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Professors Prof. dr. ir. J. (Hans) Hellendoorn Head of department Director of Education & Graduate School Autonomous Robots & software-based control architectures Knowledge Representation, Model-based Systems Engineering, Self-adaptive Systems Prof. dr. ir. D.A. (David) Abbink Human-Robot Interaction Human-Robot Interaction, Shared Control, Haptic Interaction, Driver Modeling, Interface Design, Tele-robotics, Meaningful Human Control Prof. dr. R. (Robert) Babuska Group leader Learning and Autonomous Control Reinforcement learning, nonlinear control, data-driven model construction, deep learning Prof. dr. D.M. (Dariu) Gavrila Group leader Intelligent Vehicles Intelligent Vehicles, Machine Perception Prof. Dr. ir. R. (Riender) Happee Intelligent Vehicles Human factors and comfort of automated driving Prof. Dr. ir. J.C.F. (Joost) de Winter Human-Robot Interaction Human Factors, simulation, driver assessment, statistics, automated driving Prof. dr. ir. M. (Martijn) Wisse Group leader Robot Dynamics Biorobotics Associate Professors / Senior Lecturers Dr. J. (Javier) Alonso-Mora Learning and Autonomous Control Motion Planning, Multi-robot Systems, Robot Autonomy , Intelligent Transportation, Learning for Coordination and Planning Dr. C. (Cosimo) Della Santina Learning and Autonomous Control Soft Robotics, Robot Control, Learning for Control, Robotic Hands, Nonlinear Dynamics, Human Motor Control Dr. ir. C. (Carlos) Hernández Corbato Robot Dynamics Autonomous Robots, Software-based Control Architectures, Knowledge Representation and Reasoning, Self-Adaptive Systems, Model-Based Systems Engineering Dr. ing. J. (Jens) Kober Learning and Autonomous Control Motor Skill Learning, Reinforcement Learning, Imitation Learning, Deep Learning, Interactive Learning Dr. J.F.P. (Julian) Kooij Intelligent Vehicles Multi-sensor Perception, Machine Learning, Self-driving vehicles, Situation analysis and Prediction, Probabilistic inference, Representation learning, 3D Urban Understanding lab Dr. ing. L. (Laura) Marchal Crespo Group leader Human-Robot Interaction Rehabilitation robotics, motor learning, neurorehabilitation, sensorimotor training, robot control, virtual/augmented reality Dr. ir. G.N. (Gillian) Saunders Robot Dynamics Senior Lecturer Engineering Education, Competencies & Skills, Assessment & Curriculum Development Dr. B. (Barys) Shyrokau Intelligent Vehicles Dynamics, Vehicle Control, ADAS/AD Dr. M. (Michaël) Wiertlewski Human-Robot Interaction Human-Machine Interaction, Friction modulation, Haptics Assistant Professors Dr. H. (Holger) Caesar Intelligent Vehicles Deep learning, Autonomous Vehicles, Perception, Prediction, ML Planning, Active learning Dr. Ir. Y.B. (Yke Bauke) Eisma Human-Robot Interaction Eye-tracking and visual attention modelling, Gaze-contingent paradigms, Attention-based robot control Psychometrics, Expertise measurement Dr. L. (Laura) Ferranti Learning and Autonomous Control Cooperative Control, Real-time Optimization, Fault-tolerant Control Mobile Robots, Privacy-preserving Motion Planning Dr. G. (Georgios) Papaioannou Intelligent Vehicles Motion comfort, human perception, postural stability, human body modelling, seat design, vehicle control Dr. C. (Chris) Pek Robot Dynamics Robot safety, task and motion planning, formal methods, machine learning, human-robot interaction Dr. L. (Luka) Peternel Human-Robot Interaction Physical Human-Robot Interaction, Teleoperation, Robotic Manipulator Control, Robot Learning, Human Motor Control Dr. J.M. (Micah) Prendergast Human-Robot Interaction Medical Robotics and Rehabilitation, Human-Robot Interaction, Robot Perception and Control Dr. Y. (Yasemin) Vardar Human-Robot Interaction haptic interface technology, understanding tactile contact, human-machine interaction Dr. A. (Arkady) Zgonnikov Human-robot Interaction Human cognition, Cognitive modeling, Driver behavior, Traffic interactions, Meaningful human control Support J. (Jolanda) Dijkshoorn Department Manager E.M. (Ellen) Driessen Management - / Office Assistant N. (Noortje) Fousert Management - / Office Assistant & Event management PA Head of department H.M. (Hanneke) Hustinx Management - / Office Assistant (Coordinator) K. (Kseniia) Khomenko Assistent lecturer/Junior researcher assistant L.A.M. (Loulou) Zorgui-Martens Management - / Office Assistant K.J. (Karin) van Tongeren Assistant MSc Coördinator Robotics/Vehicle Engineering/ BioRobotics/Haptic Interfaces Technical Support Ir. R.M. (Ronald) Ensing Intelligent Vehicles Dr. M.A. (Mario) Garzon Intelligent Vehicles Ir. G.A. (Gijs) van der Hoorn Robot Dynamics A.A.M. (André) van der Kraan Cognitive Robotics M. (Maurits) Pfaff Cognitive Robotics Teachers Ir. C. (Cilia) Claij