Swarming algorithm for unmanned aerial vehicle (UAV) quadrotors - Swarm behavior for aggregation, foraging, formation, and tracking

This paper presents the fusion of swarm behavior in multi robotic system specifically the quadrotors unmanned aerial vehicle (QUAV) operations. This study directed on using robot swarms because of its key feature of decentralized processing amongst its member. This characteristic leads to advantages...

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Main Authors: Bandala, Argel A., Dadios, Elmer P., Vicerra, Ryan Rhay P., Gan Lim, Laurence A.
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Published: Animo Repository 2014
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/335
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-13342021-12-10T03:33:44Z Swarming algorithm for unmanned aerial vehicle (UAV) quadrotors - Swarm behavior for aggregation, foraging, formation, and tracking Bandala, Argel A. Dadios, Elmer P. Vicerra, Ryan Rhay P. Gan Lim, Laurence A. This paper presents the fusion of swarm behavior in multi robotic system specifically the quadrotors unmanned aerial vehicle (QUAV) operations. This study directed on using robot swarms because of its key feature of decentralized processing amongst its member. This characteristic leads to advantages of robot operations because an individual robot failure will not affect the group performance. The algorithm emulating the animal or insect swarm behaviors is presented in this paper and implemented into an artificial robotic agent (QUAV) in computer simulations. The simulation results concluded that for increasing number of QUAV the aggregation accuracy increases with an accuracy of 90.62%. The experiment for foraging revealed that the number of QUAV does not affect the accuracy of the swarm instead the iterations needed are greatly improved with an average of 160.53 iterations from 50 to 500 QUAV. For swarm tracking, the average accuracy is 89.23%. The accuracy of the swarm formation is 84.65%. These results clearly defined that the swarm system is accurate enough to perform the tasks and robust in any QUAV number. © 2014, Fuji Technology Press. All rights reserved. 2014-09-01T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/335 Faculty Research Work Animo Repository Swarm intelligence Drone aircraft Electrical and Electronics Robotics
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
Drone aircraft
Electrical and Electronics
Robotics
spellingShingle Swarm intelligence
Drone aircraft
Electrical and Electronics
Robotics
Bandala, Argel A.
Dadios, Elmer P.
Vicerra, Ryan Rhay P.
Gan Lim, Laurence A.
Swarming algorithm for unmanned aerial vehicle (UAV) quadrotors - Swarm behavior for aggregation, foraging, formation, and tracking
description This paper presents the fusion of swarm behavior in multi robotic system specifically the quadrotors unmanned aerial vehicle (QUAV) operations. This study directed on using robot swarms because of its key feature of decentralized processing amongst its member. This characteristic leads to advantages of robot operations because an individual robot failure will not affect the group performance. The algorithm emulating the animal or insect swarm behaviors is presented in this paper and implemented into an artificial robotic agent (QUAV) in computer simulations. The simulation results concluded that for increasing number of QUAV the aggregation accuracy increases with an accuracy of 90.62%. The experiment for foraging revealed that the number of QUAV does not affect the accuracy of the swarm instead the iterations needed are greatly improved with an average of 160.53 iterations from 50 to 500 QUAV. For swarm tracking, the average accuracy is 89.23%. The accuracy of the swarm formation is 84.65%. These results clearly defined that the swarm system is accurate enough to perform the tasks and robust in any QUAV number. © 2014, Fuji Technology Press. All rights reserved.
format text
author Bandala, Argel A.
Dadios, Elmer P.
Vicerra, Ryan Rhay P.
Gan Lim, Laurence A.
author_facet Bandala, Argel A.
Dadios, Elmer P.
Vicerra, Ryan Rhay P.
Gan Lim, Laurence A.
author_sort Bandala, Argel A.
title Swarming algorithm for unmanned aerial vehicle (UAV) quadrotors - Swarm behavior for aggregation, foraging, formation, and tracking
title_short Swarming algorithm for unmanned aerial vehicle (UAV) quadrotors - Swarm behavior for aggregation, foraging, formation, and tracking
title_full Swarming algorithm for unmanned aerial vehicle (UAV) quadrotors - Swarm behavior for aggregation, foraging, formation, and tracking
title_fullStr Swarming algorithm for unmanned aerial vehicle (UAV) quadrotors - Swarm behavior for aggregation, foraging, formation, and tracking
title_full_unstemmed Swarming algorithm for unmanned aerial vehicle (UAV) quadrotors - Swarm behavior for aggregation, foraging, formation, and tracking
title_sort swarming algorithm for unmanned aerial vehicle (uav) quadrotors - swarm behavior for aggregation, foraging, formation, and tracking
publisher Animo Repository
publishDate 2014
url https://animorepository.dlsu.edu.ph/faculty_research/335
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