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Looking down: a model for visual route following in flying insects.

J Stankiewicz1, B Webb1

  • 1School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, United Kingdom.

Bioinspiration & Biomimetics
|July 9, 2021
PubMed
Summary

Honey bees navigate using ground-based visual memories, not just horizons. This study demonstrates a new method for autonomous drones to follow routes using learned ground views, proving effective in real-world tests.

Keywords:
UAVbioroboticshoney beeinsect navigationvisual route following

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Area of Science:

  • Computational Neuroscience
  • Robotics
  • Animal Behavior

Background:

  • Insect visual navigation traditionally relies on panoramic horizon views.
  • Honey bees' ability to navigate in featureless environments challenges this assumption.
  • Alternative navigation strategies, such as using ground-based visual cues, are hypothesized.

Purpose of the Study:

  • To investigate the hypothesis that insects navigate using ground-based view memories.
  • To develop and test a novel visual route-following approach for autonomous navigation.
  • To evaluate the robustness and biological relevance of the proposed navigation system.

Main Methods:

  • Utilized low-resolution aerial views of natural terrain as spatial descriptors.
  • Developed a route-following algorithm using transverse oscillations to center flight paths.
  • Employed a wavelet-based bandpass filter for robust view matching with translational invariance.

Main Results:

  • Ground-based view memories provide robust descriptors of precise spatial locations in natural scenes.
  • The proposed visual route-following system demonstrated robust real-world performance on an autonomous quadcopter up to 30 meters.
  • The wavelet-based filter exhibited double the catchment area of standard view-matching approaches.

Conclusions:

  • Insects can navigate effectively using ground-level visual information, challenging traditional horizon-based models.
  • The developed autonomous navigation system, inspired by insect behavior, offers a viable solution for real-world route following.
  • The findings highlight the potential of biologically inspired algorithms for robotic navigation systems.