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[klicova_slova] => Markov chain, Markov analysis Optimising, Theory, Finite state first order Markov Chain
[klicova_slova_orig] => Markov chain, Markov analysis Optimising, Theory, Finite state first order Markov Chain
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[nazev] => Plánování pohybu skupiny robotů pomocí flocking algoritmu a Voroného diagramů
[nazev_orig] => Plánování pohybu skupiny robotů pomocí flocking algoritmu a Voroného diagramů
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[popis] => Článek prezentuje techniky pro řízení a plánování pohybu skupiny robotů mezi dvěma danými místy v prostředí s překážkami. Tyto techniky jsou založeny na „flocking“ algoritmu simulující přirozený život, ve kterém mohou být simulovány houfující se či rojící se zvířata jako např. roje včel nebo hejna ryb, ale s menším ohledem na realitu jejich pohybu. Pro nalezení nejkratší cesty je použit A* algoritmus ve spojení s Voroného diagramem, který je jednou ze základních datových struktur ve výpočetní geometrii. Nejkratší cesta tvoří základ pro vytvoření koridoru umožňující přesun skupiny robotů.
Jednoduchá pravidla pro simulaci shromažďujícího chování roje – řídícího chování – jsou separace, koheze a zarovnání. Hlavním cílem skupiny robotů je dosáhnutí co nejrychleji cíle při udržování koheze skupiny a zajištění toho, že skupina má největší možnou vzdálenost od okolních překážek. Používáme kombinaci řídících chování k zajištění bezkolizního pohybu skupiny robotů uvnitř koridoru, která je postaven podél nejkratší cesty. Roboti nacházející se v přední části skupiny vedou skupinu a počítají nejkratší cestu zatímco zbytek skupiny je následuje (Reynolds, 1999).
[popis_orig] => Článek prezentuje techniky pro řízení a plánování pohybu skupiny robotů mezi dvěma danými místy v prostředí s překážkami. Tyto techniky jsou založeny na „flocking“ algoritmu simulující přirozený život, ve kterém mohou být simulovány houfující se či rojící se zvířata jako např. roje včel nebo hejna ryb, ale s menším ohledem na realitu jejich pohybu. Pro nalezení nejkratší cesty je použit A* algoritmus ve spojení s Voroného diagramem, který je jednou ze základních datových struktur ve výpočetní geometrii. Nejkratší cesta tvoří základ pro vytvoření koridoru umožňující přesun skupiny robotů.
Jednoduchá pravidla pro simulaci shromažďujícího chování roje – řídícího chování – jsou separace, koheze a zarovnání. Hlavním cílem skupiny robotů je dosáhnutí co nejrychleji cíle při udržování koheze skupiny a zajištění toho, že skupina má největší možnou vzdálenost od okolních překážek. Používáme kombinaci řídících chování k zajištění bezkolizního pohybu skupiny robotů uvnitř koridoru, která je postaven podél nejkratší cesty. Roboti nacházející se v přední části skupiny vedou skupinu a počítají nejkratší cestu zatímco zbytek skupiny je následuje (Reynolds, 1999).
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Simple rules for simulating the flocking behaviour of swarm – steering behaviours – are separation, cohesion, and alignment. The main goal of the swarm of robots is to reach a destination as fast as possible, while strictly maintaining the cohesion of the swarm and to assure that the swarm has the biggest distance from obstacles as possible. We use a combination of steering behaviours to produce a collision–free motion of the swarm of robots within a corridor that is built along the shortest path. Robots at front of the swarm lead the swarm, which means that they run path finding algorithm, while the rest of swarm follow the leaders (Reynolds, 1999).
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[citace_text] => ŠVEC, P. Motion Planning of a Swarm of Robots Using Flocking Algorithm and Voronoi Diagram. In Proceedings of the 39th Spring International Conference Modelling and Simulation of Systems MOSIS '05. Hradec nad Moravicí: MARQ Ostrava, 2005. 7 p. ISBN: 80-248-0774-2.
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author="Petr {Švec}",
title="A Construction of the 2D Generalized Voronoi Diagram, Part II: Some Issues of Implementation of Fortune’s Plane Sweep Algorithm",
booktitle="Proceedings of the 12th International Conference on Soft Computing MENDEL 2006",
year="2006",
volume="2006",
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address="Brno",
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[nazev_en] => A Construction of the 2D Generalized Voronoi Diagram, Part II: Some Issues of Implementation of Fortune’s Plane Sweep Algorithm
[popis_en] => This paper clearly describes one of the possible constructions of the Voronoi diagram for n point generators using Fortune’s plane sweep algorithm computing in O(n log n) worst-time complexity. This paper aims to aid optimal implementation with thorough documentation, providing a guide through the several possibilities of implementation to choose from, and includes some techniques for better practical realization.
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title="GATE/Matlab - implementation of soft-computing algorithms",
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publisher="VUT Brno",
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Publikace Rok: 2005
ŠVEC, P.
Publikace Rok: 2005
ŠVEC, P.
Publikace Rok: 2006
ŠVEC, P.
Zobrazeny výsledky 766–780 z 2086