This HTML5 document contains 44 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n5http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n16http://purl.org/net/nknouf/ns/bibtex#
n8http://localhost/temp/predkladatel/
n15http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n6http://linked.opendata.cz/resource/domain/vavai/projekt/
n7http://linked.opendata.cz/ontology/domain/vavai/
n21https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n17http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n10http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n14http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n12http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n18http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n19http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21230%2F14%3A00224921%21RIV15-MSM-21230___/
n11http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n20http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00224921%21RIV15-MSM-21230___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
This paper presents our early results on multi-goal trajectory planning with motion primitives for a hexapod walking robot. We propose to use an on-line unsupervised learning method to simultaneously find a solution of the underlying traveling salesman problem together with particular trajectories between the goals. Using this technique, we avoid pre-computation of all possible trajectories between the goals for a graph based heuristic solvers for the traveling salesman problem. The proposed approach utilizes principles of self-organizing map to steer the randomized sampling of configuration space in promising areas regarding the multi-goal trajectory. The presented results indicate the proposed steering mechanism provides a feasible multi-goal trajectory in a less number of samples than an approach based on a priori known sequence of the goals visits. This paper presents our early results on multi-goal trajectory planning with motion primitives for a hexapod walking robot. We propose to use an on-line unsupervised learning method to simultaneously find a solution of the underlying traveling salesman problem together with particular trajectories between the goals. Using this technique, we avoid pre-computation of all possible trajectories between the goals for a graph based heuristic solvers for the traveling salesman problem. The proposed approach utilizes principles of self-organizing map to steer the randomized sampling of configuration space in promising areas regarding the multi-goal trajectory. The presented results indicate the proposed steering mechanism provides a feasible multi-goal trajectory in a less number of samples than an approach based on a priori known sequence of the goals visits.
dcterms:title
Multi-Goal Trajectory Planning with Motion Primitives for Hexapod Walking Robot Multi-Goal Trajectory Planning with Motion Primitives for Hexapod Walking Robot
skos:prefLabel
Multi-Goal Trajectory Planning with Motion Primitives for Hexapod Walking Robot Multi-Goal Trajectory Planning with Motion Primitives for Hexapod Walking Robot
skos:notation
RIV/68407700:21230/14:00224921!RIV15-MSM-21230___
n4:aktivita
n12:S n12:P
n4:aktivity
P(GP13-18316P), S
n4:dodaniDat
n20:2015
n4:domaciTvurceVysledku
n15:4111303 n15:1695541 n15:7341520
n4:druhVysledku
n18:D
n4:duvernostUdaju
n10:S
n4:entitaPredkladatele
n19:predkladatel
n4:idSjednocenehoVysledku
31000
n4:idVysledku
RIV/68407700:21230/14:00224921
n4:jazykVysledku
n14:eng
n4:klicovaSlova
motion planning; traveling salesman problem; unsupervised learning
n4:klicoveSlovo
n17:traveling%20salesman%20problem n17:unsupervised%20learning n17:motion%20planning
n4:kontrolniKodProRIV
[0826AEE41019]
n4:mistoKonaniAkce
Vienna
n4:mistoVydani
Porto
n4:nazevZdroje
Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics
n4:obor
n11:JD
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n6:GP13-18316P
n4:rokUplatneniVysledku
n20:2014
n4:tvurceVysledku
Vaněk, Petr Faigl, Jan Masri, Diar
n4:typAkce
n5:WRD
n4:zahajeniAkce
2014-09-01+02:00
s:numberOfPages
6
n16:hasPublisher
SciTePress - Science and Technology Publications
n21:isbn
978-989-758-040-6
n8:organizacniJednotka
21230