Nothing Special   »   [go: up one dir, main page]
Top > JACIII > Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotor ...

single-jc.php

JACIII Vol.18 No.5 pp. 745-751
doi: 10.20965/jaciii.2014.p0745
(2014)

Paper:

Views over last 60 days: 1,367

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

Argel A. Bandala, Elmer P. Dadios, Ryan Rhay P. Vicerra,
and Laurence A. Gan Lim

De La Salle University, Manila, 2401 Taft Avenue, Manila 1004, Philippines

Received:
February 5, 2014
Accepted:
May 3, 2014
Published:
September 20, 2014
Creative Commons License
Keywords:
swarm robotics, swarm intelligence, social behaviors, unmanned aerial vehicles
Abstract
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.
Cite this article as:
A. Bandala, E. Dadios, R. Vicerra, and L. Lim, “Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors – Swarm Behavior for Aggregation, Foraging, Formation, and Tracking –,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.5, pp. 745-751, 2014.
Data files:
References
  1. [1] I. Navarro and F.Matia, “An Introuction to Swarm Robotics,” ISRN Robotics, pp. 1-10, 2013.
  2. [2] E. Sahin, “Swarm Robotics: From Sources of Inspiration to Domains of Application,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 10-20, 2004.
  3. [3] V. Gazi and K.M. Passino, “Swarm Coordination and Control Problems,” Swarm Stability and Optimization, Chapter 2, pp. 15-25, Springer, 2011.
  4. [4] G. Beni, “Order by Disordered Action in Swarms,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 153-171, 2004.
  5. [5] A. F. T. Winfield, C. J. Harper, and J. Nembrini, “Towards Dependable Swarms and a New Discipline of Swarm Engineering,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 126-142, 2004.
  6. [6] D. Payton, R. Estkowski, and M. Howard, “Pheromone Robotics and the Logic of Virtual Pheromones,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 45-57, 2004.
  7. [7] J. A. Rothermich, M. I. Ecemis, and P. Gaudiano, “Distributed Localization and Mapping with a Robotic Swarm,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 58-69, 2004.
  8. [8] S. Lacroix and G. L. Besnerais, “Issues in Cooperative Air/Ground Robotic Systems,” Roboics Research, Vol.14, pp. 421-432, Springer, 2010.
  9. [9] T. Balch, “Communication, Diversity and Learning: Cornerstones of Swarm Behavior,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 21-30, 2004.
  10. [10] R. R. McCune and G. R. Madey, “Swarm Control of UAVs for Cooperative Hunting with DDDAS,” Procedia Computer Science, Vol.18, pp. 2537-2544, 2013.
  11. [11] O. Soysal and E. Sahin, “A Macroscopic Model for Self-organized Aggregation in Swarm Robotic Systems,” Swarm Robotics, pp. 27-42, 2006.
  12. [12] Y. Altshuler, A. Bruckstein, and I. Wagner, “Cooperative Cleaners: A Study in Ant Robotics,” The Int. J. of Robotics Research, Vol.27, No.1, pp. 127-151, 2008.
  13. [13] H. Hamann and H.Worn, “An Analytical and Spacial Model of Foraging in a Swarm Robots,” Swarm Robotics, pp. 43-55, 2006.
  14. [14] N. Ayanian, V. Kumar, and D. Koditschek, “Synthesis of Controllers to Create, Maintain, and Reconfigure Robot Formations with Communication Constraints,” Robotics Research Springer Tracts in Advance Robotics, Vol.70, pp. 625-642, 2011.
  15. [15] R. D. Nardi and O. Holland, “UltraSwarm: A Further Step Towards a Flock of Miniature Helicopters,” Swarm Robotics, pp. 116-128, 2006.
  16. [16] I. Maza and A. Ollero, “Multiple UAV Cooperative Searching Operation using Polygon Area Decomposition and Efficient Coverage Algorithms,” Distributed Autonomous Robotic Systems, Vol.6, pp. 221-230, 2007.
  17. [17] W. M. Spears, D. F. Spears, R. Heil, W. Kerr, and S. Hettiarachchi, “An Overview of Physicomimetics,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 84-97, 2004.
  18. [18] K. Lerman, A. Martinoli, and A. Galstyan, “A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 143-152, 2004.
  19. [19] G. Beni, “From Swarm Intelligence to Swarm Robotics,” Swarm Robotics: Sab 2004 Int. Workshop, pp. 1-9, 2004.
  20. [20] P. Doherty, et al., “A Distributed Architecture for Autonomous Unmanned Aerial Vehicle Experimentation,” Distributed Autonomous Robotic Systems, Vol.6, pp. 233-242, 2007.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Nov. 19, 2024