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Auflistung nach Autor:in "Zhang, Jianwei"

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  • Konferenzbeitrag
    Natural 3D Interaction Techniques for Locomotion with Modular Robots
    (Mensch und Computer 2015 – Proceedings, 2015) Krupke, Dennis; Lubos, Paul; Bruder, Gerd; Zhang, Jianwei; Steinicke, Frank
    Defining 3D movements of modular robots is a challenging task, which is usually addressed with computationally expensive algorithms that aim to create self-propelling locomotion. So far only few user interfaces exist which allow a user to naturally interact with a modular robot in real-time. In this paper we present two approaches for baseline research of 3D user interfaces for intuitive manipulation of 3D movements of a modular chain-like robot in the scope of an iterative design process. We present a comparative evaluation of the techniques, which shows that they can provide intuitive human-robot interaction via remote control for real-time guidance of modular robots to move through heavy terrains and pass obstacles. In particular, our results show that steering a robot’s locomotion via rotational hand movements has benefits for challenging locomotion tasks compared to translational hand movements. We discuss the results and present lessons learned for steering user interfaces for modular robots.
  • Zeitschriftenartikel
    The RACE Project
    (KI - Künstliche Intelligenz: Vol. 28, No. 4, 2014) Hertzberg, Joachim; Zhang, Jianwei; Zhang, Liwei; Rockel, Sebastian; Neumann, Bernd; Lehmann, Jos; Dubba, Krishna S. R.; Cohn, Anthony G.; Saffiotti, Alessandro; Pecora, Federico; Mansouri, Masoumeh; Konečný, Štefan; Günther, Martin; Stock, Sebastian; Lopes, Luis Seabra; Oliveira, Miguel; Lim, Gi Hyun; Kasaei, Hamidreza; Mokhtari, Vahid; Hotz, Lothar; Bohlken, Wilfried
    This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.