Skip to Main content Skip to Navigation
Journal articles

Neural Nets trained by genetic algorithms for collision avoidance

Abstract : As air traffic keeps increasing, many research programs focus on collision avoidance techniques. For short or medium term avoidance, new headings have to be computed almost on the spot, and feed forward neural nets are susceptible to find solutions in a much shorter amount of time than classical avoidance algorithms (A_, stochastic optimization, etc.) In this article, we show that a neural network can be built with unsupervised learning to compute nearly optimal trajectories to solve two aircraft conflicts with the highest reliability, while computing headings in a few milliseconds.
Document type :
Journal articles
Complete list of metadata

Cited literature [17 references]  Display  Hide  Download
Contributor : Laurence Porte Connect in order to contact the contributor
Submitted on : Tuesday, April 29, 2014 - 5:12:39 PM
Last modification on : Tuesday, October 19, 2021 - 11:02:49 AM
Long-term archiving on: : Tuesday, July 29, 2014 - 10:45:20 AM


Files produced by the author(s)




Nicolas Durand, Jean-Marc Alliot, Frédéric Medioni. Neural Nets trained by genetic algorithms for collision avoidance. Applied Intelligence, Springer Verlag (Germany), 2000, 13 (3), pp 205-213. ⟨10.1023/A:1026507809196⟩. ⟨hal-00934535⟩



Les métriques sont temporairement indisponibles