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International Conference on Intelligent Transportation

作者： Michael Roth Hanno Winter Institute of Transportation Systems German Aerospace Center (DLR) Braunschweig Germany Control Methods and Robotics Laboratory Technische Universität Darmstadt Germany

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

This paper describes an openly available data set for rail vehicle positioning experiments. The data were collected using the DLR research vehicle RailDriVE on a segment of the harbor railway of Braunschweig, Germany, in February 2019. Several sensors of the RailDriVE equipment and an additional self-sufficient system provided by Technische Universität Darmstadt were employed, including two GNSS receivers, two inertial measurement units (IMU), and several speed and distance sensors (radar, optical, odometer) from the rail domain. Front-facing camera data has been included for documentation purposes. In order to simplify its use, some pre-processing steps were applied to the data, mainly to have common time and coordinate frames. Furthermore, example and reference positioning solutions have been included. The data set is described in detail, with information about the individual sensors and the data set structure (with parameters, raw, pre-processed, and reference data). Our work should be seen as a step towards more open and data-driven research in the rail domain, where experiments are difficult and costly. It is our hope to provide a solid basis for many different research efforts that provide the required technological advances for the rail sector.

关键词： Sensors Rails Global navigation satellite system Optical sensors Receivers Sensor systems Radar tracking

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Generating compact geometric track-maps for train positioning applications

arXiv

引用

arXiv 2019年

作者： Winter, Hanno Luthardt, Stefan Willert, Volker Adamy, Jürgen Control Methods and Robotics Laboratory TU Darmstadt Germany

In this paper, we present a method to generate compact geometric track-maps for train-borne localization applications. Therefore, we first give a brief overview on the purpose of track maps in train-positioning applications. It becomes apparent that there are hardly any adequate methods to generate suitable geometric track-maps. This is why we present a novel map generation procedure. It uses an optimization formulation to find the continuous sequence of track geometries that fits the available measurement data best. The optimization is initialized with the results from a localization filter [1] developed in our previous work. The localization filter also provides the required information for shape identification and measurement association. The presented approach will be evaluated on simulated data as well as on real measurements. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

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Train-borne Localization Exploiting Track-Geometry Constraints – A Practical Evaluation

arXiv

引用

arXiv 2019年

作者： Winter, Hanno Willert, Volker Adamy, Jürgen Control Methods and Robotics Laboratory TU Darmstadt Germany

Today’s railway signalling system heavily relies on trackside infrastructure such as axle counters and track balises. This system has proven itself to be reliable, however, it is not very efficient and, moreover, very costly. Thus, it is not suited to overcome the future challenges in railway transportation. For this reason, signalling systems based on train-borne sensors have gained interest recently. In this context, train-borne localization is one of the main research challenges. So far there is no sensor set-up which meets the demanding requirements for a localization system, both in the sense of accuracy as well as safety. To help overcome these issues in the near future we present our latest research results here. Earlier we published a localization algorithm which is characterized by an increased accuracy in cross-track direction compared to a standard Kalman filter (KF) approach, as has been shown in simulations [1]. To verify these results practically, we recorded data from a Global Navigation Satellite System (GNSS) and an inertial measurement unit (IMU) on a test drive. The localization accuracy is evaluated with the help of OpenStreetMap (OSM) data and site plans. Furthermore, we evaluate the quality of the estimated geometric track-map, which is additionally provided in the process of the localization algorithm [2]. We conclude with some remarks on the research challenges towards train-borne localization and suggest further steps to overcome them. Copyright © 2019, The Authors. All rights reserved.

