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This paper presents the fusion of swarm behavior inmulti robotic system specically the quadrotors un-manned aerial vehicle (QUAV) operations. This studydirected on using robot swarms because of its key fea-ture of decentralized processing amongst its member.This characteristicleadstoadvantagesofrobotopera-tionsbecauseanindividual robotfailurewill notaffectthe group performance. The algorithm emulating theanimal or insect swarm behaviors is presented in thispaper and implemented into anarticial roboticagent(QUAV) in computer simulations. The simulation re-sults concluded that for increasing number of QUAVthe aggregation accuracy increases with an accuracyof 90.62%. The experiment for foraging revealed thatthe number of QUAV does not affect the accuracy ofthe swarm instead the iterations needed are greatlyimproved with an averageof 160.53iterations from 50to 500 QUAV. For swarm tracking, the average accu-racy is 89.23%. The accuracy of the swarm forma-tion is 84.65%. These results clearly dened that theswarmsystemis accurateenoughto performthe tasksand robust in any QUAV number.Keywords: swarm robotics, swarm intelligence, socialbehaviors, unmannedaerial vehicles

Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors – Swarm Behavior for Aggregation, Foraging, Formation, and Tracking –