Cognitive Robotics Junior Docent Ir. T. (Thijs) Hoedemakers Cognitive Robotics Junior Docent M. (Martin) Klomp MSc Robot Dynamics Researchers and PostDocs A. (Alberto) Bertipaglia Intelligent Vehicles Dr. G. (Gang) Chen Learning and Autonomous Control C.S. (Charlotte) Croucher Intelligent Vehicles Dr. J. (Jiatao) Ding Learning and Autonomous Control Dr. D.F. (Deborah) Forster Human-Robot Interaction A. (Alex) Gabriel Robot Dynamics A. (Ahmad) Gazar) Learning and Autonomous Control Ir. O.M. (Oscar) de Groot Intelligent Vehicles A. (Arend-Jan) van Hilten Robot Dynamics Education Robot Developer A. (Alessandro) Ianniello Human-Robot Interaction G. (Guopeng) Li Intelligent Vehicles Y. (Yancong) Lin Intelligent Vehicles X. (Xiaoyu) Liu Intelligent Vehicles F. (Federico) Scarí Human-Robot Interaction Dr. E. (Ebrahim) ShahabiShalghouni Learning and Autonomous Control Ir. O. (Olger) Siebinga Human-Robot Interaction Dr.ir. J. (Joris) Sijs Learning and Autonomous Control State estimation, navigation, automated planning, knowledge representation and reasoning Dr. B. (Burak) Şişman Robot Dynamics Ir. M. (Max) Spahn Learning and Autonomous Control S. (Sihao) Sun Learning and Autonomous Control W. (Wilbert) Tabone Human-Robot Interaction F. (Farzam) Tajdari Intelligent Vehicles D. (Daniel) Feliu Talegón Learning and Autonomous Control Ir. E. (Elia) Trevisan Learning and Autonomous Control C. (Cong) Wang Learning and Autonomous Control N. (Nils) Wilde Learning and Autonomous Control L. (Laurence) Willemet Human-Robot Interaction L. (Li) Zou Human-Robot Interaction PhD Students S. (Saray) Bakker Learning and Autonomous Control I. (Italo) Belli Human-Robot Interaction Ir. D. (Dennis) Benders Learning and Autonomous Control Ir. A. (Alex) van den Berg Human-Robot Interaction H.J. (Hidde) Boekema Intelligent Vehicles Z. (Zhaochong) Cai Human-Robot Interaction T. (Tomás) Coleman Learning and Autonomous Control S.L. (Salvo) Cucinella Human-Robot Interaction M.C. (Moses) Ebere Robot Dynamics A. (Alberto) Garzás Villar Human-Robot Interaction Ir. A. (Ashwin) George Human-Robot Interaction R. (Roel) Horeman Human-Robot Interaction Y. (Yuxuan) Hu Human-Robot Interaction E. (Ekaterina) Karmanova Human-Robot Interaction C.U. (Celal) Kenanoğlu Human-Robot Interaction Ir. V. (Varun) Kotian Intelligent Vehicles L. (Luzia) Knödler Learning and Autonomous Control J. (Jagan) Krishnasamy Balasubramanian Human-Robot Interaction L. (Zhaoting) Li Learning and Autonomous Control Ir. J. (Jingyue) Liu Learning and Autonomous Control Ir. J.D. (Jelle) Luijkx Learning and Autonomous Control Ir. L. (Lorenzo) Lyons Learning and Autonomous Control A. (Andreu) Matoses Gimenez Learning and Autonomous Control C. (Chrysovalante) Messiou Intelligent Vehicles Ir. N. (Nicky) Mol Human-Robot Interaction A. (Anna) Mészáros Learning and Autonomous Control K. (Khaled) Mustafa Learning and Autonomous Control L. (Lasse) Peters Learning and Autonomous Control M. (Mariano) Ramírez Montero Learning and Autonomous Control A.R. (Alex) Ratschat Human-Robot Interaction G. (Gustavo) Rezende Silva Robot Dynamics Ir. J.F. (Julian) Schumann Human-Robot Interaction M.W. (Maximilian) Stölzle Learning and Autonomous Control L. (Lukas) Stracovsky Human-Robot Interaction Y. (Yujie) Tang Robot Dynamics T. (Tasos) Tsolakis Learning and Autonomous Control G. (Giuseppe) Vitrani Human-Robot Interaction T. (Ted) de Vries Lentsch Intelligent Vehicles S. (Shiming) Wang Intelligent Vehicles M. (Mubariz) Zaffar Intelligent Vehicles F. (Forough) Zamani Robot Dynamics C. (Chuhan) Zhang Learning and Autonomous Control R. (Renchi) Zhang Human-Robot Interaction

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How storm surge barriers can keep the Netherlands safe and liveable

A safe and liveable delta, who doesn't go for that? Storm surge barriers play a crucial role in this. Yet there are many choices to be made in the short term to keep the storm surge barriers in a good condition, to eventually cope with rising sea levels in the longer term. A new project receives funding from NWO for five years to explore the best routes to a liveable delta. Storm surge barriers, like the Maeslantkering and the Oosterscheldekering are essential for protecting the Netherlands from high water coming in from the sea. How long will these imposing structures remain effective bearing in mind sea level rise, decay of the structures and an altering surrounding area. In the short term, decisions will have to be taken on maintenance, while in the longer term, adaptation or replacement should be considered. Linking storm surge barriers with the delta Within the SSB-Δ (storm surge barrier delta) project, a diverse consortium will investigate under what circumstances storm