Advertisement

Swarm intelligence routing approach in networked robots

  • S. HoceiniEmail author
  • A. Mellouk
  • A. Chibani
  • Y. Touati
  • B. Augustin
Article

Abstract

Robot swarm combined with wireless communication has been a key driving force in recent few years and has currently expanded to wireless multihop networks, which include ad hoc radio networks, sensor networks, wireless mesh networks, etc. The aim of this paper is to propose an approach which introduces a polynomial time approximation path navigation algorithm and constructs dynamic state-dependent navigation policies. The proposed algorithm uses an inductive approach based on trial/error paradigm combined with swarm adaptive approaches to optimize simultaneously two criteria: cumulative cost path and end-to-end delay path. The approach samples, estimates, and builds the model of pertinent aspects of the environment. It uses a model that combines both a stochastic planned prenavigation for the exploration phase and a deterministic approach for the backward phase. To show the robustness and performances of the proposed approach, simulation scenario is built through the specification of the interested network topology and involved network traffic between robots. For this, this approach has been compared to traditional optimal path routing policy.

Keywords

Networked robots Robot swarm Adaptive approaches Irregular traffic Quality of service based routing 

References

  1. 1.
    Cao E, Spears WM (2005) Swarm robotics. Springer, BerlinGoogle Scholar
  2. 2.
    Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344(2–3):243–278MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Tapus A, Mataric MJ (2007) Towards active learning for socially assistive robots. In: Poster paper in Neural Information Processing Systems (NIPS): workshop on robotics challenges for machine learning, Whistler, B.C., CanadaGoogle Scholar
  4. 4.
    Koenig N, Mataric MJ (2006) Demonstration-based behavior and task learning. Working notes, AAAI Spring Symposium, Stanford, CaliforniaGoogle Scholar
  5. 5.
    Mataric M (1997) Behavior base control: examples from navigation, learning, and group behavior. Exp Theor Artif Intell 9:323–326CrossRefGoogle Scholar
  6. 6.
    Parker LE, Claude Touzet C, Jung D (2000) Learning and adaptation in multi-robot teams. In: Proc. of 18th Symposium on Energy Engineering Sciences, pp 177–185Google Scholar
  7. 7.
    Parker LE (1999) A case study for life-long learning and adaptation in cooperative robot teams. In: Proc. of SPIE Sensor Fusion and Decentralized Control in Robotic Systems II, Vol. 3839, pp 92–101Google Scholar
  8. 8.
    Takamuku S, Arkin CA (2007) Multi-method learning and assimilation. Robot Auton Syst 55(8):618–627CrossRefGoogle Scholar
  9. 9.
    Arkin RC, Lee B (2003) Adaptive multi-robot behavior via learning momentum. In: IEEE International Conference on Intelligent Robots and Systems, IROS 2003, Las Vegas, Nevada, Vol. 2, pp 2029–2036Google Scholar
  10. 10.
    Sgorbissa A, Arkin RC (2003) Local navigation strategies for a team of robots. Robotica 21:461–473CrossRefGoogle Scholar
  11. 11.
    Pugh J, Martinoli A (2007) Parallel learning in heterogeneous multi-robot swarms. IEEE Congress on Evolutionary Computation, pp 3839–3846Google Scholar
  12. 12.
    Winfield AFT, Nembrini J (2006) Safety in numbers: fault tolerance in robot swarms. Int J Model Identif Control 1(1):30–37CrossRefGoogle Scholar
  13. 13.
    Li W, Shen W (2011) Swarm behavior control of mobile multi-robots with wireless sensor networks. J Netw Comput Appl 34(4):1398–1407MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kim DM, Hwang Y, Kim S, Jin G (2011) Testbed results of an opportunistic routing for multi-robot wireless networks. Comput Comm 34(18):2174–2183CrossRefGoogle Scholar
  15. 15.
    Kok JR, Vlassis N (2005) Using the max-plus algorithm for multiagent decision making in coordination graphs. RoboCup 2005, Robot Soccer World Cup IX, Osaka, JapanGoogle Scholar
  16. 16.
