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Towards Evolved Time to Contact Neurocontrollers for Quadcopters

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Artificial Life and Computational Intelligence (ACALCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9592))

Abstract

Bio-inspired controllers based on visual odometry — or time to contact — have been previously shown to allow vehicles to navigate in a way that simultaneously specifies both the spatial waypoint and temporal arrival time at the waypoint, based on a single variable, tau (\(\tau \)). In this study, we present an initial investigation into the evolution of neural networks as bio-inspired tau-controllers that achieve successful mappings between \(\tau \) and desired control outputs. As this mapping is highly nonlinear and difficult to hand-design, an evolutionary algorithm is used to progressively optimise a population of neural networks based on quality of generated behaviour. The proposed system is implemented on Hardware-in-the-loop setup and demonstrated for the autonomous landing of a quadcopter. Preliminary results indicate that suitable controllers can be successfully evolved.

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Authors and Affiliations

  1. CSIRO Autonomous Systems Program, QCAT, 1 Technology Court, Pullenvale, Brisbane, QLD, 4069, Australia

    David Howard & Farid Kendoul

Authors
  1. David Howard
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  2. Farid Kendoul
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Corresponding author

Correspondence to David Howard .

Editor information

Editors and Affiliations

  1. Canberra, Aust Capital Terr, Australia

    Tapabrata Ray

  2. University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  3. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

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Howard, D., Kendoul, F. (2016). Towards Evolved Time to Contact Neurocontrollers for Quadcopters. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_28

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  • DOI: https://doi.org/10.1007/978-3-319-28270-1_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28269-5

  • Online ISBN: 978-3-319-28270-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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