The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

人間を模擬した知覚系を持つ2足ヒューマノイドロボットの自己位置推定機能に対する歩行動作の影響に関する研究

早大学位記番号:新8279早稲田大

Tactile Perception And Visuotactile Integration For Robotic Exploration

As the close perceptual sibling of vision, the sense of touch has historically received less than deserved attention in both human psychology and robotics. In robotics, this may be attributed to at least two reasons. First, it suffers from the vicious cycle of immature sensor technology, which causes industry demand to be low, and then there is even less incentive to make existing sensors in research labs easy to manufacture and marketable. Second, the situation stems from a fear of making contact with the environment, avoided in every way so that visually perceived states do not change before a carefully estimated and ballistically executed physical interaction. Fortunately, the latter viewpoint is starting to change. Work in interactive perception and contact-rich manipulation are on the rise. Good reasons are steering the manipulation and locomotion communities’ attention towards deliberate physical interaction with the environment prior to, during, and after a task. We approach the problem of perception prior to manipulation, using the sense of touch, for the purpose of understanding the surroundings of an autonomous robot. The overwhelming majority of work in perception for manipulation is based on vision. While vision is a fast and global modality, it is insufficient as the sole modality, especially in environments where the ambient light or the objects therein do not lend themselves to vision, such as in darkness, smoky or dusty rooms in search and rescue, underwater, transparent and reflective objects, and retrieving items inside a bag. Even in normal lighting conditions, during a manipulation task, the target object and fingers are usually occluded from view by the gripper. Moreover, vision-based grasp planners, typically trained in simulation, often make errors that cannot be foreseen until contact. As a step towards addressing these problems, we present first a global shape-based feature descriptor for object recognition using non-prehensile tactile probing alone. Then, we investigate in making the tactile modality, local and slow by nature, more efficient for the task by predicting the most cost-effective moves using active exploration. To combine the local and physical advantages of touch and the fast and global advantages of vision, we propose and evaluate a learning-based method for visuotactile integration for grasping

Model-Based Environmental Visual Perception for Humanoid Robots

The visual perception of a robot should answer two fundamental questions: What? and Where? In order to properly and efficiently reply to these questions, it is essential to establish a bidirectional coupling between the external stimuli and the internal representations. This coupling links the physical world with the inner abstraction models by sensor transformation, recognition, matching and optimization algorithms. The objective of this PhD is to establish this sensor-model coupling

Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

Intervall-basierte Kartierung von statischen Soundquellen durch ein mobiles Mikrofonarray

Mobile Serviceroboter nehmen vermehrt Einzug in das tägliche Leben und das industrielle Umfeld. Um ihre Aufgaben umzusetzen, müssen sie die Fähigkeit besitzen, ihre Umgebung ausreichend wahrzunehmen. Viel Aufmerksamkeit wurde bisher der visuellen Erfassung durch Kameras oder Laser-scanner gegeben. Im Vergleich dazu konzentriert sich hingegen wenig Forschung auf die akustische Wahrnehmung. In vielen Einsatzgebieten zeigt sich, dass Mikrofone relevante Informationen aus der Umgebung ermitteln können, die durch visuelle Sensoren nicht erfassbar sind. Ziel dieser Dissertation ist es, ein neues Verfahren zu entwickeln, um die dreidimensionale Kartierung von mehreren statischen Soundquellen innerhalb einer metrischen Karte durchzuführen. Dabei wird eine mobile Plattform verwendet, die mit mehreren Mikrofonen ausgerüstet ist und autonom durch die Umgebung navigiert. Für die Kartierung wird in dieser Arbeit das neue Verfahren IB-SSM (engl. Interval-based Sound Source Mapping) vorgestellt. IB-SSM basiert auf der Intervall-Arithmetik und führt mathematische Berechnungen auf begrenzten Mengen durch. Im Vergleich zu bekannten Verfahren der Soundquellenkartierung werden einige Einschränkungen aufgehoben. So wird für IB-SSM die Gesamtzahl der aktiven Soundquellen nicht als bekannt vorausgesetzt. Bekannte Verfahren heben geometrische Mehrdeutigkeiten durch die Randbedingungen in der Versuchsdurchführung auf. Demgegenüber werden in IB-SSM die geometrischen Mehrdeutigkeiten explizit mit modelliert. Falls mehrere Soundquellen aktiv sind, muss keine Datenzuordnung zwischen der möglichen Position einer Soundquelle und der Messung durchgeführt werden. Um die Robustheit des Verfahrens für reale Anwendungen zu steigern, werden in dieser Arbeit die Unsicherheiten der akustischen Merkmale und der Mikrofonpositionen durch Methoden der Intervall-Arithmetik beschrieben. Um die Unsicherheit der akustischen Merkmale zu ermitteln, werden in dieser Dissertation zur vergleichenden Evaluation die zwei alternativen Verfahren DoATiD (engl. Direction-of-Arrival-based Time Difference) und InTiD (engl. Interval-based Time Difference) entwickelt. DoATiD verwendet für die Berechnung die Ergebnisse von bekannten Ansätzen der Soundquellenlokalisierung. Im Gegensatz dazu basiert InTiD direkt auf intervall-basierten Methoden, die auf die Mikrofonsignale angewendet werden. Die vorgestellten Verfahren werden jeweils zunächst mit simulierten sowie nachfolgend mit realen Daten evaluiert. Weiterhin erfolgt ein systematischer Vergleich mit klassischen Verfahren. Diese Arbeit zeigt, dass das vorgestellte Kartierungsverfahren IB-SSM korrekte Bereiche für die Position von mehreren Soundquellen berechnet und in realen Umgebungen anwendbar ist

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data