Recurrent neural robot controllers: feedback mechanisms for identifying environmental motion dynamics

MCKIBBIN, S., AMAVASAI, B., SELVAN, A., CAPARRELLI, Fabio and OTHMAN, W. (2007). Recurrent neural robot controllers: feedback mechanisms for identifying environmental motion dynamics. Artificial intelligence review, 27 (2-3), 113-130.

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Link to published version:: https://doi.org/10.1007/s10462-008-9087-0
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    Abstract

    In this paper a series of recurrent controllers for mobile robots have been developed. The system combines the iterative learning capability of neural controllers and the optimisation ability of particle swarms. In particular, three controllers have been developed: an Exo-sensing, an Ego-sensing and a Composite controller which is the hybrid of the latter two. The task for each controller is to learn to follow a moving target and identify its trajectory using only local information. We show how the learned behaviours of each architecture rely on different sensory representations, although good results are obtained in all cases.

    Item Type: Article
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Materials Analysis and Research Services
    Identification Number: https://doi.org/10.1007/s10462-008-9087-0
    Page Range: 113-130
    Depositing User: Ann Betterton
    Date Deposited: 12 Jul 2010 15:19
    Last Modified: 18 Mar 2021 21:15
    URI: http://shura.shu.ac.uk/id/eprint/2334

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