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Tracking improves performance of biological collision avoidance models

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Abstract

Collision avoidance models derived from the study of insect brains do not perform universally well in practical collision scenarios, although the insects themselves may perform well in similar situations. In this article, we present a detailed simulation analysis of two well-known collision avoidance models and illustrate their limitations. In doing so, we present a novel continuous-time implementation of a neuronally based collision avoidance model. We then show that visual tracking can improve performance of these models by allowing an relative computation of the distance between the obstacle and the observer. We compare the results of simulations of the two models with and without tracking to show how tracking improves the ability of the model to detect an imminent collision. We present an implementation of one of these models processing imagery from a camera to show how it performs in real-world scenarios. These results suggest that insects may track looming objects with their gaze.

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Author notes

  1. Vivek Pant

    Present address: Atmel Corporation, San Jose, CA, USA

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ, 85721, USA

    Vivek Pant & Charles M. Higgins

  2. Department of Neuroscience, The University of Arizona, Tucson, AZ, 85721, USA

    Charles M. Higgins

Authors
  1. Vivek Pant
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  2. Charles M. Higgins
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Correspondence to Charles M. Higgins.

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Pant, V., Higgins, C.M. Tracking improves performance of biological collision avoidance models. Biol Cybern 106, 307–322 (2012). https://doi.org/10.1007/s00422-012-0499-1

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  • DOI: https://doi.org/10.1007/s00422-012-0499-1

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