    Mondada F, Pettinaro GC, Guignard A, Kwee I, Floreano D, Dneubourg JL, Nolfi S, Gambardella LM, Dorigo M (2004) SWARM-BOT: a new distributed robotic concept, autonomous robots. Swarm Robot 17(2–3):193–221Google Scholar
  17. 17.
    Poduri S, Sukhatme GS (2004) Constrained coverage for mobile sensor networks. In: IEEE Int. Conf. on Robotics and Automation, New Orleans, LA, USAGoogle Scholar
  18. 18.
    Rutishauser S, Correll N, Martinoli A (2009) Collaborative coverage using a swarm of networked miniature robots. Robot Autonom Syst 57(5):517–525CrossRefGoogle Scholar
  19. 19.
    Kovacs T, Pasztor A, Istenes Z (2011) A multi-robot exploration algorithm based on a static Bluetooth communication chain. Robot Auton Syst 59(num. 7):530–542CrossRefGoogle Scholar
  20. 20.
    Bertsekas D, Gallager R (1992) Data networks. Prentice-Hall, Upper Saddle RiverzbMATHGoogle Scholar
  21. 21.
    Oida K, Sekido M (1999) An agent-based routing system for QoS guarantees. In: Proc. IEEE International Conference on Systems, Man, and Cybernetics, Oct. 12–15, pp 833–838Google Scholar
  22. 22.
    Chakrabarti S, Mishra A (2001) QoS issues in Ad Hoc wireless networks. IEEE Comm Mag 39:142–148CrossRefGoogle Scholar
  23. 23.
    Bieszczad A, Pagurek B, White T (1998) Mobile agents for network management. IEEE Commun Surv, fourth quarter 1(1)Google Scholar
  24. 24.
    Dressler F, Akan O (2010) Bio-inspired networking: from theory to practice. IEEE Comm Mag 48(11):176–183CrossRefGoogle Scholar
  25. 25.
    Heusse M, Snyers D, Guérin S, Kuntz P (1998) Adaptive agent-driven routing and load balancing in communication network. In: Proc. ANTS’98, 1st Int. Workshop on Ant Colony Optimization, Brussels, Belgium, October 15–16Google Scholar
  26. 26.
    Choonderwoerd R, Holland OE, Bruten J, Rothkrantz L (1996) Ant-based load balancing in telecommunications networks. HP Labs Technical Report, HPL-96-76Google Scholar
  27. 27.
    Kassabalidis I, El-Sharkawi MA, Marks RJ, Arabshahi P, Gray AA (2001) Swarm intelligence for routing in communication networks. IEEE Globcom, San AntonioGoogle Scholar
  28. 28.
    Bonabeau E, Dorigo M, Théraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, OxfordzbMATHGoogle Scholar
  29. 29.
    Martins J, Correia S, Celestino J (2010) Ant-DYMO: a bio-inspired algorithm for MANETS. In: Proceedings of the 17th IEEE International Conference on Telecommunication (ICT), pp 748–754Google Scholar
  30. 30.
    Villalba, Canas D, Orozco A (2010) Bio-inspired routing protocol for mobile ad hoc networks. IET Communications 4(18):2187–2195CrossRefGoogle Scholar
  31. 31.
    Correia S, Junior J, Cherkaoui O (2011) Mobility-aware ant colony optimization routing for vehicular ad hoc networks. In: Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), pp 1125–1130Google Scholar
  32. 32.
    Kuipers F, Van Mieghem P (2005) Conditions that impact the complexity of QoS routing. IEEE/ACM Trans Network 13(4):717–730CrossRefGoogle Scholar
  33. 33.
    Mellouk A, Hoceini S, Zeadally S (2009) Design and performance analysis of an inductive QoS routing algorithm. Comput Comm J 32(12):1371–1376CrossRefGoogle Scholar
  34. 34.
    Mellouk A, Hoceini S, Amirat Y (2007) Adaptive quality of service based routing approaches: development of a neuro-dynamic state-dependent reinforcement learning algorithm. Int J Comm Syst 20(10):1113–1130CrossRefGoogle Scholar
  35. 35.
    Vapnik VN, Bottou L (2004) “Stochastic learning”, advanced lectures on machine learning, LNAI 3176. Springer, BerlinGoogle Scholar
  36. 36.
    Tsypkin Y (1973) Foundations of the theory of learning systems. Academic, New YorkzbMATHGoogle Scholar
  37. 37.
    Henig M (1983) Vector-valued dynamic programming. SIAM J Contr Optim 3:490–499MathSciNetCrossRefGoogle Scholar

Copyright information

© Institut Mines-Télécom and Springer-Verlag 2012

Authors and Affiliations

  • S. Hoceini
    • 1
    Email author
  • A. Mellouk
    • 1
  • A. Chibani
    • 1
  • Y. Touati
    • 2
  • B. Augustin
    • 1
  1. 1.LiSSi Laboratory & Department of Networks and Telecoms, IUT C/VUniversity of Paris-Est (UPEC)Vitry-sur-SeineFrance
  2. 2.LIASD LaboratoryParis 8 UniversitySaint-Denis CedexFrance

Personalised recommendations