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Sensorimotor representation and knowledge-based reasoning for spatial exploration and localisation.

C Zetzsche1, J Wolter, K Schill

  • 1Kognitive Neuroinformatik, Universität Bremen, Bremen, Germany.

Cognitive Processing
|May 8, 2008
PubMed
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This study introduces a novel hybrid system for autonomous exploration and navigation in virtual environments. The system efficiently uses sensorimotor features and a maximum information gain strategy for rapid and robust localization.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Autonomous agents require efficient exploration and navigation strategies.
  • Map-based representations can be computationally intensive and inflexible.
  • Sensorimotor-based approaches offer an alternative for real-time decision-making.

Purpose of the Study:

  • To develop and evaluate a hybrid system for autonomous exploration and navigation.
  • To implement a sensorimotor-based representation for virtual agents.
  • To test a knowledge-based reasoning strategy for optimal action selection.

Main Methods:

  • A hybrid system integrating bottom-up sensorimotor processing and top-down knowledge-based reasoning.
  • A non-map-like representation based on sensorimotor features (sensory inputs and motor actions).

Related Experiment Videos

  • Implementation in a virtual mobile agent operating in diverse virtual spatial environments.
  • Two sensorimotor levels (macro for spatial movement, micro for eye movements) utilizing a maximum information gain principle.

    • Main Results:

    • The system demonstrated efficient exploratory motor actions maximizing information gain about the environment.
    • Localization was typically achieved within a few steps in tested virtual rooms.
    • The system exhibited limited computational complexity and robustness to minor environmental variations.

    Conclusions:

    • The developed hybrid system provides an effective approach for autonomous exploration and navigation.
    • Sensorimotor-based representations and information gain principles are viable for efficient agent control.
    • The system's efficiency, speed, and robustness make it suitable for complex virtual environments.