Obstacle Avoidance for Drones Based on the Self-Organizing Migrating Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10247261" target="_blank" >RIV/61989100:27240/20:10247261 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/20:10247261

Výsledek na webu

<a href="https://link.springer.com/content/pdf/10.1007%2F978-3-030-61401-0_35.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007%2F978-3-030-61401-0_35.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-61401-0_35" target="_blank" >10.1007/978-3-030-61401-0_35</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Obstacle Avoidance for Drones Based on the Self-Organizing Migrating Algorithm

Popis výsledku v původním jazyce

The paper proposes a method for the drone to catch the given target and avoid detected obstacles in its path based on the self-organizing migrating algorithm. In particular, a two-component fitness function is proposed based on the principle that the closer the target, the lower the fitness value, and the closer the obstacle, the higher the fitness value. Self-organizing migrating algorithm, a swarm intelligence algorithm, is used to predict the next positions that the drone will move to. These positions both satisfy the requirement to avoid obstacles and shorten the distance to the target. A map of two drones, two corresponding targets and four static obstacles was modeled on Matlab. The simulation results verify the correctness and effectiveness of the proposed method. (C) 2020, Springer Nature Switzerland AG.

Název v anglickém jazyce

Obstacle Avoidance for Drones Based on the Self-Organizing Migrating Algorithm

Popis výsledku anglicky

The paper proposes a method for the drone to catch the given target and avoid detected obstacles in its path based on the self-organizing migrating algorithm. In particular, a two-component fitness function is proposed based on the principle that the closer the target, the lower the fitness value, and the closer the obstacle, the higher the fitness value. Self-organizing migrating algorithm, a swarm intelligence algorithm, is used to predict the next positions that the drone will move to. These positions both satisfy the requirement to avoid obstacles and shorten the distance to the target. A map of two drones, two corresponding targets and four static obstacles was modeled on Matlab. The simulation results verify the correctness and effectiveness of the proposed method. (C) 2020, Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 12415

ISBN

978-3-030-61400-3

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

11

Strana od-do

376-386

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Zakopané

Datum konání akce

12. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—