Journal of Intelligent & Robotic Systems

, Volume 68, Issue 1, pp 3–19 | Cite as

Swarm-like Methodologies for Executing Tasks with Deadlines

Article

Abstract

Very few studies have been carried out to test multi-robot task allocation swarm algorithms in real time systems, where each task must be executed before a deadline. This paper presents a comparative study of several swarm-like algorithms and auction based methods for this kind of scenarios. Moreover, a new paradigm called pseudo-probabilistic swarm-like, is proposed, which merges characteristics of deterministic and probabilistic classical swarm approaches. Despite that this new paradigm can not be classified as swarming, it is closely related with swarm methods. Pseudo-probabilistic swarm-like algorithms can reduce the interference between robots and are particularly suitable for real time environments. This work presents two pseudo-probabilistic swarm-like algorithms: distance pseudo-probabilistic and robot pseudo-probabilistic. The experimental results show that the pseudo-probabilistic swarm-like methods significantly improve the number of finished tasks before a deadline, compared to classical swarm algorithms. Furthermore, a very simple but effective learning algorithm has been implemented to fit the parameters of these new methods. To verify the results a foraging task has been used under different configurations.

Keywords

Multi-robot Task allocation Swarm-like Pseudo-random swarm Learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agassounon, W., Martinoli, A.: Efficiency and robustness of threshold-based distributed allocation algorithms in multi-agent systems. In: 1st Int. Joint Conference on Autonomous Agents and Multi-agents Systems, pp. 1090–1097. Bologna, Italy (2002)Google Scholar
  2. 2.
    Altshuler, Y., Bruckstein, A., Wagner, I.: On swarm optimality in dynamic and symmetric environments. Second International Conference on Informatics in Control, Automation and Robotics (ICINCO) (2005)Google Scholar
  3. 3.
    Bonabeau, E., Sobkowski, A., Theraulaz, G., Deneubourg, J.L.: Adaptive task allocation inspired by a model of division of labor in social insects. In: Lundh, D., Olsson, B., Narayanan, A. (eds.) Bio Computation and Emergent Computing, pp. 36–45. World Scientific (1997)Google Scholar
  4. 4.
    Brian P. Gerkey, M.M.: A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Rob. Res. 23(9), 939–954 (2004)CrossRefGoogle Scholar
  5. 5.
    Campbell, A., Wu, A., Shumaker, R.: Multi-agent task allocation: learning when to say no. In: 10th Annual Conference on Genetic and Evolutionary Computation, pp. 201–208. Atlanta, USA (2008)Google Scholar
  6. 6.
    Choi, H.L., Brunet, L., How, J.: Consensus-based decentralized auctions for robust task allocation. IEEE Trans. on Robotics 25(4), 912–926 (2009). doi:10.1109/TRO.2009.2022423 CrossRefGoogle Scholar
  7. 7.
    Daichi Kato, K.S., Fukuda, T.: Risk management system based on uncertainty estimation by multi-robot. J. Robot. Mechatronics 20(4), 456–466 (2010)Google Scholar
  8. 8.
    de Oliveira, D., Ferreira, P.R., Bazzan, A.L.: A swarm based approach for task allocation in dynamic agents organizations. In: 3th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 3, pp. 1252–1253. Nueva York, USA (2004)Google Scholar
  9. 9.
    del Acebo, E., de-la Rosa, J.L.: Introducing bar systems: a class of swarm intelligence optimization algorithms. In: AISB Convention Communication, Interaction and Social Intelligence, pp. 18–23. Aberdeen, Scotland (2008)Google Scholar
  10. 10.
    Dias, M.B., Stentz, A.: Traderbots: a market-based approach for resource, role, and task allocation in multirobot coordination. Tech. Rep. CMU-RI-TR-03-19, Carnegie Mellon University, Pittsburgh, USA (2003)Google Scholar
