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Application of Biological Learning Theories to Mobile Robot Avoidance and Approach Behaviors

Author

Listed:
  • Carolina Chang

    (Boston University Neurobotics Lab, Department of Cognitive and Neural Systems, 677 Beacon Street, Boston, MA 2215, USA)

  • Paolo Gaudiano

    (Boston University Neurobotics Lab, Department of Cognitive and Neural Systems, 677 Beacon Street, Boston, MA 2215, USA)

Abstract

We present a neural network that learns to control approach and avoidance behaviors in a mobile robot based on a form of animal learning known asoperant conditioning. Learning, which requires no supervision, takes place as the robot moves around an environment cluttered with obstacles and light sources. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot's sensors. In this article we provide a detailed presentation of the model, and show our results with theKheperaandPioneer 1mobile robots.

Suggested Citation

  • Carolina Chang & Paolo Gaudiano, 1998. "Application of Biological Learning Theories to Mobile Robot Avoidance and Approach Behaviors," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 79-114.
    • Handle: RePEc:wsi:acsxxx:v:01:y:1998:i:01:n:s0219525998000065
    DOI: 10.1142/S0219525998000065
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