000 Allgemeines, Wissenschaft
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Das übergeordnete Forschungsgebiet, in das sich die vorliegende Arbeit einbettet, befasst sich mit der Erforschung von informationsverabeitenden Prozessen im Gehirn und der Anwendung der resultierenden Erkenntnisse auf technische Systeme. In Analogie zu biologischen Systemen, deren Beschaffenheit aus den Anforderungen der Umwelt an ihr Verhalten resultiert, leitet sich die Anthropomorphie als Entwurfsprinzip für die Struktur des mit den Menschen interagierenden robotischen Assistenzsystemen ab. Der Autor behandelt in der vorliegende Arbeit das Problem der Erzeugung von Motorverhalten im dreidimensionalen Raum am Beispiel eines anthropomorphen Roboterarmes in einem anthropomorphen robotischen Assistenzsystem. Entwickelt wurde hierbei ein allgemeiner Ansatz, der die Konzepte der Erzeugung von Motorverhalten im 3D-Raum, der Voraussimulation dynamischer Systeme zur Systemdiagnose und zur Suche gewünschter Systemzustände, sowie ein Konzept der Organisation von Verhalten enthält und vereinigt. Nichtlineare dynamische Systeme bilden das mathematische Fundament, die einheitlich, formale Sprache des Ansatzes, mit der sowohl das Motorverhalten des Roboters als auch dessen zeitkontinuierliche Teilsysteme rückgekoppelt werden.
Auf gute Zusammenarbeit
(2008)
Simulated reality environment incorporating humans and physically plausible behaving robots, providing natural interaction channels, with the option to link simulator to real perception and motion, is gaining importance for the development of cognitive, intuitive interacting and collaborating robotic systems. In the present work we introduce a head tracking system which is utilized to incorporate human ego motion in simulated environment improving immersion in the context of human-robot collaborative tasks.
Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave enough system resources for higher level function like forming a sequence of local goals to reach a distant one. The comparatively high performance of local behavior generation allows the global planning to be relatively simple. In the present paper, we apply this approach to generate walking trajectories for a small humanoid robot, the Aldebaran Nao, that are goal-directed and avoid obstacles. The sensor information is a single camera in the head of the robot. The limited field of vision is compensated by head movements. The design of the dynamical system for motion generation and the choice of state variable makes a computationally expensive scene representation or local map building unnecessary.
To enable a robotic assistant to autonomously reach for and transport objects while avoiding obstacles we have generalized the attractor dynamics approach established for vehicles to trajectory formation in robot arms. This approach is able to deal with the time-varying environments that occur when a human operator moves in a shared workspace. Stable fixed points (attractors) for the heading direction of the end-effector shift during movement and are being tracked by the system. This enables the attractor dynamics approach to avoid the spurious states that hamper potential field methods. Separating planning and control computationally, the approach is also simpler to implement. The stability properties of the movement plan make it possible to deal with fluctuating and imprecise sensory information. We implement this approach on a seven degree of freedom anthropomorphic arm reaching for objects on a working surface. We use an exact solution of the inverse kinematics, which enables us to steer the spatial position of the elbow clear of obstacles. The straight-line trajectories of the end-effector that emerge as long as the arm is far from obstacles make the movement goals of the robotic assistant predictable for the human operator, improving man-machine interaction
For any kind of assistant systems, the ability to interact with the human operator and taking into account his or her assumptions and expectations, is the basis for a reasonable behavior. As a consequence the human behavior have to be studied in order to generate driver models that are learned from human driving data. In this work we focus on the improvement of the immersion in driving simulation environment by developing and implementing a cheap and efficient method for head tracking. We also explain why head tracking feedback is crucial for the quality of collected behavioural data, especially for simulators with close screen distances.
In this paper we describe an architecture for behavioral organization based on dynamical systems. This architecture
enables the generation of complex behavioral sequences, which is demonstrated using the example of approaching and
passing a door. The behavioral sequence is generated by activating and deactivating the elementary behaviors dependent
on sensory information and internal logical conditions. The architecture is demonstrated on a mobile KOALA robot and
in simulation as well.