A., Dellenbach, C., Rosset, S., Shea, H., & Rohr, S. (2019). High-speed mechano-active multielectrode array for investigating rapid stretch effects on cardiac tissue. Nature Communications, 10(1), Article 1.
  31. Jaensch, F., Csiszar, A., Sarbandi, J., & Verl, A. (2019). Reinforcement Learning of a Robot Cell Control Logic using a Software-in-the-Loop Simulation as Environment. 2019 Second International Conference on Artificial Intelligence for Industries (AI4I), 79–84. https://doi.org/10.1109/AI4I46381.2019.00027
  32. Jarrett, C., & McDaid, A. (2019). Modeling and Feasibility of an Elastomer-Based Series Elastic Actuator as a Haptic Interaction Sensor for Exoskeleton Robotics. IEEE/ASME Transactions on Mechatronics, 24(3), Article 3. https://doi.org/10.1088/1361-665x/ab6695
  33. Khuu, S., Virgilio, K. M., Fernandez, J. W., & Handsfield, G. (2019). Mechano-Physiological Modeling to Probe the Role of Satellite Cells and Fibroblasts in Cerebral Palsy Muscle Degeneration. International Symposium on Computer Methods in Biomechanics and Biomedical Engineering, 142--157. https://doi.org/10.1007/978-3-030-43195-2_11
  34. Kuruppu, S., Cheng, L. K., Angeli, T. R., Avci, R., & Paskaranandavadivel, N. (2019). A Framework for Spatiotemporal Analysis of Gastrointestinal Spike Burst Propagation. Conf Proc IEEE Eng Med Biol Soc, 2019, 4619–4622. https://doi.org/10.1109/EMBC.2019.8856924
  35. Kusumoto, R., Palmieri, L., Spies, M., Csiszar, A., & Arras, K. O. (2019). Informed Information Theoretic Model Predictive Control. 2019 International Conference on Robotics and Automation (ICRA), 2047--2053. https://doi.org/10.1109/icra.2019.8793945
  36. Köhler, J., Müller, M. A., & Allgöwer, F. (2019). A simple framework for nonlinear robust output-feedback MPC. Proc. 18th European Control Conference (ECC), 793–798.
  37. Köhler, J., Müller, M. A., & Allgöwer, F. (2019). Distributed model predictive control - Recursive feasibility under inexact dual optimization. Automatica, 102, 1–9.
  38. Köhler, J., Andina, E., Soloperto, R., Müller, M. A., & Allgöwer, F. (2019). Linear robust adaptive model predictive control: Computational complexity and conservatism. Proc. 58th IEEE Conference on Decision and Control (CDC), 1383–1388.
  39. Köhler, J., Müller, M. A., & Allgöwer, F. (2019). Nonlinear reference tracking: An economic model predictive control perspective. IEEE Trans. Autom. Control, 64, 254–269. https://doi.org/10.1109/tac.2018.2800789
  40. Lara Aguayo, J. E., Paskaranandavadivel, N., & Cheng, L. K. (2019). Effect of Segmentation Parameters on Classification Accuracy of High-Density EMG recordings. Conf Proc IEEE Eng Med Biol Soc, 2019, 6229–6232. https://doi.org/10.1109/embc.2019.8856809
  41. Maier, B., Emamy, N., Krämer, A., & Mehl, M. (2019). Highly Parallel Multi-Physics Simulation of Muscular Activation and EMG. Proceedings of VIII International Conference on Computational Methods for Coupled Problems in Science and Engineering.
  42. Meyer, C., Blemker, S., Handsfield, G., & Abel, M. F. (2019). Image-based identification of muscle abnormalities.
  43. Mostashiri, N., Chang, C., Wang, J., Dhupia, J. S., & Xu, P. (2019). In-vitro Measurement of Reaction Forces in the Temporomandibular Joints Using a Redundantly Actuated Parallel Chewing Robot. 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 1467--1472. https://doi.org/10.1109/aim.2019.8868649
  44. Mostashiri, N., Dhupia, J., & Xu, P. (2019). Redundancy in Parallel Robots: A Case Study of Kinematics of a Redundantly Actuated Parallel Chewing Robot. Lecture Notes in Mechanical Engineering, 65--78. https://doi.org/10.1007/978-981-13-8323-6_6
  45. O’Grady, G., Angeli, T. R., Paskaranandavadivel, N., Erickson, J. C., Wells, C. I., Gharibans, A. A., Cheng, L. K., & Du, P. (2019). Methods for High-Resolution Electrical Mapping in the Gastrointestinal Tract. IEEE Rev Biomed Eng, 12, 287–302. https://doi.org/10.1109/RBME.2018.2867555
  46. Paskaranandavadivel, N., Angeli, T. R., Manson, T., Stocker, Ared., McElmurray, L., O’Grady, G., Abell, T., & Cheng, L. K. (2019). Multi-day, multi-sensor ambulatory monitoring of gastric electrical activity. Physiol Meas, 40(2), Article 2. https://doi.org/10.1088/1361-6579/ab0668
  47. Paskaranandavadivel, N., Avci, R., & Cheng, L. K. (2019). Quantification of Dynamic Gastric Slow Wave Activity using Recurrence Plots. Conf Proc IEEE Eng Med Biol Soc, 2019, 729–732. https://doi.org/10.1109/EMBC.2019.8856679
  48. Pham, T., Zgierski-Johnston, C. M., Tran, K., Taberner, A. J., Loiselle, D. S., & Han, J.-C. (2019). Energy expenditure for isometric contractions of right and left ventricular trabeculae over a wide range of frequencies at body temperature. Scientific Reports, 9(1), Article 1. https://doi.org/10.1038/s41598-019-45273-1
  49. Piumsomboon, T., Dey, A., Ens, B., Lee, G., & Billinghurst, M. (2019). The Effects of Sharing Awareness Cues in Collaborative Mixed Reality. Frontiers in Robotics and AI, 6. https://doi.org/10.3389/frobt.2019.00005
  50. Pott, A., Tempel, P., Verl, A., & Wulle, F. (2019). Design, Implementation and Long-Term Running Experiences of the Cable-Driven Parallel Robot CaRo Printer. Mechanisms and Machine Science, 379--390. https://doi.org/10.1007/978-3-030-20751-9_32
  51. Potts, D., Loveys, K., Ha, H., Huang, S., Billinghurst, M., & Broadbent, E. (2019, June). ZenG. Proceedings of the 2019 on Creativity and Cognition. https://doi.org/10.1145/3325480.3326584
  52. Poulin, A., & Rosset, S. (2019). An open-loop control scheme to increase the speed and reduce the viscoelastic drift of dielectric elastomer actuators. Extreme Mechanics Letters, 27, 20–26.
