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.
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© 1997 Springer-Verlag Berlin Heidelberg
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Neumann, T.R., Huber, S.A., Bülthof, H.H. (1997). Minimalistic approach to 3D obstacle avoidance behavior from simulated evolution. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020238
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DOI: https://doi.org/10.1007/BFb0020238
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