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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F09%3A00158896%21RIV10-MSM-21230___
rdf:type
n18:Vysledek skos:Concept
dcterms:description
In this paper we describe simulation of autonomous robots controlled by recurrent neural networks, which are evolved through indirect encoding using HyperNEAT algorithm. The robots utilize 180 degree wide sensor array. Thanks to the scalability of the neural network generated by HyperNEAT, the sensor array can have various resolution. This would allow to use camera as an input for neural network controller used in real robot. The robots were simulated using software simulation environment. In the experiments the robots were trained to drive with imaximum average speed. Such fitness forces them to learn how to drive on roads and avoid collisions. Evolved neural networks show excellent scalability. Scaling of the sensory input breaks performance of the robots, which should be gained back with re-training of the robot with a different sensory input resolution. In this paper we describe simulation of autonomous robots controlled by recurrent neural networks, which are evolved through indirect encoding using HyperNEAT algorithm. The robots utilize 180 degree wide sensor array. Thanks to the scalability of the neural network generated by HyperNEAT, the sensor array can have various resolution. This would allow to use camera as an input for neural network controller used in real robot. The robots were simulated using software simulation environment. In the experiments the robots were trained to drive with imaximum average speed. Such fitness forces them to learn how to drive on roads and avoid collisions. Evolved neural networks show excellent scalability. Scaling of the sensory input breaks performance of the robots, which should be gained back with re-training of the robot with a different sensory input resolution.
dcterms:title
HyperNEAT Controlled Robots Learn How to Drive on Roads in Simulated Environment HyperNEAT Controlled Robots Learn How to Drive on Roads in Simulated Environment
skos:prefLabel
HyperNEAT Controlled Robots Learn How to Drive on Roads in Simulated Environment HyperNEAT Controlled Robots Learn How to Drive on Roads in Simulated Environment
skos:notation
RIV/68407700:21230/09:00158896!RIV10-MSM-21230___
n3:aktivita
n7:P n7:Z
n3:aktivity
P(KJB201210701), Z(MSM6840770012)
n3:dodaniDat
n5:2010
n3:domaciTvurceVysledku
n4:9121870 n4:7438907 n4:7035586
n3:druhVysledku
n12:D
n3:duvernostUdaju
n21:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
318215
n3:idVysledku
RIV/68407700:21230/09:00158896
n3:jazykVysledku
n13:eng
n3:klicovaSlova
HyperNEAT; neural networks; simulated robots
n3:klicoveSlovo
n16:neural%20networks n16:simulated%20robots n16:HyperNEAT
n3:kontrolniKodProRIV
[370AB7079EE2]
n3:mistoKonaniAkce
Trondheim
n3:mistoVydani
Singapore
n3:nazevZdroje
2009 IEEE Congress on Evolutionary Computation
n3:obor
n14:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n8:KJB201210701
n3:rokUplatneniVysledku
n5:2009
n3:tvurceVysledku
Šnorek, Miroslav Drchal, Jan Koutník, Jan
n3:typAkce
n17:WRD
n3:zahajeniAkce
2009-05-18+02:00
n3:zamer
n15:MSM6840770012
s:numberOfPages
6
n6:hasPublisher
Research Publishing Services
n11:isbn
978-1-4244-2959-2
n22:organizacniJednotka
21230