JACIII Vol.21 No.2 pp. 189-196
doi: 10.20965/jaciii.2017.p0189


Utilization of the Physicomimetics Framework for Achieving Local, Decentralized, and Emergent Behavior in a Swarm of Quadrotor Unmanned Aerial Vehicles (QUAV)

Reiichiro Christian S. Nakano*, Ryan Rhay P. Vicerra**, Laurence A. Gan Lim*, Edwin Sybingco*, Elmer P. Dadios*, and Argel A. Bandala*

*De La Salle University
2401 Taft Avenue, Manila 1004, Philippines
**Electronics Engineering Department, University of Santo Tomas
Roque Ruaño Building, Ruaño Drive, UST, Sampaloc, Manila, Philippines

June 16, 2016
September 23, 2016
Online released:
March 15, 2017
March 20, 2017
swarm, physicomimetics, quadrotors, aggregation, swarming behavior

This paper presents the implementation of the physicomimetics framework in governing the behavior of a swarm of quadrotors. Each quadrotor uses only local information about itself and the neighboring quadrotors to determine its own movement by applying the principles of physicomimetics. Through these localized and relatively simple interactions, the swarm of quadrotors was able to organize itself into various structures and exhibit different swarm behaviors such as aggregation, obstacle avoidance, lattice formation, and dispersion.

  1. [1] I. Maza and A. Ollero, “Multiple UAV Cooperative Searching Operation using Polygon Area Decomposition and Efficient Coverage Algorithms,” Distributed Autonomous Robotic Systems, Vol.VI, pp. 221-230, 2007.
  2. [2] A. A. Bandala, R. R. P. Vicerra, L. Gan Lim, and E. P. Dadios, “Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors – Swarm Behavior for Aggregation, Foraging, Formation, and Tracking –,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.18, No.5, pp. 745-751, 2014.
  3. [3] P. Doherty, P. Haslum, F. Heintz, T. Merz, P. Nyblom, T. Persson, and B. Wingman, “A Distributed Architecture for Autonomous Unmanned Aerial Vehicle Experimentation,” Distributed Autonomous Robotic Systems, Vol.VI, pp. 233-242, 2007.
  4. [4] T. S. Kim, K. Stol, and V. Kecman, “Control of 3 DOF Quadrotor Model,” Robot Motion and Control, Springer, pp. 29-38, 2007.
  5. [5] T. Bajd, M. M. J. Lenarcic, A. Stanovnik, and M. Munih, “Robotics,” Springer, 2010.
  6. [6] T. Braunl, “Embedded Robotics,” Springer, 2006.
  7. [7] A. Gasparri, A. Priolo, and G. Ulivi, “A swarm aggregation algorithm for multi-robot systems based on local interaction,” IEEE Int. Conf. on Control Applications, 2012.
  8. [8] Y. Altshuler, A. Bruckstein, and I. Wagner, “Cooperative Cleaners: A Study in Ant Robotics,” The Int. J. of Robotics Research, Vol.XXVII, No.1, pp. 127-151, 2008.
  9. [9] D. Payton, R. Estkowski, and M. Howard, “Pheromone Robotics and the Logic of Virtual Pheromones,” Swarm Robotics, Springer, pp. 45-57, 2004.
  10. [10] J. Pestana, J. Sanchez-Lopez, P. de la Puente, and A. Carrio, “A Vision-based quadrotor swarm for the participation in the 2013 Int. Micro Air Vehicle Competition,” Unmanned Aircraft Systems (ICUAS), pp. 617-622, 2014.
  11. [11] G. Beni, “From Swarm Intelligence to Swarm Robotics,” Swarm Robotics, Springer, pp. 1-9, 2004.
  12. [12] G. Beni, “Order by Disordered Action in Swarms,” Swarm Robotics, Springer, pp. 153-171, 2004.
  13. [13] F. Fahini, “Autonomous Robots: Modeling, Path Planning, and Control,” Springer, 2009.
  14. [14] M. Beekman, G. Sword, and S. Simpson, “Biological Foundations of Swarm Intelligence,” Swarm Intelligence Introduction and Applications, Springer-Verlag, pp. 3-42, 2008.
  15. [15] A. Gasparri, G. Oriolo, A. Priolo, and G. Ulivi, “A Swarm Aggregation Algorithm based on Local Interaction for Multi-Robot Systems with Actuator Saturations,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2012.
  16. [16] W. Spears and D. Spears, “Physicomimetics: Physics-Based Swarm Intelligence,” Springer, pp. 84-97, 2012.
  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] A. A. Bandala and E. P. Dadios, “Dynamic Aggregation Method for Target Enclosure Using Smoothed Particle Hydrodynamics Technique – An Implementation in Quadrotor Unmanned Aerial Vehicles (QUAV) Swarm –,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.20, No.1, pp. 84-91, 2016.
  19. [19] J. Lennard-Jones, “On the Determination of Molecular Fields,” 1924.
  20. [20] A. A. Bandala, R. R. P. Vicerra, and E. P. Dadios, “Adaptive aggregation algorithm for target enclosure implemented in quadrotor unmanned aerial vehicle (QUAV) swarm,” Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014.
  21. [21] G. Faelden, J. Maningo, R. Nakano, A. Bandala, and E. Dadios, “Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm,” Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 24, 2017