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Robot Learning from Human Demonstrations for Human-Robot Synergy
Get PDFHuman-robot synergy enables new developments in industrial and assistive robotics research. In recent years, collaborative robots can work together with humans to perform a task, while sharing the same workplace. However, the teachability of robots is a crucial factor, in order to establish the role of robots as human teammates. Robots require certain abilities, such as easily learning diversified tasks and adapting to unpredicted events. The most feasible method, which currently utilizes human teammate to teach robots how to perform a task, is the Robot Learning from Demonstrations (RLfD). The goal of this method is to allow non-expert users to a programa a robot by simply guiding the robot through a task. The focus of this thesis is on the development of a novel framework for Robot Learning from Demonstrations that enhances the robotsa abilities to learn and perform the sequences of actions for object manipulation tasks (high-level learning) and, simultaneously, learn and adapt the necessary trajectories for object manipulation (low-level learning). A method that automatically segments demonstrated tasks into sequences of actions is developed in this thesis. Subsequently, the generated sequences of actions are employed by a Reinforcement Learning (RL) from human demonstration approach to enable high-level robot learning. The low-level robot learning consists of a novel method that selects similar demonstrations (in case of multiple demonstrations of a task) and the Gaussian Mixture Model (GMM) method. The developed robot learning framework allows learning from single and multiple demonstrations. As soon as the robot has the knowledge of a demonstrated task, it can perform the task in cooperation with the human. However, the need for adaptation of the learned knowledge may arise during the human-robot synergy. Firstly, Interactive Reinforcement Learning (IRL) is employed as a decision support method to predict the sequence of actions in real-time, to keep the human in the loop and to enable learning the usera s preferences. Subsequently, a novel method that modifies the learned Gaussian Mixture Model (m-GMM) is developed in this thesis. This method allows the robot to cope with changes in the environment, such as objects placed in a different from the demonstrated pose or obstacles, which may be introduced by the human teammate. The modified Gaussian Mixture Model is further used by the Gaussian Mixture Regression (GMR) to generate a trajectory, which can efficiently control the robot. The developed framework for Robot Learning from Demonstrations was evaluated in two different robotic platforms: a dual-arm industrial robot and an assistive robotic manipulator. For both robotic platforms, small studies were performed for industrial and assistive manipulation tasks, respectively. Several Human-Robot Interaction (HRI) methods, such as kinesthetic teaching, gamepad or a hands-freea via head gestures, were used to provide the robot demonstrations. The a hands-freea HRI enables individuals with severe motor impairments to provide a demonstration of an assistive task. The experimental results demonstrate the potential of the developed robot learning framework to enable continuous humana robot synergy in industrial and assistive applications
Alterungsanalyse komplexer analoger integrierter Schaltungen aus Systemsicht
Get PDFThe design of analog circuits ranges from the specifications on system level, the selection of a suitable circuit topology up to the choice of the concrete physical dimensions of components like transistors. The individual steps are performed within computer-aided design environments. These environments are based on a database made available by the semiconductor manufacturers containing process parameters and influences on the components. In particular, the influences to be considered in the design have increased in recent years due to the continuous reduction of the producible structural sizes. Thus, it must be possible to analyze the deviations due to process, temperature, time degradation and, for special applications, radiation influences during the design phase. Conventional approaches regard these additional effects as standing next to the actual design process. As a result, the latter is no longer consistent and it is much more complex to consider different circuits and effects on different abstraction levels within the design flow. The focus of this work lies on the development of a consistent consideration of process, voltage, temperature, aging and radiation influences (PVTAR) during the entire design process of analog circuits to the initial measurement of manufactured circuits. To achieve this goal, a transistor model was extended by the influences to be considered. Thereby, the analysis of the additional effects is seamlessly integrated into conventional design processes and methods. In addition, the possibility of a structured analog design is evaluated. This approach allows the estimation of PVTAR influences on dedicated analog function blocks and their propagation on circuit level. Thus, the enormous simulation effort associated with aging analyses can be reduced. The design and manufacture of circuits is always followed by the measurement of the core properties of these circuits. In the context of this work a method was developed which makes it possible to use all insights from the design of a circuit for the improvement of the measuring results. In addition, the internal parameter sets of individual components can be inferred from the terminal behavior of circuits and systems. Finally, the results of the measurement method can be used for the automated calculation of circuit reliability parameters
Schalldämpfung zur Unterdrückung thermoakustischer Instabilitäten unter Brennkammerbedingungen
Get PDFThe replacement of the Helmholtz resonators currently used for damping thermoacoustic instabilities in stationary gas turbines by ceramic pore absorbers could improve both the efficiency of combustion and the operating range of the gas turbines. In this thesis, the effect of ceramic pore absorbers on thermoacoustic instabilities in a combustion chamber was experimentally investigated. Their sound absorption properties were calculated simulatively under combustion chamber conditions as well as determined experimentally under normal conditions and at ambient pressure increased to 5 bar. The comparison of the results was used to validate the simulation. Finally, the simulation was used to determine acoustic target parameters under specific operating conditions
Response of Southwest Pacific storminess to changing climate
Get PDFMid-latitude island nations of the Southwest Pacific (SWP) region like New Zealand not only host winter mid-latitude storms but also tropical storms that develop during summer and autumn. In recent decades, perhaps under the influence of a changing global climate, such tropical storms are observed to travel longer distances towards the southern mid latitudes. The aim of the current study is to estimate likely changes in future storminess in the SWP region during summer and autumn. It uses a new set up of a regional coupled atmosphere-ocean model (integrated over five years each under historical, 1960-1964, and projected future, 2095-2099, boundary conditions) as a tool that allows for frequent air-sea interaction at a mean horizontal resolution of about 25 km. The role of observed changes in large-scale environmental variables in causing recent changes in storminess is illustrated, and a relationship between the two is established by multiple linear regression. This relationship is used to construct scenarios of likely changes in future storminess using the simulated (projected future minus historical) differences in large-scale environmental conditions
