From speech recognition to instruction learning

In robotics, learning from demonstration is a research field that has gained a lot of attention since it provides an easy and intuitive way to teach a robot complex movements. When teaching a human it is common to use natural language to provide additional information. Regardless of that, it is rarely investigated how speech can be used in addition for teaching a robot with learning from demonstration. Therefore this thesis examines how natural language is already used in robotics and how the learning process can be supported further by using speech. Furthermore a learning from demonstration system is implemented based on task spaces that are an intuitive abstraction of the state of the robot and its relation to the environment and therefore are more correlated with spoken instructions. Additional a task space selection instruction and a correction instruction are introduced to use natural language to support the learning process. They are evaluated on a real robot in an experiment where the robot has to draw a point.In der Robotik ist learning from demonstration ein Forschungsfeld, welches viel Beachtung bekommen hat, da es ermöglicht, einen Roboter auf einfache und intuitive Weise komplexe Bewegungen beizubringen. Sofern man einem Menschen etwas beibringt, ist es üblich, natürliche Sprache zu benutzen um zusätzliche Informationen zu vermitteln. Ungeachtet dessen ist es kaum erforscht worden, wie Sprache zusätzlich genutzt werden kann, um Robotern etwas mittels learning from demonstration beizubringen. Daher untersucht diese Thesis, wie natürliche Sprache in der Robotik bereits genutzt wird und wie der Lernprozess mittels Sprache besser unterstützt werden kann. Im Weiteren wird ein learning from demonstration System auf Basis von task spaces implementiert, welche eine intuitive Abstraktion des Zustands des Roboters und dessen Relation zur Umgebung sind und daher stärker mit gesprochenen Anweisungen verknüpft sind. Zusätzlich wird eine task space-Auswahl-Instruktion und eine Verbesserungs-Instruktion vorgestellt, um natürliche Sprache zur Unterstützung des Lernprozesses zu nutzen. Diese werden auf einem echten Roboter mittels eines Experiments evaluiert, bei dem der Roboter einen Punkt zeichnen muss

Design and layout of an energy autarkic wireless sensor network in underground metro tunnels

The transition to a society increasingly reliant on underground mass transportation in the 21st century has led to heightened demands on safety and security of its passengers concerning terror attacks and natural disasters. The Fraunhofer Ernst-Mach-Institut, in coordination with several partners in India and Germany, is developing an integrated security management and emergency response system. The system is based on energy self-sufficient wireless sensor networks for the communication of security relevant sensing information reliably to the central management system. In this paper, the system requirements, layout and operating modes are described. Based on these, an energy consumption assessment was compiled and the requirements for the energy harvesting sub-system were defined

A concept for an ultra-low power sensor network - detecting and monitoring disaster events in underground metro systems

In this paper, the concept for an ultra-low power wireless sensor network (WSN) for underground tunnel systems is presented highlighting the chosen sensors. Its objectives are the detection of emergency events either from natural disasters, such as flooding, or from terrorist attacks. Earlier works have demonstrated that the power consumption for the communication can be reduced such that the data acquisition (i.e. sensor subsystem) becomes the most significant energy consumer. By using ultra-low power components for the smoke detector, a hydrostatic pressure sensor for water ingress detection and a passive acoustic emission sensor for explosion detection, all considered threats are covered while the energy consumption can be kept very low in relation to the data acquisition. The total average consumption for operating the sensor sub-system is calculated to be less than 35.9 mu W

Ultra-low power sensor system for disaster event detection in metro tunnel systems

In this extended paper, the concept for an ultra-low power wireless sensor network (WSN) for underground tunnel systems is presented highlighting the chosen sensors. Its objectives are the detection of emergency events either from natural disasters, such as flooding or fire, or from terrorist attacks using explosives. Earlier works have demonstrated that the power consumption for the communication can be reduced such that the data acquisition (i.e. sensor sub-system) becomes the most significant energy consumer. By using ultra-low power components for the smoke detector, a hydrostatic pressure sensor for water ingress detection and a passive acoustic emission sensor for explosion detection, all considered threats are covered while the energy consumption can be kept very low in relation to the data acquisition. In addition to 1 the sensor system is integrated into a sensor board. The total average power consumption for operating the sensor sub-system is measured to be 35.9 µW for lower and 7.8 µW for upper nodes