A neuroplasticity-inspired neural circuit for acoustic navigation with obstacle avoidance that learns smooth motion paths

  • Danish ShaikhEmail author
  • Poramate Manoonpong
EANN 2017


Acoustic spatial navigation for mobile robots is relevant in the absence of reliable visual information about the target that must be localised. Reactive robot navigation in such goal-directed phonotaxis tasks requires generating smooth motion paths towards the acoustic target while simultaneously avoiding obstacles. We have reported earlier on a neural circuit for acoustic navigation which learned stable robot motion paths for a simulated mobile robot. However, in complex environments, the learned motion paths were not smooth. Here, we extend our earlier architecture, by adding a path-smoothing behaviour, to generate smooth motion paths for a simulated mobile robot. This allows the robot to learn to smoothly navigate towards a virtual sound source while avoiding randomly placed obstacles in the environment. We demonstrate through five independent learning trials in simulation that the proposed extension learns motion paths that are not only smooth but also relatively shorter as compared to those generated without learning as well as by our earlier architecture.


Smooth path-planning Reactive navigation Behaviour-based robotics Phonotaxis Lizard peripheral auditory system Neuroplasticity Correlation-based learning 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Embodied AI and Neurorobotics Laboratory, Centre for BioRobotics, Maersk Mc-Kinney Moeller InstituteUniversity of Southern DenmarkOdense MDenmark
  2. 2.Bio-inspired Robotics and Neural Engineering Laboratory, School of Information Science and TechnologyVidyasirimedhi Institute of Science and TechnologyRayongThailand

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