  53. Romer, A., Berberich, J., Köhler, J., & Allgöwer, F. (2019). One-shot verification of dissipativity properties from input-output data. IEEE Control Systems Letters, 3, 709--714.
  54. Romer, A., Berberich, J., Köhler, J., & Allgöwer, F. (2019). One-shot verification of dissipativity properties from input--output data. IEEE Control Systems Letters, 3(3), Article 3. https://doi.org/10.1109/lcsys.2019.2917162
  55. Röhrle, O., Yavuz, U. S., Klotz, T., Negro, F., & Heidlauf, T. (2019). Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, e1457. https://doi.org/10.1002/wsbm.1457
  56. Sachdeva, N., Fan, I., Babcock, E., Burghoff, M., Chupp, T. 0. 167emE., Degenkolb, S., Fierlinger, P., Haude, S., Kraegeloh, E., Kilian, W., Knappe-Grüneberg, S., Kuchler, F., Liu, T., Marino, M., Meinel, J., Rolfs, K., Salhi, Z., Schnabel, A., Singh, J. 0. 167emT., … Voigt, J. (2019). New Limit on the Permanent Electric Dipole Moment of Xe 129 Using He 3 Comagnetometry and SQUID Detection. Physical Review Letters, 123(14), Article 14. https://doi.org/10.1103/physrevlett.123.143003
  57. Sahrmann, A. S., Stott, N. S., Besier, T. F., Fernandez, J. W., & Handsfield, G. (2019). Soleus muscle weakness in cerebral palsy: Muscle architecture revealed with Diffusion Tensor Imaging. PloS One, 14(2), Article 2. https://doi.org/10.1371/journal.pone.0205944
  58. Sasikumar, P., Gao, L., Bai, H., & Billinghurst, M. (2019). Wearable RemoteFusion: A Mixed Reality Remote Collaboration System with Local Eye Gaze and Remote Hand Gesture Sharing. 2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), 393--394. https://doi.org/10.1109/ismar-adjunct.2019.000-3
  59. Scheufele, K. (2019). Coupling Schemes and Inexact Newton for Multi-Physics and Coupled Optimization Problems (p. 276). http://www2.informatik.uni-stuttgart.de/cgi-bin/NCSTRL/NCSTRL_view.pl?id=DIS-2019-01&engl=1
  60. Scheufele, K., Subramanian, S., Mang, A., Biros, G., & Mehl, M. (2019). Image-driven biophysival tumor growth model calibration. SIAM Journal on Scientific Computing, 1--24. https://doi.org/10.1137/19m1275280
  61. Shultz, S. P., Millar, S.-K., Fink, P. W., Hébert-Losier, K., Handsfield, G., Sheerin, K., Wells, D., & Clarke, J. (2019). Improving engagement with biomechanics: student perspectives and a professional development initiative. Journal of Biomechanical Engineering, 141(12), Article 12. https://doi.org/10.1115/1.4044782
  62. Shultz, S., Hughes-Oliver, C., Wells, D., Sheerin, K., Fink, P., Handsfield, G., Hébert-Losier, K., Clarke, J., & Queen, R. (2019). Can research align with service? Lessons learned from the Big Experiment and National Biomechanics Day. Journal of Biomechanics, 87, 202--205. https://doi.org/10.1016/j.jbiomech.2019.03.006
  63. Soloperto, R., Köhler, J., Müller, M. A., & Allgöwer, F. (2019). Collision avoidance for uncertain nonlinear systems and moving obstacles using robust Model Predictive Control. Proc. 18th European Control Conference (ECC).
