Implementation of swarm aggregation in quadrotor swarms using an artificial potential function model

Swarm robotics is one of the novel approaches being explored in multiple quadrotor. It aims to mimic social behaviors of animals and insects. This paper presents the physical implementation of the swarm behavior aggregation in a quadrotor swarm. It is implemented over a quadrotor swarm testbed that...

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Main Authors: Faelden, Gerard Ely, Maningo, Jose Martin, Nakano, Reiichiro Christian S., Bandala, Argel A., Vicerra, Ryan Rhay P., Dadios, Elmer P.
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Published: Animo Repository 2017
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/1496
https://animorepository.dlsu.edu.ph/context/faculty_research/article/2495/type/native/viewcontent
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-24952022-04-27T06:33:15Z Implementation of swarm aggregation in quadrotor swarms using an artificial potential function model Faelden, Gerard Ely Maningo, Jose Martin Nakano, Reiichiro Christian S. Bandala, Argel A. Vicerra, Ryan Rhay P. Dadios, Elmer P. Swarm robotics is one of the novel approaches being explored in multiple quadrotor. It aims to mimic social behaviors of animals and insects. This paper presents the physical implementation of the swarm behavior aggregation in a quadrotor swarm. It is implemented over a quadrotor swarm testbed that makes use of external motion capture cameras. The completed algorithm makes use of the artificial potential function model with a linear attraction and bounded repulsion. Results show successful demonstration of the aggregation algorithm with minimal error in position. It is tested for an increasing number of quadrotors and errors are seen to increase with swarm size. Results show an error of 3.293 cm from the individual target position for aggregation. It also shows and average aggregation speed of 1.896 secs for all test while having an increase in aggregation speed of about 1.772 sec per increase in swarm size. The time in aggregate is seen to be at an average of 98.5405% of the time. All the tests show successful demonstration of the swarming behavior which can now mark the start of development of implementation of more complex swarming behaviors. © 2016 IEEE. 2017-02-08T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/1496 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2495/type/native/viewcontent Faculty Research Work Animo Repository Swarm intelligence Quadrotor helicopters Electrical and Computer Engineering Electrical and Electronics Systems and Communications
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Swarm intelligence
Quadrotor helicopters
Electrical and Computer Engineering
Electrical and Electronics
Systems and Communications
spellingShingle Swarm intelligence
Quadrotor helicopters
Electrical and Computer Engineering
Electrical and Electronics
Systems and Communications
Faelden, Gerard Ely
Maningo, Jose Martin
Nakano, Reiichiro Christian S.
Bandala, Argel A.
Vicerra, Ryan Rhay P.
Dadios, Elmer P.
Implementation of swarm aggregation in quadrotor swarms using an artificial potential function model
description Swarm robotics is one of the novel approaches being explored in multiple quadrotor. It aims to mimic social behaviors of animals and insects. This paper presents the physical implementation of the swarm behavior aggregation in a quadrotor swarm. It is implemented over a quadrotor swarm testbed that makes use of external motion capture cameras. The completed algorithm makes use of the artificial potential function model with a linear attraction and bounded repulsion. Results show successful demonstration of the aggregation algorithm with minimal error in position. It is tested for an increasing number of quadrotors and errors are seen to increase with swarm size. Results show an error of 3.293 cm from the individual target position for aggregation. It also shows and average aggregation speed of 1.896 secs for all test while having an increase in aggregation speed of about 1.772 sec per increase in swarm size. The time in aggregate is seen to be at an average of 98.5405% of the time. All the tests show successful demonstration of the swarming behavior which can now mark the start of development of implementation of more complex swarming behaviors. © 2016 IEEE.
format text
author Faelden, Gerard Ely
Maningo, Jose Martin
Nakano, Reiichiro Christian S.
Bandala, Argel A.
Vicerra, Ryan Rhay P.
Dadios, Elmer P.
author_facet Faelden, Gerard Ely
Maningo, Jose Martin
Nakano, Reiichiro Christian S.
Bandala, Argel A.
Vicerra, Ryan Rhay P.
Dadios, Elmer P.
author_sort Faelden, Gerard Ely
title Implementation of swarm aggregation in quadrotor swarms using an artificial potential function model
title_short Implementation of swarm aggregation in quadrotor swarms using an artificial potential function model
title_full Implementation of swarm aggregation in quadrotor swarms using an artificial potential function model
title_fullStr Implementation of swarm aggregation in quadrotor swarms using an artificial potential function model
title_full_unstemmed Implementation of swarm aggregation in quadrotor swarms using an artificial potential function model
title_sort implementation of swarm aggregation in quadrotor swarms using an artificial potential function model
publisher Animo Repository
publishDate 2017
url https://animorepository.dlsu.edu.ph/faculty_research/1496
https://animorepository.dlsu.edu.ph/context/faculty_research/article/2495/type/native/viewcontent
_version_ 1731309285673009152