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 arc 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.
CITATION STYLE
Neumann, T. R., Huber, S. A., & Bülthoff, H. H. (1997). Minimalistic approach to 3D obstacle avoidance behavior from simulated evolution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1327, pp. 715–720). Springer Verlag. https://doi.org/10.1007/bfb0020238
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