MONODA: A Neural Modular Architecture for Obstacle Avoidance without Knowledge of the Environment

  • Authors:
  • Catarina Silva;Bernardete Ribeiro

  • Affiliations:
  • -;-

  • Venue:
  • IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
  • Year:
  • 2000

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Abstract

A new technique is proposed to detect and avoid obstacles for a mobile robot in an unknown environment. The usual problem of having too much sensorial information is dealt with by using several neural networks that cooperate in the guidance of the robot. Several unknown obstacle configurations were presented to the modular networks, proving that the MONODA architecture is very effective for obstacle avoidance when there is neither a priori nor a posteriori map of the environment.