Obstacle Avoidance for Drones Based on the Self-Organizing Migrating Algorithm
The result's identifiers
Result code in 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>
Alternative codes found
RIV/61989100:27740/20:10247261
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Obstacle Avoidance for Drones Based on the Self-Organizing Migrating Algorithm
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
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Number of pages
11
Pages from-to
376-386
Publisher name
Springer
Place of publication
Cham
Event location
Zakopané
Event date
Oct 12, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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