surge barriers can keep the Netherlands safe and liveable. The consortium consists of the universities of Delft, Utrecht, and Rotterdam; the universities of applied sciences of Rotterdam and Zeeland; knowledge institutes Deltares and TNO, as well as Rijkswaterstaat, water boards and companies. Bram van Prooijen, associate professor at TU Delft, will lead the research: “Decisions on flood defences are important for the entire delta. The link between the hinterland and the flood defences needs to be made properly. During this project, we will have the opportunity to bring different areas of expertise together and strengthen each other.” Long term perspective Therefore, the research is not only about the technical lifespan of the barriers. It will also clarify how the delta is going to change and how society thinks about it, resulting in a guideline to on how and when decisions need to be taken in the short term, with a long term perspective. Van Prooijen cites an example of car maintenance: “Think of replacing the engine block. This is very expensive maintenance, but sometimes necessary to keep the car running safely. But is it worth the investment if you plan to buy a new car next year? Or if you prefer to travel by train? Important choices will have to be made for storm surge barriers. We want to provide a strong basis for that.” Informed decisions The project will reveal the possible pathways to a liveable delta, and how storm surge barriers fit into that. Van Prooijen: “That offers clarity, to make quick and better-informed decisions. Many trials run for a long time, with the outcome of this research we can decide which trials specifically are the best option to proceed with.” Future experts One of the storm surge barriers involved in the research is the Maeslantkering. This barrier is expected to last another fifty years or so. That may seem far away, Van Prooijen reasons, “but we need to train the experts who will decide on this now. Those are probably the PhD students on this project.”

Researchers hand over Position Paper to Tweede Kamer

On behalf of the TU Delft PowerWeb Institute, researchers Kenneth Brunninx and Simon Tindemans are handing over a Position Paper to the Dutch Parliament on 14 November 2024, with a possible solution to the major grid capacity problems that are increasingly cropping up in the Netherlands. The Netherlands is unlikely to meet the 2030 climate targets, and one of the reasons for this is that large industry cannot switch to electricity fast enough, partly because of increasingly frequent problems around grid capacity and grid congestion. In all likelihood, those problems will actually increase this decade before they can decrease, the researchers argue. The solution offered by the TU Delft PowerWeb Institute researchers is the ‘flexible backstop’. With a flexible backstop, the current capacity of the power grid can be used more efficiently without sacrificing safety or reliability. A flexible backstop is a safety mechanism that automatically and quickly reduces the amount of electricity that an electric unit can draw from the grid (an electric charging station or a heat pump) or deliver (a PV installation). It is a small device connected or built into an electrical unit, such as a charging station or heat pump, that ‘communicates’ with the distribution network operator. In case of extreme stress on the network, the network operator sends a signal to the device to limit the amount of power. Germany recently introduced a similar system with electric charging stations. The backstop would be activated only in periods of acute congestion problems and could help prevent the last resort measure, which is cutting off electricity to users. ‘Upgrading the electricity network remains essential, but in practice it will take years. So there is a need for short-term solutions that can be integrated into long-term planning. We, the members of the TU Delft PowerWeb Institute, call on the government, network operators and regulator to explore the flexible backstop as an additional grid security measure,’ they said. The entire Paper can be read here . Kenneth Brunninx Associate Professor at the Faculty of Engineering, Governance and Management, where he uses quantitative models to evaluate energy policy and market design with the aim of reducing CO2 emissions. Simon Tindemans is Associate Professor in the Intelligent Electrical Power Grids group at Faculty of Electrical Engineering, Mathematics and Computer Science. His research interests include uncertainty and risk management for power grids. TU Delft PowerWeb Institute is a community of researchers who are investigating how to make renewable energy systems reliable, future proof and accessible to everyone.