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A Neural Field Approach to Obstacle Avoidance

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KI 2016: Advances in Artificial Intelligence (KI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9904))

Abstract

Cognitive robotics aims at understanding biological processes, though it has also the potential to improve future robotics systems. Here we show how a biologically inspired model of motor control with neural fields can be augmented with additional components such that it is able to solve a basic robotics task, that of obstacle avoidance. While obstacle avoidance is a well researched area, the focus here is on the extensibility of a biologically inspired framework. This work demonstrates how easily the biological inspired system can be used to adapt to new tasks. This flexibility is thought to be a major hallmark of biological agents.

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Author information

Authors and Affiliations

  1. University of Applied Science Bonn Rhein Sieg, Sankt Augustin, Germany

    Chun Kwang Tan & Paul G. Plöger

  2. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada

    Thomas P. Trappenberg

Authors
  1. Chun Kwang Tan
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  2. Paul G. Plöger
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  3. Thomas P. Trappenberg
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Corresponding author

Correspondence to Chun Kwang Tan .

Editor information

Editors and Affiliations

  1. Alpen-Adria Universität Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  2. University of Basel, Basel, Switzerland

    Malte Helmert

  3. Technische Universität Graz, Graz, Austria

    Franz Wotawa

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© 2016 Springer International Publishing AG

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Tan, C.K., Plöger, P.G., Trappenberg, T.P. (2016). A Neural Field Approach to Obstacle Avoidance. In: Friedrich, G., Helmert, M., Wotawa, F. (eds) KI 2016: Advances in Artificial Intelligence. KI 2016. Lecture Notes in Computer Science(), vol 9904. Springer, Cham. https://doi.org/10.1007/978-3-319-46073-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-46073-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46072-7

  • Online ISBN: 978-3-319-46073-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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