  64. Soloperto, R., Köhler, J., Müller, M. A., & Allgöwer, F. (2019). Collision avoidance for uncertain nonlinear systems with moving obstacles using robust Model Predictive Control. Proc.\ European Control Conf.\ (ECC), 811–817. https://doi.org/10.23919/ecc.2019.8796049
  65. Taberner, A., Nielsen, P., Johnston, C., Anderson, A., Cheuk, M., Garrett, A., Dowrick, J., Tang, E. L. P., HajiRassouliha, A., Ruddy, B., & others. (2019). A dynamometer for nature’s engines. IEEE Instrumentation & Measurement Magazine, 22(2), Article 2. https://doi.org/10.1109/mim.2019.8674628
  66. Terfurth, J., & Parspour, N. (2019). Integrated Planetary Gear Joint Actuator Concept for Wearable and Industrial Robotic Applications. 2019 Wearable Robotics Association Conference (WearRAcon), 28--33. https://doi.org/10.1109/wearracon.2019.8719400
  67. Terfurth, J., & Parspour, N. (2019). Concept of a Small Permanent Magnet Motor with External Spur Gear Rotor and High Torque Density. 2019 IEEE International Electric Machines & Drives Conference (IEMDC), 637--643. https://doi.org/10.1109/IEMDC.2019.8785303
  68. Terrano, W. A., Meinel, J., Sachdeva, N., Chupp, T. E., Degenkolb, S., Fierlinger, P., Kuchler, F., & Singh, J. T. (2019). Frequency shifts in noble-gas comagnetometers. Physical Review A, 100(1), Article 1. https://doi.org/10.1103/physreva.100.012502
  69. Theophilus, T., Lawrence, L., Lee, G. A., Billinghurst, M., & Adcock, M. (2019). Mixed reality remote collaboration combining 360 video and 3d reconstruction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1--14. https://doi.org/10.1145/3290605.3300431
  70. Tomalka, A., Röhrle, O., Han, J.-C., Pham, T., Taberner, A. J., & Siebert, T. (2019). Extensive eccentric contractions in intact cardiac trabeculae: revealing compelling differences in contractile behaviour compared to skeletal muscles. Proceedings of the Royal Society B: Biological Sciences, 286(1903), Article 1903. https://doi.org/10.1098/rspb.2019.0719
  71. Wang, L., Malik, A., Roop, P. S., Cheng, L. K., & Paskaranandavadivel, N. (2019). A Formal Approach for Scalable Simulation of Gastric ICC Electrophysiology. IEEE Trans Biomed Eng, 66(12), Article 12. https://doi.org/10.1109/TBME.2019.2904043
  72. Wells, C. I., Paskaranandavadivel, N., Lin, A. Y., Du, P., Penfold, J. A., Dinning, P., Cheng, L. K., Bissett, I. P., Arkwright, J. W., & O’Grady, G. (2019). Development and feasibility of an ambulatory acquisition system for fiber-optic high-resolution colonic manometry. Neurogastroenterol Motil, 31(12), Article 12. https://doi.org/10.1111/nmo.13704
  73. Williams, H., Nejati, M., Hussein, S., Penhall, N., Lim, J. Y., Jones, M. H., Bell, J., Ahn, H. S., Bradley, S., Schaare, P., Martinsen, P., Alomar, M., Patel, P., Seabright, M., Duke, M., Scarfe, A., & MacDonald, B. (2019). Autonomous pollination of individual kiwifruit flowers: Toward a robotic kiwifruit pollinator. Journal of Field Robotics, 37(2), Article 2. https://doi.org/10.1002/rob.21861
  74. Williams, H., Ting, C., Nejati, M., Jones, M. H., Penhall, N., Lim, J., Seabright, M., Bell, J., Ahn, H. S., Scarfe, A., Duke, M., & MacDonald, B. (2019). Improvements to and large-scale evaluation of a robotic kiwifruit harvester. Journal of Field Robotics, 37(2), Article 2. https://doi.org/10.1002/rob.21890
  75. Williams, H. A. M., Jones, M. H., Nejati, M., Seabright, M. J., Bell, J., Penhall, N. D., Barnett, J. J., Duke, M. D., Scarfe, A. J., Ahn, H. S., Lim, J., & MacDonald, B. A. (2019). Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosystems Engineering, 181, 140--156. https://doi.org/10.1016/j.biosystemseng.2019.03.007
  76. Wirzberger, M., Borst, J. P., Krems, J. F., & Rey, G. D. (2019). An ACT-R approach to investigating mechanisms of performance-related changes in an interrupted learning task. Proceedings of the 41st Annual Meeting of the Cognitive Science Society, 1206--1211. https://cogsci.mindmodeling.org/2019/papers/0220/0220.pdf
  77. Wnuk, M., Wenger, T., Lechler, A., & Verl, A. (2019). Nachgiebigkeit ist Einstellungssache. Handling, 9, Article 9.
  78. Zhang, H., Yu, H., Walcott, G. P., Paskaranandavadivel, N., Cheng, L. K., O’Grady, G., & Rogers, J. M. (2019). High-resolution optical mapping of gastric slow wave propagation. Neurogastroenterol Motil, 31(1), Article 1. https://doi.org/10.1111/nmo.13449
  79. Ziem, F., Garsi, M., Fedder, H., & Wrachtrup, J. (2019). Quantitative nanoscale MRI with a wide field of view. Scientific Reports, 9(1), Article 1. https://doi.org/10.1038/s41598-019-47084-w

2018

  1. Terfurth, J., & Parspour, N. (2019). Integrated Planetary Gear Joint Actuator Concept for Wearable and Industrial Robotic Applications. 2019 Wearable Robotics Association Conference (WearRAcon), 28--33. https://doi.org/10.1109/WEARRACON.2019.8719400
  2. Ahn, H. S., Jin, E., Zhu, G., & MacDonald, B. A. (2018, June). Design of Low Cost Pneumatic Robot Arm Control System. 2018 15th International Conference on Ubiquitous Robots (UR). https://doi.org/10.1109/urai.2018.8441836