  11. 11.
    Gerkey, B.P., Mataric, M.: Sold!: auction methods for multi-robot coordination. IEEE Trans. Robot. Autom. Special Issue on Multi-robot Systems 18(5), 758–768 (2002)Google Scholar
  12. 12.
    Ghiasvand, O.A., Sharbafi, M.A.: Using earliest deadline first algorithms for coalition formation in dynamic time-critical environment. Education and Information Tech. 1(2), 120–125 (2011)Google Scholar
  13. 13.
    Guerrero, J., Oliver, G.: A multi-robot auction method to allocate tasks with deadlines. In: 7th IFAC Symposium on Intelligent Autonomous Vehicles. Lecce, Italy (2010)Google Scholar
  14. 14.
    Guerrero, J., Oliver, G.: Multi-Robot Systems, Trends and Development, chap. Auction and Swarm Multi-Robot Task Allocation Algorithms in Real Time Scenarios, pp. 437–456. InTech (2011)Google Scholar
  15. 15.
    Jones, E.G., Dias, M., Stentz, A.: Learning-enhanced market-based task allocation for disaster response. Tech. Rep. CMU-RI-TR-06-48, Carnegie Mellon University, Pittsburgh, USA (2006)Google Scholar
  16. 16.
    Kalra, N., Martinoli, A.: A comparative study of market-based and threshold-based task allocation. In: 8th International Symposium on Distributed Autonomous Robotic Systems, pp. 91–102. Minneapolis, USA (2006)Google Scholar
  17. 17.
    Koes, M., Nourbakhsh, I., Sycara, K.: Heterogeneous multirobot coordination with spatial and temporal constraints. In: 20th National Conference on Artificial Intelligence (AAAI), pp. 1292–1297. Boston, USA (2005)Google Scholar
  18. 18.
    Lemaire, T., Alami, R., Lacroix, S.: A distributed tasks allocation scheme in multi-uav context. In: International Conference on Robotics and Automation (ICRA), vol. 4, pp. 3622–3627. New Orleans, USA (2004)Google Scholar
  19. 19.
    Liu, W., Winfield, A., Sa, J., Chen, J., Dou, L.: Strategies for energy optimisation in swarm of foraging robots. Lect. Notes Comput. Sci. 4433, 14–26 (2007)CrossRefGoogle Scholar
  20. 20.
    Melvin, J., Keskinocak, P., Koenig, S., Tovey, C., Ozkaya, B.Y.: Multi-robot routing with rewards and disjoint time windows. In: International Conference on Intelligent Robots and Systems (IROS), pp. 2332–2337. San Diego, USA (2007)Google Scholar
  21. 21.
    Ramchurn, S.D., Polukarov, M., Farinelli, A., Truong, C.: Coalition formation with spatial and temporal constraints. In: International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 1181–1188. Toronto, Canada (2010)Google Scholar
  22. 22.
    Rosenfeld, A., Kaminka, G.A., Kraus, S.: Coordination of Large Scale Multiagent Systems, chap. A Study of Scalability Properties in Robotic Teams, pp. 27–51. Springer-Verlag (2006)Google Scholar
  23. 23.
    Smith, S.L., Bullo, F.: The dynamic team forming problem: throughput and delay for unbiased policies. Syst. Control. Lett. 58, 709–715 (2009)MathSciNetMATHCrossRefGoogle Scholar
  24. 24.
    Werger, B.B., Mataric, M.J.: Broadcast of local eligibility for multi-target observation. In: 5th International Symposium on Distributed Autonomous Robotic Systems, pp. 347–356. Knoxville, USA (2000)Google Scholar
  25. 25.
    Yang, Y., Zhou, C., Tin, Y.: Swarm robots task allocation based on response threshold model. In: 4th International Conference on Autonomous Robots and Agents, pp. 171–176. Willengton, New Zealand (2009)Google Scholar
  26. 26.
    Yu, L., Cai, Z.: Robot exploration mission planning based on heterogeneous interactive cultural hybrid algorithm. In: 5th International Conference on Natural Computation, pp. 583–587. Tianjin, China (2009)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Departament de Matemàtiques i InformàticaUniversitat de les Illes BalearesPalmaSpain

Personalised recommendations