In this paper, we propose using Long Short Term Memory Networks (LSTM) to serve as planners for bio-inspired robotic motion. LSTMs can learn long or short term correlations of sequential data. Using LSTM networks, we implement a motion planner using simulated fish trajectories. The motion or path planning unit can then be implemented on robots such that they can operate autonomously without knowing their absolute position in a global frame. Simulation results show that the planned path demonstrates characteristics that are similar to simulated fish trajectories. This work may lead to learning animal behavior and then formulating bio-inspired path planners for robots to operate in unknown environments.


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    Title :

    Using recurrent neural networks (RNNs) as planners for bio-inspired robotic motion


    Contributors:
    Khan, Ayesha (author) / Zhang, Fumin (author)


    Publication date :

    2017-08-01


    Size :

    1089961 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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