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    Geometric reinforcement learning for path planning of UAVs

    Access Status
    Fulltext not available
    Authors
    Zhang, Baochang
    Mao, Zhili
    Liu, Wan-Quan
    Liu, Jianzhuang
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Zhang, B. and Mao, Z. and Liu, W. and Liu, J. 2013. Geometric reinforcement learning for path planning of UAVs. Journal of Intelligent and Robotic Systems. 77 (2): pp. 391-409.
    Source Title
    Journal of Intelligent and Robotic Systems
    DOI
    10.1007/s10846-013-9901-z
    ISSN
    0921-0296
    URI
    http://hdl.handle.net/20.500.11937/13448
    Collection
    • Curtin Research Publications
    Abstract

    We proposed a new learning algorithm, named Geometric Reinforcement Learning (GRL), for path planning of Unmanned Aerial Vehicles (UAVs). The contributions of GRL are as: (1) GRL exploits a specific reward matrix, which is simple and efficient for path planning of multiple UAVs. The candidate points are selected from the region along the Geometric path from the current point to the target point. (2) The convergence of calculating the reward matrix is theoretically proven, and the path in terms of path length and risk measure can be calculated. (3) In GRL, the reward matrix is adaptively updated based on the Geometric distance and risk information shared by other UAVs. Extensive experimental results validate the effectiveness and feasibility of GRL on the navigation of UAVs.

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