Journal of Applied Mathematics

An Improved Fast Flocking Algorithm with Obstacle Avoidance for Multiagent Dynamic Systems

Jialiang Wang, Hai Zhao, Yuanguo Bi, Shiliang Shao, Qian Liu, Xingchi Chen, Ruofan Zeng, Yu Wang, and Le Ha

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

Flocking behavior is a common phenomenon in nature, such as flocks of birds and groups of fish. In order to make the agents effectively avoid obstacles and fast form flocking towards the direction of destination point, this paper proposes a fast multiagent obstacle avoidance (FMOA) algorithm. FMOA is illustrated based on the status of whether the flocking has formed. If flocking has not formed, agents should avoid the obstacles toward the direction of target. If otherwise, these agents have reached the state of lattice and then these agents only need to avoid the obstacles and ignore the direction of target. The experimental results show that the proposed FMOA algorithm has better performance in terms of flocking path length. Furthermore, the proposed FMOA algorithm is applied to the formation flying of quad-rotor helicopters. Compared with other technologies to perform the localization of quad-rotor helicopter, this paper innovatively constructs a smart environment by deploying some wireless sensor network (WSN) nodes using the proposed localization algorithm. Finally, the proposed FMOA algorithm is used to conduct the formation flying of these quad-rotor helicopters in the smart environment.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 659805, 13 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305850

Digital Object Identifier
doi:10.1155/2014/659805

Citation

Wang, Jialiang; Zhao, Hai; Bi, Yuanguo; Shao, Shiliang; Liu, Qian; Chen, Xingchi; Zeng, Ruofan; Wang, Yu; Ha, Le. An Improved Fast Flocking Algorithm with Obstacle Avoidance for Multiagent Dynamic Systems. J. Appl. Math. 2014 (2014), Article ID 659805, 13 pages. doi:10.1155/2014/659805. https://projecteuclid.org/euclid.jam/1425305850


