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    A cellular neural network for robot path planning

    Access Status
    Fulltext not available
    Authors
    Zhong, Yongmin
    Shirinzadeh, B.
    Date
    2007
    Type
    Conference Paper
    
    Metadata
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    Citation
    Zhong, Yongmin and Shirinzadeh, Bijian. 2007. A cellular neural network for robot path planning, in Guizzo, E. (ed), The 13th International Conference on Advanced Robotics, Aug 22-25 2007. Jeju, Korea: IEEE.
    Source Title
    The 13th International Conference on Advanced Robotics
    Source Conference
    The 13th International Conference on Advanced Robotics
    URI
    http://hdl.handle.net/20.500.11937/47756
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a new methodology based on neural dynamics for robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. An improved CNN model is established to propagate the target activity within the states pace in the manner of physical heat conduction, which guarantees that the target and the obstacles remain at the peak and the bottom of the activity landscape of the neural network, respectively. The novelty of the proposed neural network model is that local connectivity of neurons is harmonic rather than symmetric in the existing neural network models. The proposed methodology can not only generate real-time, smooth, optimal and collision-free paths without any prior knowledge of the dynamic environment, without explicitly searching over the global free work space or searching collision paths, and without any learning procedures, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

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