关键词： Global positioning system

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Learning Nash Equilibria in Monotone Games

arXiv

引用

arXiv 2019年

作者： Tatarenko, Tatiana Kamgarpour, Maryam Control Methods and Robotics Lab Technical University Darmstadt Darmstadt64283 Germany Automatic Control Laboratory ETH Zürich Switzerland

—We consider multi-agent decision making where each agent’s cost function depends on all agents’ strategies. We propose a distributed algorithm to learn a Nash equilibrium, whereby each agent uses only obtained values of her cost function at each joint played action, lacking any information of the functional form of her cost or other agents’ costs or strategy sets. In contrast to past work where convergent algorithms required strong monotonicity, we prove algorithm convergence under mere monotonicity assumption. This significantly widens algorithm’s applicability, such as to games with linear coupling constraints. Copyright © 2019, The Authors. All rights reserved.

关键词： Cost functions

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Receding-horizon planning using recursive Monte Carlo Tree Search with Sparse Action Sampling for continuous state and action spaces

Receding-horizon planning using recursive Monte Carlo Tree S...

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American control Conference

作者： Moritz Schneider Laboratory of Control Methods and Robotics Institute of Automatic Control and Mechatronics Dept. of Electrical Engineering and Information Technology Technische Universit?t Darmstadt Germany

ISBN: (纸本)9781467386838

This paper introduces a recursive, sampling-based Monte Carlo Tree Search (MCTS) approach to planning, i.e. receding horizon control, in continuous state and action nonlinear set-point control problems. Usually, predictive control methods for nonlinear systems aim at (sub-)optimal control behavior through solving a dynamic optimization problem over a fixed prediction horizon in every time step. Tree-based methods, which have recently gained interest in planning, model-based reinforcement learning and path planning, replace the optimization problem by an incremental probabilistic search for (sub-)optimal open-loop control sequences in the space of variable-length closed-loop trajectories. The benefit of using a weaker optimization procedure is that the algorithm is very simple to understand/apply and works for general nonlinear systems. This article is concerned with increasing the control performance and sampling-efficiency of MCTS in continuous state and action spaces based on ideas from the field of standard nonlinear model predictive control.

关键词： Search problems Planning Aerospace electronics Optimization Trajectory Monte Carlo methods Prediction algorithms

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Artificial motivations based on drive-reduction theory in self-referential model-building control systems

Artificial motivations based on drive-reduction theory in se...

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International Joint Conference on Neural Networks (IJCNN)

作者： Moritz Schneider Jürgen Adamy Institute of Automatic Control and Mechatronics Laboratory of Control Methods and Robotics Technische Universität Darmstadt Darmstadt Germany

ISBN: (纸本)9781479919611

Motivation and emotion are inseparable component factors of value systems in living beings, which enable them to act purposefully in a partially unknown and sometimes unforgiving environment. Value systems that drive innate reinforcement learning mechanisms have been identified as key factors in self-directed control and autonomous development towards higher intelligence and seem crucial in the development of a concept of “self” in sentient beings [1]. This contribution is concerned with the relationship between artificial learning control systems and innate value systems. In particular, we adapt the state-of-the-art model of motivational processes based on reduction of generalized drives towards higher flexibility, expressivity and representation capability. A framework for modelling self-adaptive value systems, which develop autonomously starting from an inherited (or designed) innate representation, within a learning control system architecture is formulated. We discuss the relationship of anticipated effects in this control architecture with psychological theory on motivations and contrast our framework with related approaches.

关键词： Biology Artificial intelligence Cognition Adaptation models

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Feedforward Tracking control of Flat Recurrent Fuzzy Systems

引用

Journal of Physics: Conference Series 2014年第2期570卷

作者： Stefan Gering Jürgen Adamy Laboratory of Control Methods and Robotics at Technische Universität Darmstadt Landgraf-Georg-Str. 4 64283 Darmstadt Germany

Flatness based feedforward control has proven to be a feasible solution for the problem of tracking control, which may be applied to a broad class of nonlinear systems. If a flat output of the system is known, the control is often based on a feedforward controller generating a nominal input in combination with a linear controller stabilizing the linearized error dynamics around the trajectory. We show in this paper that the very same idea may be incorporated for tracking control of MIMO recurrent fuzzy systems. Their dynamics is given by means of linguistic differential equations but may be converted into a hybrid system representation, which then serves as the basis for controller synthesis.

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