Export citation

References

  • http://en.wikipedia.org/wiki/Flocking_(behavior).
  • C. W. Reynolds, “Flocks, herds, and schools: a distributed behavioral model,” ACM SIGGRAPH Computer, vol. 21, no. 4, pp. 25–34, 1987.
  • C. W. Reynolds, “Steering behaviors for autonomous characters,” in Proceedings of the of Game Developers Conference, pp. 763–782, San Francisco, Calif, USA, 1999.
  • C. W. Reynolds, “Interaction with a group of autonomous characters,” in Proceedings of the of Game Developers Conference, pp. 449–460, San Francisco, Calif, USA, 2000.
  • J. Toner and Y. Tu, “Flocks, herds, and schools: a quantitative theory of flocking,” Physical Review E, vol. 58, no. 4, pp. 4828–4858, 1998.
  • N. Shimoyama, K. Sugawara, T. Mizuguchi, Y. Hayakawa, and M. Sano, “Collective motion in a system of motile elements,” Physical Review Letters, vol. 76, no. 20, pp. 3870–3873, 1996.
  • A. Mogilner and L. Edelstein-Keshet, “Spatio-angular order in populations of self-aligning objects: formation of oriented patches,” Physica D: Nonlinear Phenomena, vol. 89, no. 3-4, pp. 346–367, 1996.
  • A. Mogilner and L. Edelstein-Keshet, “A non-local model for a swarm,” Journal of Mathematical Biology, vol. 38, no. 6, pp. 534–570, 1999.
  • A. Jadbabaie, J. Lin, and A. S. Morse, “Coordination of groups of mobile autonomous agents using nearest neighbor rules,” IEEE Transactions on Automatic Control, vol. 48, no. 6, pp. 988–1001, 2003.
  • J. M. E. Gabbai, Complexity and the aerospace industry: understanding emergence by relating structure to performance using multi-agent systems [Ph.D. thesis], University of Manchester, Manchester, UK, 2005.
  • J. Ibáñez, A. F. Gómez-Skarmeta, and J. Blat, “DJ-Boids: emergent collective behavior as multichannel radio station programming,” in Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 248–250, January 2003.
  • A. V. Moere, “Time-varying data visualization using information flocking boids,” in Proceedings of the IEEE Symposium on Information Visualization (INFO VIS '04), pp. 97–104, October 2004.
  • Z. Cui and Z. Shi, “Boid particle swarm optimisation,” International Journal of Innovative Computing and Applications, vol. 2, no. 2, pp. 78–85, 2009.
  • R. Olfati-Saber, “Flocking for multi-agent dynamic systems: algorithms and theory,” IEEE Transactions on Automatic Control, vol. 51, no. 3, pp. 401–420, 2006.
  • A. Bry, A. Bachrach, and N. Roy, “State estimation for aggressive flight in GPS-denied environments using onboard sensing,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '12), pp. 1–8, St Paul, Minn, USA, 2012.
  • D. B. Jourdan, J. J. Deyst Jr., M. Z. Win, and N. Roy, “Monte Carlo localization in dense multipath environments using UWB ranging,” in Proceedings of the IEEE International Conference on Ultra-Wideband (ICU '05), pp. 314–319, September 2005.
  • R. He, A. Bachrach, M. Achtelik et al., “On the design and use of a micro air vehicle to track and avoid adversaries,” International Journal of Robotics Research, vol. 29, no. 5, pp. 529–546, 2010.
  • J. Wang, H. Zhao, J. Xu, and Y. Bi, “Webit&NEU: an embedded device for the Internet of things,” International Journal of Distributed Sensor Networks, vol. 2014, Article ID 839540, 10 pages, 2014.
  • W. Sun, Z. Zhao, and H. Gao, “Saturated adaptive robust control for active suspension systems,” IEEE Transactions on Industrial Electronics, vol. 60, no. 9, pp. 3889–3896, 2013.
  • W. Sun, H. Gao, and B. Yao, “Adaptive robust vibration control of full-car active suspensions with electrohydraulic actuators,” IEEE Transactions on Control Systems Technology, vol. 21, no. 6, pp. 2417–2422, 2013.
  • W. Sun, H. Gao Sr., and O. Kaynak, “Finite frequency ${H}_{\infty }$ control for vehicle active suspension systems,” IEEE Transactions on Control Systems Technology, vol. 19, no. 2, pp. 416–422, 2011.
  • W. Sun, Y. Zhao, J. Li, L. Zhang, and H. Gao, “Active suspension control with frequency band constraints and actuator input delay,” IEEE Transactions on Industrial Electronics, vol. 59, no. 1, pp. 530–537, 2012.
  • W. Sun, H. Gao, and O. Kaynak, “Adaptive backstepping control for active suspension systems with hard constraints,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 3, pp. 1072–1079, 2013.
  • W. Sun, J. Li, Y. Zhao, and H. Gao, “Vibration control for active seat suspension systems via dynamic output feedback with limited frequency characteristic,” Mechatronics, vol. 21, no. 1, pp. 250–260, 2011.
  • W. Sun, H. Gao, and O. Kaynak, “Vibration isolation for active suspensions with performance constraints and actuator saturation,” IEEE/ASME Transactions on Mechatronics, 2014.
  • W. Sun, H. Pan, Y. Zhang, and H. Gao, “Multi-objective control for uncertain nonlinear active suspension systems,” Mechatronics, vol. 24, no. 4, pp. 318–327, 2014.
  • J. Wang, H. Zhao, Y. Bi, X. Chen, R. Zeng, and Y. Wang, “An improved task scheduling algorithm for intelligent control in tiny mechanical system,” Mathematical Problems in Engineering, vol. 2014, Article ID 307869, 8 pages, 2014.
  • C. Delgado-Mata, J. Ibanez Martinez, S. Bee, R. Ruiz-Rodarte, and R. Aylett, “On the use of virtual animals with artificial fear in virtual environments,” New Generation Computing, vol. 25, no. 2, pp. 145–169, 2007. \endinput