  3. Angeli, T. R., O\textquotesingleGrady, G., Vather, R., Bissett, I. P., & Cheng, L. K. (2018). Intra-operative high-resolution mapping of slow wave propagation in the human jejunum: Feasibility and initial results. Neurogastroenterology & Motility, 30(7), Article 7. https://doi.org/10.1111/nmo.13310
  4. Ates, F., Temelli, Y., & Yucesoy, C. A. (2018). Effects of antagonistic and synergistic muscles’ co-activation on mechanics of activated spastic semitendinosus in children with cerebral palsy. Hum Mov Sci, 57, 103–110. https://doi.org/10.1016/j.humov.2017.11.011
  5. Ates, F., & Yucesoy, C. A. (2018). Botulinum toxin type-A affects mechanics of non-injected antagonistic rat muscles. J Mech Behav Biomed Mater, 84, 208–216. https://doi.org/10.1016/j.jmbbm.2018.05.027
  6. Broadbent, E., Feerst, D. A., Lee, S. H., Robinson, H., Albo-Canals, J., Ahn, H. S., & MacDonald, B. A. (2018). How could companion robots be useful in rural schools? International Journal of Social Robotics, 10(3), Article 3. https://doi.org/10.1007/s12369-017-0460-5
  7. Broadbent, E., Garrett, J., Jepsen, N., Ogilvie, V. L., Ahn, H. S., Robinson, H., Peri, K., Kerse, N., Rouse, P., Pillai, A., & MacDonald, B. (2018). Using Robots at Home to Support Patients With Chronic Obstructive Pulmonary Disease: Pilot Randomized Controlled Trial. Journal of Medical Internet Research, 20(2), Article 2. https://doi.org/10.2196/jmir.8640
  8. Chase, J. G., Preiser, J.-C., Dickson, J. L., Pironet, A., Chiew, Y. S., Pretty, C. G., Shaw, G. M., Benyo, B., Moeller, K., Safaei, S., Tawhai, M., Hunter, P., & Desaive, T. (2018). Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. BioMedical Engineering OnLine, 17(1), Article 1. https://doi.org/10.1186/s12938-018-0455-y
  9. Chen, Z., Deng, Z., Dhupia, J. S., Stommel, M., & Xu, P. (2018). Modelling of a Soft Actuator for a Planar Manipulator Table. 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 1--6. https://doi.org/10.1109/m2vip.2018.8600916
  10. de Bono, B., Safaei, S., Grenon, P., & Hunter, P. J. (2018). Meeting the multiscale challenge: representing physiology processes over ApiNATOMY circuits using bond graphs. Interface Focus, 8(1), Article 1. https://doi.org/10.1098/rsfs.2017.0026
  11. Driess, D., Zimmermann, H., Wolfen, S., Suissa, D., Haeufle, D., Hennes, D., Toussaint, M., & Schmitt, S. (2018). Learning to Control Redundant Musculoskeletal Systems with Neural Networks and SQP: Exploiting Muscle Properties. 2018 IEEE International Conference on Robotics and Automation (ICRA), 6461–6468. https://doi.org/10.1109/icra.2018.8463160
  12. Erdemir, A., Hunter, P. J., Holzapfel, G. A., Loew, L. M., Middleton, J., Jacobs, C. R., Nithiarasu, P., Löhner, R., Wei, G., Winkelstein, B. A., Barocas, V. H., Guilak, F., Ku, J. P., Hicks, J. L., Delp, S. L., Sacks, M. S., Weiss, J. A., Ateshian, G. A., Maas, S. A., … Peng, G. C. Y. (2018). Perspectives on Sharing Models and Related Resources in Computational Biomechanics Research. Journal of Biomechanical Engineering, 140(2), Article 2. https://doi.org/10.1115/1.4038768
  13. Erickson, J. C., Hayes, J. A., Bustamante, M., Joshi, R., Rwagaju, A., Paskaranandavadivel, N., & Angeli, T. R. (2018). Intsy: a low-cost, open-source, wireless multi-channel bioamplifier system. Physiol Meas, 39(3), Article 3. https://doi.org/10.1088/1361-6579/aaad51
  14. Fernandez, J., Mithraratne, K., Alipour, M., Handsfield, G., Besier, T., & Zhang, J. (2018). Towards rapid prediction of personalised muscle mechanics: integration with diffusion tensor imaging. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1--9. https://doi.org/10.1080/21681163.2018.1519850
  15. Haasdonk, B., & Santin, G. (2018). Greedy Kernel Approximation for Sparse Surrogate Modeling. Reduced-Order Modeling (ROM) for Simulation and Optimization, 21--45. https://doi.org/10.1007/978-3-319-75319-5_2
  16. Hertneck, M., Köhler, J., Trimpe, S., & Allgöwer, F. (2018). Learning an approximate model predictive controller with guarantees. IEEE Control Systems Lett., 2(3), Article 3. https://doi.org/10.1109/LCSYS.2018.2843682
  17. Hinze, C., Tasci, T., Lechler, A., & Verl, A. (2018). Towards Real-Time Capable Simulations with a Containerized Simulation Environment. 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 1–6. https://doi.org/10.1109/M2VIP.2018.8600827
  18. Jaensch, F., Csiszar, A., Kienzlen, A., & Verl, A. (2018). Reinforcement Learning of Material Flow Control Logic Using Hardware-in-the-Loop Simulation. 2018 First International Conference on Artificial Intelligence for Industries (AI4I), 77–80. https://doi.org/10.1109/AI4I.2018.8665712
  19. Jaensch, F., Csiszar, A., Scheifele, C., & Verl, A. (2018). Digital Twins of Manufacturing Systems as a Base for Machine Learning. 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 1–6. https://doi.org/10.1109/M2VIP.2018.8600844
  20. Ji, X., El Haitami, A., Sorba, F., Rosset, S., Nguyen, G. T. M., Plesse, C., Vidal, F., Shea, H. R., & Cantin, S. (2018). Stretchable composite monolayer electrodes for low voltage dielectric elastomer actuators. Sensors and Actuators, B: Chemical, 261, 135–143.
