Locust’s Looming Detectors for Robot Sensors

  • F. Claire Rind
  • Roger D. Santer
  • J. Mark Blanchard
  • Paul F. M. J. Verschure


Visual systems in the animal kingdom are incredibly good at extracting useful information from what can often be a very complicated world. Many of these systems can provide inspiration for the design of our own ’seeing machines’ which we can then use in a variety of applications. Our own research is concerned with the detection of ’looming’ or motion in depth. Our biological inspiration is the locust, Locusta migratoria, which possesses two uniquely identifiable neurons (the LGMD and DCMD) that respond preferentially to movements directly towards the animal. The way in which these cells are able to identify such stimuli is now becoming well understood. As such, we have been able to create a plausible computational model of the afferent inputs to these neurons that has been shown to respond in a locust-like way to looming stimuli. This model is now being used to control the movements of a mobile robot within a simplified visual environment. We aim to continue the development of this model so that it may one day function within the same visual world as the locust itself.


Mobile Robot Optic Lobe Visual Environment Machine Vision System Direct Collision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Wien 2003

Authors and Affiliations

  • F. Claire Rind
  • Roger D. Santer
  • J. Mark Blanchard
  • Paul F. M. J. Verschure

There are no affiliations available

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