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    Cooperative and Geometric Learning for Path P{lanning of UAVs

    Access Status
    Fulltext not available
    Authors
    Zhang, B.
    Mao, Z.
    Liu, Wan-Quan
    Liu, J.
    Zheng, Z.
    Date
    2013
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Zhang, Baochang and Mao, Zhili and Liu, Wanquan and Liu, Jianzhuang and Zheng, Zheng. 2013. Cooperative and Geometric Learning for Path Planning of UAVs, in International Conference on Unmanned Aircraft Systems (ICUAS), May 28-31 2013, pp. 69-78. Atlanta, GA: IEEE.
    Source Title
    2013 International Conference on Unmanned Aircraft Systems
    Source Conference
    ICUAS
    DOI
    10.1109/ICUAS.2013.6564675
    URI
    http://hdl.handle.net/20.500.11937/30735
    Collection
    • Curtin Research Publications
    Abstract

    We propose a new learning algorithm, named Cooperative and Geometric Learning (CGL), to solve maneuverability, collision avoidance and information sharing problems in path planning for Unmanned Aerial Vehicles (UAVs). The contributions of CGL are threefold: 1) CGL exploits a specific reward matrix G, which leads to a simple and efficient algorithm for the path planning of multiple UAVs. 2) The optimal path in terms of path length and risk measure from a given point to the target point can be calculated. 3) In CGL, the reward matrix G is calculated in real-time and adaptively updated based on the geometric distance and risk information shared by other UAVs. Extensive experimental results validate the effectiveness and feasibility of CGL on the navigation of UAVs.

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