  21. Kaiser, B., Csiszar, A., & Verl, A. (2018). Generative models for direct generation of CNC toolpaths. In 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (pp. 1–6). https://doi.org/10.1109/m2vip.2018.8600856
  22. Kim, S., Billinghurst, M., & Lee, G. (2018). The Effect of Collaboration Styles and View Independence on Video-Mediated Remote Collaboration. Computer Supported Cooperative Work (CSCW), 27(3–6), Article 3–6. https://doi.org/10.1007/s10606-018-9324-2
  23. Klein, M., Hoher, S., Kimpeler, S., Lehner, M., Jaensch, F., Kühfuß, F., Lehmann, H., & Snelting, F. (2018). Machines Without Humans -- Post-Robotics. Proceedings of Robophilosophy 2018 / TRANSOR 2018, 88--92. https://doi.org/10.3233/978-1-61499-931-7-88
  24. Klein, M., Hoher, S., Kimpeler, S., Lehner, M., Jaensch, F., Kühfuß, F., Lehmann, H., & Snelting, F. (2018). Machines Without Humans—Post-Robotics. In M. Coeckelbergh, J. Loh, & M. Funk (Eds.), Proceedings of Robophilosophy 2018 / TRANSOR 2018 (pp. 88–92). https://doi.org/10.3233/978-1-61499-931-7-88
  25. Köhler, J., Müller, M. A., & Allgöwer, F. (2018). MPC for nonlinear periodic tracking using reference generic offine computations. Proc. IFAC Conf. Nonlinear Model Predictive Control (NMPC), 656–661.
  26. Köhler, J., Müller, M. A., & Allgöwer, F. (2018). Nonlinear Reference Tracking with Model Predictive Control: An Intuitive Approach. Proc. European Control Conf. (ECC), 1355–1360.
  27. Köhler, J., Müller, M. A., & Allgöwer, F. (2018). Nonlinear reference tracking: An economic model predictive control perspective. IEEE Trans. Automat. Control, 64, 254–269.
  28. Köhler, J., Müller, M. A., & Allgöwer, F. (2018). A novel constraint tightening approach for nonlinear robust model predictive control. Proc. American Control Conf. (ACC), 728–734.
  29. Köhler, J., Müller, M. A., & Allgöwer, F. (2018). A novel constraint tightening approach for nonlinear robust model predictive control. Proc. American Control Conf.\ (ACC), 728--734. https://doi.org/10.23919/acc.2018.8431892
  30. Köhler, J., Müller, M. A., & Allgöwer, F. (2018). On periodic dissipativity notions in economic model predictive control. IEEE Control Systems Letters, 2(3), Article 3.
  31. Köhler, J., Müller, M. A., & Allgöwer, F. (2018). MPC for nonlinear periodic tracking using reference generic offline computations. IFAC-PapersOnLine, 51(20), Article 20. https://doi.org/10.1016/j.ifacol.2018.11.032
  32. Liarokapis, M., & Dollar, A. M. (2018). Combining Analytical Modeling and Learning to Simplify Dexterous Manipulation With Adaptive Robot Hands. IEEE Transactions on Automation Science and Engineering. https://doi.org/10.1109/tase.2018.2885801
  33. Mahmoudinezhad, M. H., Karkhaneh, A., & Jadidi, K. (2018). Effect of PEDOT: PSS in tissue engineering composite scaffold on improvement and maintenance of endothelial cell function. Journal of Biosciences, 43(2), Article 2. https://doi.org/10.1007/s12038-018-9748-3
  34. Mayne, T. P., Paskaranandavadivel, N., Erickson, J. C., G, O. G., Cheng, L. K., & Angeli, T. R. (2018). Improved Visualization of Gastrointestinal Slow Wave Propagation Using a Novel Wavefront-Orientation Interpolation Technique. IEEE Trans Biomed Eng, 65(2), Article 2. https://doi.org/10.1109/TBME.2017.2764945
  35. Mordhorst, M., Haasdonk, B., & Röhrle, O. (2018). Towards a stable and fast dynamic skeletal muscle model. ScienceOpen Posters. https://doi.org/10.14293/p2199-8442.1.sop-math.mtlybn.v1
  36. Mostashiri, N., Dhupia, J. S., Verl, A. W., & Xu, P. (2018). A Novel Spatial Mandibular Motion-Capture System Based on Planar Fiducial Markers. IEEE Sensors Journal, 18(24), Article 24. https://doi.org/10.1109/jsen.2018.2873349
  37. Mostashiri, N., Dhupia, J. S., Verl, A. W., & Xu, P. (2018). A review of research aspects of redundantly actuated parallel robotsw for enabling further applications. IEEE/ASME Transactions on Mechatronics, 23(3), Article 3. https://doi.org/10.1109/tmech.2018.2792450
  38. Mostashiri, N., Sesiashvili, E., Verl, A., Dhupia, J., Ratnaweera, P. M., & Xu, P. (2018). A Low-cost simple method for capturing the mandibular Motion. In Proceedings of the International Symposium on Flexible Automation (pp. 199--204). The Institute of Systems, Control and Information Engineers. https://www.jstage.jst.go.jp/article/isfa/2018/0/2018_199/_pdf
  39. Norte, G. E., Knaus, K. R., Kuenze, C., Handsfield, G., Meyer, C. H., Blemker, S. S., & Hart, J. M. (2018). MRI-based assessment of lower-extremity muscle volumes in patients before and after ACL reconstruction. Journal of Sport Rehabilitation, 27(3), Article 3. https://doi.org/10.1123/jsr.2016-0141
  40. Röhrle, O., Saini, H., Lee, P. V. S., & Ackland, D. C. (2018). A Novel Computational Method to Determine Subject-Specific Bite Force and Occlusal Loading during Mastication. Computer Methods in Biomechanics and Biomedical Engineering, 21(6), Article 6. https://doi.org/10.1080/10255842.2018.1479744
  41. Safaei, S., Blanco, P. J., Müller, L. O., Hellevik, L. R., & Hunter, P. J. (2018). Bond Graph Model of Cerebral Circulation: Toward Clinically Feasible Systemic Blood Flow Simulations. Frontiers in Physiology, 9. https://doi.org/10.3389/fphys.2018.00148
