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Minimalistic approach to 3D obstacle avoidance behavior from simulated evolution

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84104

Neumann,  TR
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83985

Huber,  SA
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Neumann, T., Huber, S., & Bülthoff, H. (1997). Minimalistic approach to 3D obstacle avoidance behavior from simulated evolution. Proceedings of the 7th International Conference on Artificial Neural Networks ICANN 1997 (Eds. W. Gerstner, A. Germond, M. Hasler and J.-D. Nicoud), 715-720.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E9D0-7
Abstract
We present a minimalistic approach to establish obstacle avoidance and course stabilization behavior of a simulated flying autonomous agent in a 3D virtual world. The agent uses visual cues, and its sensory and motor components are based on biological principles found in flies. A simple neural network is used for coupling the receptor and effector systems of the agent. In order to achieve appropriate reactions to sensory input, the connection weights are adjusted by a genetic algorithm under a closed loop action-perception condition.