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Description
  • When mobile robots are used among people, the best accepted motion related behavior is a human-like motion of the robot. Such behavior is difficult to obtain with commonly used finite state machine based planners, but can easily be evoked when human controls the robot. The paper presents the way of transforming such knowledge from human controller to reactive planner in the robot navigation module. Reactive planner is based on machine learning, neural networks in particular. The planner consists of two separate neural networks, one serving as predictor of dynamic obstacles behavior, second one serving as the reactive planner itself, producing desirable actions of the robot both in terms of velocity and direction. Planner was verified on real robot producing human-like behavior when used in real environment.
  • When mobile robots are used among people, the best accepted motion related behavior is a human-like motion of the robot. Such behavior is difficult to obtain with commonly used finite state machine based planners, but can easily be evoked when human controls the robot. The paper presents the way of transforming such knowledge from human controller to reactive planner in the robot navigation module. Reactive planner is based on machine learning, neural networks in particular. The planner consists of two separate neural networks, one serving as predictor of dynamic obstacles behavior, second one serving as the reactive planner itself, producing desirable actions of the robot both in terms of velocity and direction. Planner was verified on real robot producing human-like behavior when used in real environment. (en)
Title
  • Neural Network Based Reactive Navigation for Mobile Robot in Dynamic Environment
  • Neural Network Based Reactive Navigation for Mobile Robot in Dynamic Environment (en)
skos:prefLabel
  • Neural Network Based Reactive Navigation for Mobile Robot in Dynamic Environment
  • Neural Network Based Reactive Navigation for Mobile Robot in Dynamic Environment (en)
skos:notation
  • RIV/00216305:26210/13:PU106668!RIV14-MSM-26210___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • I, S, Z(AV0Z20760514)
http://linked.open...iv/cisloPeriodika
  • 198
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 91444
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26210/13:PU106668
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • mobile robot, reactive navigation, artificial neural networks (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • CH - Švýcarská konfederace
http://linked.open...ontrolniKodProRIV
  • [A233930CC26B]
http://linked.open...i/riv/nazevZdroje
  • Solid State Phenomena
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 2013
http://linked.open...iv/tvurceVysledku
  • Krejsa, Jiří
  • Ripel, Tomáš
  • Věchet, Stanislav
http://linked.open...n/vavai/riv/zamer
issn
  • 1012-0394
number of pages
http://localhost/t...ganizacniJednotka
  • 26210
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