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Fernando Lezama, Ricardo Faia, Steffen Limmer, Zahra Foroozandeh, Joao Soares, Sergio Ramos, Zita Vale , "A Scalable Three-Stage Model for Local Energy Community Management and Pricing", CSEE Journal of Power and Energy Systems, 2025.
AbstractThis study introduces and evaluates advanced optimization models for local energy community management, focusing on pricing and energy scheduling. We propose a novel three-stage approach to address the complexities of local energy community price determination and enhance model efficiency. The research builds upon previous works, analyzing a basic local energy community optimization model, modifying the formulation to a bi-level optimization mode...
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Jens Engel, Andrea Castellani, Patricia Wollstadt, Felix Lanfermann, Thomas Schmitt, Sebastian Schmitt, Lydia Fischer, Steffen Limmer, David Luttropp, René Unger, Tobias Rodemann , "A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning", Scientific Data, 2025.
AbstractWe present a large real-world dataset obtained from monitoring a smart company facility over the course of six years, from 2018 to 2023. The dataset includes energy consumption data from various facility areas and components, energy production data from a photovoltaic system and a combined heat and power plant, operational data from heating and cooling systems, and weather data from an on-site weather station. The measurement sensors installed ...
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Simon Kohaut, Nikolas Hohmann, Sebastian Brulin, Benedict Flade, Julian Eggert, Markus Olhofer, Jürgen Adamy, Devendra Dhami, Kristian Kersting , "Hybrid Many-Objective Optimization in Probabilistic Mission Design for Compliant and Effective UAV Routing", ACM Journal on Autonomous Transportation Systems: Special Issue on Applications-Driven UAV Routing and Scheduling Algorithms for Autonomous Transportation Systems, vol. 3, no. 1, pp. 1-24, 2025.
AbstractAdvanced Aerial Mobility encompasses many outstanding applications that promise to revolutionize modern logistics and pave the way for various public services and industry uses. However, throughout its history, the development of such systems has been impeded by the complexity of legal restrictions and physical constraints. Airspaces are tightly shaped by various legal requirements, and aerial vehicles must comply with, among others, energy deman...
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Jens Engel , "Machine Learning Augmented Model Predictive Control for Building Energy Management Systems", 2025.
AbstractIn light of the changing energy landscape and the need to reduce global CO2 emissions, efficient and intelligent energy management in buildings is essential, as they are among the largest contributors to energy con- sumption and emissions. A well-established method for intelligent energy management is model predictive control (MPC). However, a major bot- tleneck for adopting MPC in real-world building energy management sys- tems (EMSs) is t...
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Raphael Wenzel, Malte Probst , Tim Puphal, Markus Amann, Julian Eggert , "Negotiating Cooperative Ordering Problems with Bimodal Planning", IEEE 36th Intelligent Vehicle Symposium 2025, 2025.
AbstractIn Automated Driving (AD), traffic scenarios where two agents must resolve an ordering without knowing each other's intention are critical for expanding the operational design domain of automated vehicles to urban environments. These scenarios require negotiation to determine who passes first through an interaction zone. We present a novel agreement measure and negotiation approach to resolve these ordering problems across a wide range of common ...
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Phillip Richter, Arthur Maximilian Noller, Heiko Wersing, Anna-Lisa Vollmer , "AURORA: A Platform for Advanced User-driven Robotics Online Research and Assessment", Proc. ACM/IEEE Conf. on Human Robot Interaction (HRI) , 2025.
AbstractAURORA is a software platform, that facilitates scalable deployment of robotic simulations over the web for the Human-Robot Interaction (HRI) community. As robotics is becoming increasingly important in various disciplines, there is a growing need for accessible and scalable research methods. Traditional experiments often require expensive hardware and in-person participation, limiting accessibility and participant diversity. Our platform ...
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Markus Amann, Malte Probst , Raphael Wenzel, Thomas H Weisswange, Miguel Ángel Sotelo , "Optimal Behavior Planning for Implicit Communication using a Probabilistic Vehicle-Pedestrian Interaction Model", 36th IEEE Intelligent Vehicles Symposium (IV 2025):, 2025.
AbstractIn interactions between automated vehicles (AVs) and crossing pedestrians, modeling implicit vehicle communication is crucial. In this work, we present a combined prediction and planning approach that allows to consider the influence of the planned vehicle behavior on a pedestrian and predict a pedestrian's reaction. We plan the behavior by solving two consecutive optimal control problems (OCPs) analytically, using variational calculus. We perfor...
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David Rother, Franziska Herbert, Fabian Kalter, Dorothea Koert, Joni Pajarinen, Jan Peters, Thomas H Weisswange , "Entropy Based Blending of Policies for Multi-Agent Coexistence", Autonomous Agents and Multi-Agent Systems, vol. 27, no. 39, 2025.
Abstractapproaches have focused on environments with collaborative human behavior or a small, defined set of situations. When deploying robots in human-inhabited environments in the future, the diversity of interactions surpasses the capabilities of pre-trained collaboration models. ”Coexistence” environments, characterized by agents with varying or partially aligned objectives, present a unique challenge for robotic collaboration. Traditional reinforcem...
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Navid Hamid , "Neuro-Symbolic Systems for Constitutional Control in Advanced Air Mobility Systems", Thesis, 2025.
AbstractNeuro-Symbolic Systems that combine white-box models with advanced machine learning systems provide a new opportunity for designing missions for intelligent transportation systems (ITS). In this context, deep probabilistic logic programs which extend First Order Logic by assigning probabilities or distributions to variables which are learned by neural networks have specifically found popularity for representing laws and uncertainties associated w...
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Andreas Sochopoulos, Nikolaos Tsagkas, Joao Moura, Nikolay Malkin, Michael Gienger, Sethu Vijayakumar , "Fast Flow-based Visuomotor Policies via Conditional Optimal Transport Couplings", ArXiv, 2025.
AbstractDiffusion and flow matching policies have recently shown remarkable performance in robotic applications by accurately capturing multimodal robot trajectory distributions. However, their computationally expensive inference, due to numerical integration of an ODE or SDE, limits their application as real-time controllers for robots. We introduce a methodology that utilizes conditional Optimal Transport couplings between noise and samples, in order t...
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