  42. Saini, H., Altan, E., Ramasamy, E., Klotz, T., Gizzi, L., & Röhrle, O. (2018). Predicting Skeletal Muscle Force from Motor-Unit Activity Using a 3D FE Model. PAMM, 18(1), Article 1. https://doi.org/10.1002/pamm.201800035
  43. Schmidt, A., & Haasdonk, B. (2018). Data-driven surrogates of value functions and applications to feedback control for dynamical systems. IFAC-PapersOnLine, 51(2), Article 2. https://doi.org/10.1016/j.ifacol.2018.03.053
  44. Schmidt, A., & Haasdonk, B. (2018). Reduced basis approximation of large scale parametric algebraic Riccati-equations. ESAIM: Control, Optimisation and Calculus of Variations, 24(1), Article 1. https://doi.org/10.1051/cocv/2017011
  45. Shim, V. B., Handsfield, G. G., Fernandez, J. W., Lloyd, D. G., & Besier, T. F. (2018). Combining in silico and in vitro experiments to characterize the role of fascicle twist in the Achilles tendon. Scientific Reports, 8(1), Article 1. https://doi.org/10.1038/s41598-018-31587-z
  46. Soloperto, R., Müller, M. A., Trimpe, S., & Allgöwer, F. (2018). Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty. IFAC-PapersOnLine, 51(20), Article 20. https://doi.org/10.1016/j.ifacol.2018.11.052
  47. Taberner, A. J., Zgierski-Johnston, C. M., Pham, T., Han, J.-C., Uddin, R., Loiselle, D. S., Ruddy, B. P., & Nielsen, P. M. F. (2018). A Flowthrough Infusion Calorimeter for Measuring Muscle Energetics: Design and Performance. IEEE Transactions on Instrumentation and Measurement, 1--10. https://doi.org/10.1109/tim.2018.2800838
  48. Tomalka, A. (2018). Determination of biomechanical and architectural muscle properties: from single muscle fibre to whole muscle mechanics.
  49. Tomzik, D. A., & Xu, X. W. (2018). Architecture of a Cloud-Based Control System Decentralised at Field Level. In 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) (pp. 353--358). IEEE. https://doi.org/10.1109/coase.2018.8560418
  50. Wang, R., Abukhalaf, Z., Javan-Khoshkholgh, A., Wang, T. H., Sathar, S., Du, P., Angeli, T. R., Cheng, L. K., O’Grady, G., Paskaranandavadivel, N., & Farajidavar, A. (2018). A Miniature Configurable Wireless System for Recording Gastric Electrophysiological Activity and Delivering High-Energy Electrical Stimulation. IEEE J Emerg Sel Top Circuits Syst, 8(2), Article 2. https://doi.org/10.1109/JETCAS.2018.2812105
  51. Wang, T. H., Du, P., Angeli, T. R., Paskaranandavadivel, N., Erickson, J. C., Abell, T. L., Cheng, L. K., & O’Grady, G. (2018). Relationships between gastric slow wave frequency, velocity, and extracellular amplitude studied by a joint experimental-theoretical approach. Neurogastroenterol Motil, 30(1), Article 1. https://doi.org/10.1111/nmo.13152
  52. Wirzberger, M., Herms, R., Esmaeili Bijarsari, S., Eibl, M., & Rey, G. D. (2018). Schema-related cognitive load influences performance, speech, and physiology in a dual-task setting: A continuous multi-measure approach. Cognitive Research: Principles and Implications, 3, 48. https://doi.org/10.1186/s41235-018-0138-z
  53. Wnuk, M., Lechler, A., & Verl, A. (2018). Der Umgang mit Weichheit. Handling, 10, Article 10.
  54. Wnuk, M., Lechler, A., & Verl, A. (2018). Interdisziplinäres Forschungsprojekt ``Soft Tissue Robotics’’: Der Umgang mit Weichheit. Handling - Automation, Handhabungstechnik Und Intralogistik, 10, 34–35.
  55. Wnuk, M., Wenger, T., Lechler, A., & Verl, A. (2018). Nachgiebigkeit ist Einstellungssache. Handling - Automation, Handhabungstechnik Und Intralogistik, 9, 20.
  56. Wolfen, S., Walter, J., Günther, M., Haeufle, D. F. B., & Schmitt, S. (2018). Bioinspired pneumatic muscle spring units mimicking                  the human motion apparatus: benefits for passive                  motion range and joint stiffness variation in                  antagonistic setups. 25th International Conference on Mechatronics and                  Machine Vision in Practice (M2VIP), (6pp). https://doi.org/10.1109/M2VIP.2018.8600913

2017

  1. Terfurth, J., & Parspour, N. (2019). Integrated Planetary Gear Joint Actuator Concept for Wearable and Industrial Robotic Applications. 2019 Wearable Robotics Association Conference (WearRAcon), 28--33. https://doi.org/10.1109/WEARRACON.2019.8719400
  2. Alighaleh, S., Angeli, T. R., Sathar, S., O’Grady, G., Cheng, L. K., & Paskaranandavadivel, N. (2017). Design and application of a novel gastric pacemaker. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2181--2184. https://doi.org/10.1109/embc.2017.8037287
  3. Aslam, N., Pfender, M., Neumann, P., Reuter, R., Zappe, A., de Oliveira, F. F., Denisenko, A., Sumiya, H., Onoda, S., Isoya, J., & others. (2017). Nanoscale nuclear magnetic resonance with chemical resolution. Science, 357(6346), Article 6346. https://doi.org/10.1126/science.aam8697
  4. Besse, N., Rosset, S., Zarate, J. J., & Shea, H. (2017). Flexible Active Skin: Large Reconfigurable Arrays of Individually Addressed Shape Memory Polymer Actuators. Advanced Materials Technologies, 2(10), Article 10.
