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Nils Einecke and Andrej Robert , "Outdoor Particle Filter Localization with Sparse Observation", International Conference on Advanced Robotics, pp. 590-597, 2019.
AbstractNowadays, autonomous lawn mowers are widely used in Europe. The robust autonomous operation and the ease of installation has lead to a substantial market share. Most autonomous lawn mowers move in a random fashion or with simple patterns because their self-localization capabilities are very limited. In this work, we analyze the potential of using apriori information about the shape of the boundary wire in combination with electromagnetic wire sen...
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Akinobu Hayashi, Dirk Ruiken, Christian Goerick, Tadaaki Hasegawa , "Online adaptation of uncertain models using neural network priors and partially observable planning", International Conference on Robotics and Automation (ICRA) 2019, 2019.
AbstractOne of the key challenges in realizing a robot that is capable of completing a variety of manipulation tasks in the real world is the need to utilize sufficiently compact and rich world models. If the assumed prediction model does not match real observations, planning systems are unable to perform properly. We propose a system that corrects the models based on information collected from the robot's sensors. We encode prior experiences in a neural...
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Martina Hasenjäger and Taizo Yoshikawa , "Machine learning approaches in human walk modeling", IROS 2019 Cutting Edge Forum "Human Movement Understanding for Intelligent Robots and Systems", 2019.
AbstractThe combination of an increasing life expectancy and a low, decreasing birth rate has lead to aging societies in many countries. The resulting problems of a decreasing workforce and an increasing demand in health care are particularly acute in Japan. To address these problems, we aim to develop advanced physical assist devices to improve the quality of life and to extend activities of daily living and activities in the work place. We regard a hum...
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Viktor Losing, Martina Hasenjäger, Barbara Hammer, Heiko Wersing , "Personalized Online Learning of Whole-Body Motion Classes Using Multiple Inertial Measurement Units", International Conference on Robotics and Automation (ICRA), 2019.
AbstractOnline action classification is a field with a variety of application scenarios. It is particularly important for body assisting devices which try to support the motions of its user. This paper investigates the benefits of personalized online learning in this domain. We let different subjects perform various motions which we categorized in eighteen classes. The data was recorded using the XSens bodysuit with 17 integrated inertial sensors, resu...
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Torsten Schwan, Sebastian Schmitt, Andrea Castellani , "Calibration of HVAC system models with monitoring data - Digital Twin meets measurement data", ESI FORUM IN DEUTSCHLAND , 2019.
AbstractModern heat, ventilation and air-conditioning (HVAC) systems for buildings requires engineers to use increasingly more complex physical models to evaluate building performance in early design stages as well as during modernization and reconstruction phases. Those models often provide accurate results regarding total annual heat, cold and power consumption. However, achieving very accurate high temporal resolution results and evaluation of smart ...
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Pouya Aghaei-Pour, Tobias Rodemann, Markus Olhofer, Jussi Hakanen, Kaisa Miettinen , "On Surrogate Management in Interactive Multiobjective Building Energy System Design", ECCOMAS Thematic Conference Computational Sciences and AI in Industry (CSAI), U Jyvaeskyla, 2019.
AbstractWhen thinking of possible extensions of energy systems, decision making for larger buildings consists of a series of complex investment decisions. The consideration involves multiple objectives like investment and annual operation costs, CO 2 emissions and module lifetime to be considered simultaneous. Thus, in building energy system management, methods of multiobjective optimization are needed to support decision making. We have a system upgrade...
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Guo Yu, Yaochu Jin, Markus Olhofer , "References or Preferences – Rethinking Many-objective Evolutionary Optimization", CEC 2019, 2019.
AbstractPast decades have witnessed a rapid development in multi- and many-objective evolutionary optimization. The references-assisted and preference-driven strategies are both widely used in dealing with the multi- and many-objective optimization problems. However, few research analyzes the difference between these two strategies. Thus, this paper analyzes and compares both strategies from background, constructions, similarities, differences, to concer...
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Robin Menzenbach , "Benchmarking Sim-2-Real Algorithms on Real-World Platforms", Technical University of Darmstadt, 2019.
AbstractLearning from simulation is particularly useful, because it is typically cheaper and safer than learning on real-world systems. Nevertheless, the transfer of learned behavior from the simulation to the real word can impose difficulties because of the so-called ’reality gap’. There are multiple approaches trying to close the gap. Although many benchmarks of reinforcement learning algorithms exist, state-of-the-art sim-2-real methods are rarely com...
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Elena Raponi, Mariusz Bujny, Markus Olhofer, Simonetta Boria, Fabian Duddeck , "Surrogate-assisted Topology Optimization of Mechanical Structures", European Cooperation in Science and Technology (COST) Action Training School 2019, 2019.
AbstractMy current research deals with the Topology Optimization (TO) of mechanical structures subjected to static and dynamic crash loads, by means of surrogate modeling techniques and evolutionary computation. TO addresses the task of determining optimal concept structures through changing the material distribution in a given design domain. Although it represents an important tool in the design and analysis of mechanical structures, TO needs much more ...
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Andrea Castellani , "Real-World Anomaly Detection by using Deep Learning algorithms and Digital Twin systems", Universita Politechnica Delle Marche, Ancona, Italy, 2019.
AbstractNowadays, with the continuously growing amount of monitored data present in Smart Company environment, the need for Anomaly Detection technique has become more relevant in order to identify some anomalous behavior from time-series data generated by sensors. With the Digital Twin, a detailed simulation of a complex physical system, it is possible to provide more data to feed in a Machine Learning algorithm and thus help in the anomaly detecti...
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