PHuNAC Model: Emergence of Crowd’s Swarm Behavior

2014 ◽  
pp. 9-18
Author(s):  
Olfa Beltaief ◽  
Sameh El Hadouaj ◽  
Khaled Ghedira
Keyword(s):  
Swarm Behavior

Autonomous UAV Swarm: Behavior Generation and Simulation

2018 ◽  
Author(s):  
Ju Wang ◽  
Yong Tang ◽  
Jonathan Kavalen ◽  
Ahmed F. Abdelzaher ◽  
Sagar P. Pandit
Keyword(s):  
Swarm Behavior ◽  
Uav Swarm

scholarly journals Swarm Signal Temporal Logic Inference for Swarm Behavior Analysis

2019 ◽  
Vol 4 (3) ◽  
pp. 3021-3028 ◽  
Author(s):  
Ruixuan Yan ◽  
Zhe Xu ◽  
Agung Julius
Keyword(s):  
Behavior Analysis ◽  
Temporal Logic ◽  
Swarm Behavior ◽  
Logic Inference

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

2014 ◽  
Vol 18 (5) ◽  
pp. 745-751 ◽  
Author(s):  
Argel A. Bandala ◽  
◽  
Elmer P. Dadios ◽  
Ryan Rhay P. Vicerra ◽  
Laurence A. Gan Lim
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Computer Simulations ◽  
Group Performance ◽  
Robotic System ◽  
Robot Swarms ◽  
Average Accuracy ◽  
Swarm Behavior ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Robotic Agent

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.

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