  5. Boanta, C., & Csiszar, A. (2017). Optimal design of a parallel structure used as a haptic interface. Mechanism and Machine Theory, 116, 69--88. https://doi.org/10.1016/j.mechmachtheory.2017.05.013
  6. Csiszar, A., Eilers, J., & Verl, A. (2017). On solving the inverse kinematics problem using neural networks. In 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (pp. 1--6). IEEE. https://doi.org/10.1109/m2vip.2017.8211457
  7. Csiszar, A., Jaensch, F., Kienzlen, A., & Scheifele, C. (2017). Machine Learning in Steuerungstechnik und Robotik. SPS MAGAZIN, 30(12 2017), Article 12 2017.
  8. Datta, C., & MacDonald, B. A. (2017, April). Architecture of an Extensible Visual Programming Environment for Authoring Behaviour of Personal Service Robots. 2017 First IEEE International Conference on Robotic Computing (IRC). https://doi.org/10.1109/irc.2017.60
  9. Diprose, J., MacDonald, B., Hosking, J., & Plimmer, B. (2017). Designing an API at an appropriate abstraction level for programming social robot applications. Journal of Visual Languages & Computing, 39, 22--40. https://doi.org/10.1016/j.jvlc.2016.07.005
  10. Du, P., Calder, S., Angeli, T. R., Sathar, S., Paskaranandavadivel, N., O’Grady, G., & Cheng, L. K. (2017). Progress in Mathematical Modeling of Gastrointestinal Slow Wave Abnormalities. Front Physiol, 8, 1136. https://doi.org/10.3389/fphys.2017.01136
  11. Fernandez, J., Mithraratne, K., Alipour, M., Handsfield, G., Besier, T., & Zhang, J. (2017). Rapid prediction of personalised muscle mechanics: integration with diffusion tensor imaging. Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound, 71--77. https://doi.org/10.1007/978-3-319-67552-7_9
  12. Friedrich, C., Csiszar, A., Lechler, A., & Verl, A. (2017). Efficient task and path planning for maintenance automation using a robot system. IEEE Transactions on Automation Science and Engineering, 15(3), Article 3. https://doi.org/10.1109/tase.2017.2759814
  13. Garrett, A. S., Pham, T., Loiselle, D. S., Han, J.-C., & Taberner, A. J. (2017). Real-time model-based control of afterload for in vitro cardiac tissue experimentation. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1287--1290. https://doi.org/10.1109/embc.2017.8037067
  14. Gholami, A., Mang, A., Scheufele, K., Davatzikos, C., Mehl, M., & Biros, G. (2017). A Framework for Scalable Biophysics-based Image Analysis. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC17, 1--13. https://doi.org/10.1145/3126908.3126930
  15. HajiRassouliha, A., Taberner, A. J., Nash, M. P., & Nielsen, P. M. F. (2017). Motion correction using subpixel image registration. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10129 LNCS, 14–23.
  16. Handsfield, G., Bolsterlee, B., Inouye, J. M., Herbert, R. D., Besier, T. F., & Fernandez, J. W. (2017). Determining skeletal muscle architecture with Laplacian simulations: a comparison with diffusion tensor imaging. Biomechanics and Modeling in Mechanobiology, 16(6), Article 6. https://doi.org/10.1007/s10237-017-0923-5
  17. Handsfield, G., Inouye, J. M., Slane, L. C., Thelen, D. G., Miller, G. W., & Blemker, S. S. (2017). A 3D model of the Achilles tendon to determine the mechanisms underlying nonuniform tendon displacements. Journal of Biomechanics, 51, 17--25. https://doi.org/10.1016/j.jbiomech.2016.11.062
  18. Handsfield, G., Knaus, K. R., Blemker, S. S., Meyer, C. H., & Hart, J. M. (2017). Systems and methods for identifying and profiling muscle patterns.
  19. Handsfield, G., Knaus, K., Fiorentino, N., Meyer, C., Hart, J., & Blemker, S. (2017). Adding muscle where you need it: non-uniform hypertrophy patterns in elite sprinters. Scandinavian Journal of Medicine & Science in Sports, 27(10), Article 10. https://doi.org/10.1111/sms.12723
  20. Henry, C. C., Martin, K. S., Ward, B. B., Handsfield, G., Peirce, S. M., & Blemker, S. S. (2017). Spatial and age-related changes in the microstructure of dystrophic and healthy diaphragms. Plos One, 12(9), Article 9. https://doi.org/10.1371/journal.pone.0183853
  21. Hinze, C., Xu, P., Lechler, A., & Verl, A. (2017). A cloud-based control architecture design for the interaction of industrial robots with soft objects. In 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (pp. 1--6). IEEE. https://doi.org/10.1109/m2vip.2017.8211438
  22. Hinze, C., Xu, W., Lechler, A., & Verl, A. (2017). A cloud-based control architecture design for the interaction of industrial robots with soft objects. 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 1–6. https://doi.org/10.1109/M2VIP.2017.8211438
  23. Hosseinnejad, S. H., Besier, T. F., Taberner, A. J., & Ruddy, B. P. (2017, September). Design optimization of a direct-drive linear actuator assistive device for stroke. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros.2017.8206541
  24. Jaensch, F., Neyrinck, A., Lechler, A., & Verl, A. (2017). Variantenreiche Fabrikplanung: Automatische Variantenerzeugung für den simulationsbasierten Vergleich von Anlagenprojektierungen. Wt Werkstattstechnik Online, 107(9), Article 9.