Untersuchung eines magnetischen Lagerungskonzepts einer rotierenden Schleifkugel für achsenlose Mikroschleifwerkzeuge und der auftretenden elektrodynamischen Effekte
Get PDFThe development of ever more compact machine tools for machining smaller pieces and their surfaces is becoming increasingly important, above all in medicine, micro-machining, optics and mechatronics. This work describes the design of a novel concept for the bearing and control of a grinding ball, which is get in rotation by a pneumatic air-driven flow. Particular emphasis is placed on the methodology and parameter studies, which in addition to analytical calculations, a 3D simulation model has been created in addition. The focus of the investigations was the systematic pre-selection of the geometry and the generation of the magnetic field in the correct sections of the calotte, the particular difficulty in the consideration of the three-dimensional force generation and in the decoupling of the magnetic and mechanical quantities.The analytical and the resulting 3D-FEM model of the magnetic bearing with respect to the tensile forces could be confirmed experimentally. The analytical results became more inaccurate as soon as the saturation in the nucleus was reached. For the field guidance in the magnetic bear a ferromagnetic material without sheets was used. Due to the electrical conductivity of the material, the propagation of the eddy currents was high pronounced. The consideration of the electrodynamic effects on the signals was absolutely necessary at high sampling rates (kHz).The investigations have shown that the method of exact linearization is most effective for the nonlinear control of the considered system. Thus, the entire working area within the form and any positions of the ball can be accurately represented. The theory of the control design is based on a nonlinear transformation in the form of tables and calculations as well as the nonlinear decoupling of currents and forces. With the new control method, a clear control structure for the magnetic bearing could be realized. The model and parameter uncertainties could be compensated by the integrator components
Essays on Consumers' Attitudes toward Digital Communication
Get PDFThis cumulative dissertation consists of three research paper. They all aim to give a detailed overview and analysis of the effectiveness of different forms of digital advertising, namely, online touchpoints, mobile advertising, and social media advertising. The construct attitudes toward advertising serves as the central measure of effectiveness
Retrieval of stratospheric aerosol characteristics from spaceborne limb sounders
Get PDFIn this thesis, the methods to retrieve aerosol extinction coefficient (Ext) and particle size distribution (PSD) parameters from the remote sensing instruments measuring in the limb-viewing geometry are presented. The Ext retrieval algorithm is applied for SCIAMACHY instrument as well as for OMPS measurements. These products compose global stratospheric aerosol databases, which are covering over 16 years. Additionally, the aerosol PSD parameters are retrieved in the tropical region (20AAdegreeN-20AAdegreeS) from SCIAMACHY limb measurements, creating a unique product. Both, Ext and PSD products were validated through comparison with other space-borne and in situ measurements, showing good and consistent results. In the thesis, the case studies analyzing changes in Ext and PSD parameters after volcanic eruptions of Manam and Tavurvur are presented. Additionally, the evolution of Ext after eruptions of Sarychev Peak and Kelut as well as after Canadian Wildfires of 2017 is analyzed
New Approaches to Simultaneous Multislice Magnetic Resonance Imaging : Sequence Optimization and Deep Learning based Image Reconstruction
Get PDFMagnetic resonance imaging (MRI) is a versatile imaging modality in clinical diagnostics. Despite the impressive range of application, a main drawback of MRI is its inherently low acquisition speed. However, scan time is crucial for many applications and also for an efficient utilization of MRI in clinical routine. Two developments have influenced MRI recently: Simultaneous multislice imaging (SMS) and deep learning (DL). Simultaneous multislice imaging is a paradigm shift in MRI which has re-emerged in the early 2010'. It yields improved image quality compared to in-plane parallel imaging, because it benefits from increased signal-to-noise ratio and robustness for higher accelerations. SMS sequences accelerate data acquisition by undersampling along the slice dimension and specific algorithms allow reconstruction of these undersampled data. In the first part, SMS was extended to measure multiple image contrasts in contrast-enhanced dynamic MRI. Therefore, a bespoke MRI sequence was developed to accelerate segmented echo-planar imaging of three echoes. Dynamic in-vivo data with sufficient spatial coverage were acquired in an animal model. Data acquisition were fast enough to sample the arterial input function which is essential for pharmacokinetic modeling. Imperfections in the excitation of multiple slice and their relevance for reconstruction algorithms were closely investigated and evaluated for processing of multi-contrast data. This work connects SMS and deep learning. Today, the application of deep learning in medicine assists decision making in medical diagnosis, analysis of radiologic data or personalized medicine in genomics. In MRI however, deep learning has just entered the stage. With two abstracts matching the search term 'deep learning' at the ISMRM 2016, the number of abstracts rose to 42 in 2017 and to 139 in 2018. Most of the early contributions to DL in MRI concern image processing and data evaluation. Image reconstruction itself is mostly conducted in standard fashioned way. Common algorithmic approaches applying deep neural networks for (some) processing steps have shown impressive results and can often be generalized to similar problems. In the second part, the separation of overlapping slice content after SMS was performed by an artificial neural network. This novel reconstruction technique, termed SMSnet, does not require any reference data for calibration of the MR machine's receiver characteristics. Omitting the need for reference data could extend the use of modern accelerated imaging sequences to a broad spectrum of applications. Potential and limitations of this approach were investigated in various experiments accounting for image quality, robustness, sensitivity and how the network generalizes. The discussion at the end summarizes and relates the results of this work to state-of-the-art techniques and recent developments in MRI and gives an outlook to future work on SMS and DL-based reconstructions