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Biologically based artificial navigation systems: review and prospects

O Trullier1, S I Wiener, A Berthoz

  • 1Département de Biologie, Ecole Normale Supérieure, Paris, France. trullier@wotan.ens.fr

Progress in Neurobiology
|April 1, 1997
PubMed
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This study unifies animal navigation theories into a hierarchical framework, classifying strategies by processing complexity. Current computational models, while biologically inspired, do not fully replicate the sophisticated capabilities of cognitive maps.

Area of Science:

  • Comparative psychology
  • Computational neuroscience
  • Robotics

Background:

  • Animal navigation research encompasses diverse theories with overlapping terminologies.
  • Existing frameworks often rely on sensor availability rather than information processing.
  • Understanding spatial cognition is crucial for both biological and artificial systems.

Purpose of the Study:

  • To define and unify concepts and terminologies in animal navigation theories.
  • To present a hierarchical framework for classifying navigation strategies based on processing complexity.
  • To review computational models of animal navigation, particularly those inspired by spatial cognitive maps.

Main Methods:

  • Development of a four-level hierarchical framework: Guidance, Place recognition-triggered Response, Topological navigation, and Metric navigation.

Related Experiment Videos

  • Classification based on perceived, represented, and processed information, emphasizing central processing.
  • Review and analysis of computational models of animal navigation (animats) against biological data and cognitive map theories.

    • Main Results:

    • The proposed framework categorizes navigation strategies by information processing requirements.
    • Computational models, despite biological inspiration and cognitive map-like mechanisms, map to different levels of the hierarchy.
    • No reviewed model fully replicates the detour and shortcut capabilities characteristic of Tolman's 'Cognitive Maps'.

    Conclusions:

    • A unified framework aids in understanding and comparing diverse animal navigation theories.
    • Current computational models offer valuable insights but have limitations in replicating true cognitive map functions.
    • Further research is needed to develop algorithms that capture the full robustness of biological spatial cognition for psychobiology and robotics.