  25. Karimi Soflou, R., Mahmoudinezhad, M. H., & Karkhaneh, A. (2017). Controlled release of dexamethasone loaded in PCL-mmt fibers for bone tissue engineering application. Second International Nanomedicine and Nanosafety Conference.
  26. Lee, G. A., Teo, T., Kim, S., & Billinghurst, M. (2017). Mixed reality collaboration through sharing a live panorama. SIGGRAPH Asia 2017 Mobile Graphics & Interactive Applications, 1--4. https://doi.org/10.1145/3132787.3139203
  27. Liang, A., Piroth, I., Robinson, H., MacDonald, B., Fisher, M., Nater, U. M., Skoluda, N., & Broadbent, E. (2017). A pilot randomized trial of a companion robot for people with dementia living in the community. Journal of the American Medical Directors Association, 18(10), Article 10. https://doi.org/10.1016/j.jamda.2017.05.019
  28. Liarokapis, M., & Dollar, A. M. (2017). Learning the post-contact reconfiguration of the hand object system for adaptive grasping mechanisms. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 293--299. https://doi.org/10.1109/iros.2017.8202171
  29. Mordhorst, M., Strecker, T., Wirtz, D., Heidlauf, T., & Röhrle, O. (2017). POD-DEIM reduction of computational EMG models. Journal of Computational Science, 19, 86--96. https://doi.org/10.1016/j.jocs.2017.01.009
  30. Mostashiri, N., Akbarzadeh, A., Dhupia, J., Verl, A., & Xu, P. (2017). A comprehensive inverse dynamics problem of a Stewart platform by means of Lagrangian formulation. Dynamic Systems and Control Conference, 58271, V001T30A003. https://doi.org/10.1115/dscc2017-5098
  31. Mostashiri, N., Akbarzadeh, A., & Rezaei, A. (2017). Implementing the homotopy continuation method in a hybrid approach to solve the kinematics problem of spatial parallel robots. Intelligent Service Robotics, 10(3), Article 3. https://doi.org/10.1007/s11370-017-0222-0
  32. Mostashiri, N., Dhupia, J., Verl, A., & Xu, P. (2017). Roadmap for in-vitro investigation of interaction between food and teeth. 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 1--6. https://doi.org/10.1109/m2vip.2017.8211443
  33. Paskaranandavadivel, N., Cheng, L. K., Du, P., Rogers, J. M., & O’Grady, G. (2017). High-resolution mapping of gastric slow-wave recovery profiles: biophysical model, methodology, and demonstration of applications. Am J Physiol Gastrointest Liver Physiol, 313(3), Article 3. https://doi.org/10.1152/ajpgi.00127.2017
  34. Rosset, S., de Saint-Aubin, C., Poulin, A., & Shea, H. R. (2017). Assessing the degradation of compliant electrodes for soft actuators. Review of Scientific Instruments, 88(10), Article 10. https://doi.org/10.1063/1.4989464
  35. Sartori, M., Fernandez, J., Modenese, L., Carty, C., Barber, L., Oberhofer, K., Zhang, J., Handsfield, G., Stott, N., Besier, T., & others. (2017). Toward modeling locomotion using electromyography-informed 3D models: application to cerebral palsy. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 9(2), Article 2. https://doi.org/10.1002/wsbm.1368
  36. Schlechtendahl, J., Kretschmer, F., Sang, Z., Lechler, A., & Xu, X. (2017). Extended study of network capability for cloud based control systems. Robotics and Computer-Integrated Manufacturing, 43, 89--95. https://doi.org/10.1016/j.rcim.2015.10.012
  37. Tempel, P., Eger, F., & Verl, A. (2017). Fertigung von Schaltschränken im Wandel moderner Produktionsprozesse: Reichlich Potential zur Effizienzsteigerung. Schaltschrankbau, 2, Article 2.
  38. Tomzik, D. A., & Xu, X. W. (2017). Requirements for a cloud-based control system interacting with soft bodies. 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 1--5. https://doi.org/10.1109/m2vip.2017.8211512
  39. Wang, V. Y., Hussan, J. R., Yousefi, H., Bradley, C. P., Hunter, P. J., & Nash, M. P. (2017). Modelling Cardiac Tissue Growth and Remodelling. In Multiscale Soft Tissue Mechanics and Mechanobiology (pp. 283--305). Springer Netherlands. https://doi.org/10.1007/978-94-024-1220-8_14
  40. Wnuk, M., Pott, A., Xu, P., Lechler, A., & Verl, A. (2017). Concept for a simulation-based approach towards automated handling of deformable objects — A bin picking scenario. 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 1–6. https://doi.org/10.1109/M2VIP.2017.8211452
  41. Wnuk, M., Pott, A., Xu, W., Lechler, A., & Verl, A. (2017). Concept for a simulation-based approach towards automated handling of deformable objects - A bin picking scenario. 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 1–6. https://doi.org/10.1109/M2VIP.2017.8211452
  42. Zeng, S., Ishii, H., & Allgöwer, F. (2017). Sampled Observability and State Estimation of Linear Discrete Ensembles. IEEE Transactions on Automatic Control, 62(5), Article 5. https://doi.org/10.1109/tac.2016.2613478
To the top of the page