Advertisement

MARCoPlan: MultiAgent Remote Control for Robot Motion Planning

  • Sonia Kefi
  • Ines Barhoumi
  • Ilhem Kallel
  • Adel M. Alimi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5097)

Abstract

A multiagent system to support a mobile robot motion planning has been presented. Baptized MARCoPlan (MutiAgent Remote Control motion Planning), this system deals with optimizing robot path. Considered as an agent, the robot has to optimize its motion from a start position to a final goal in a dynamic and unknown environment, on the one hand by the introduction of sub-goals, and on the other hand by the cooperation of multiagents. In fact, we propose to agentify the proximity environment (zones) of the robot; cooperation between theses zones agents will allow the selection of the best sub-goal to be reached. Therefore, the task of the planner agent to guide the robot to its destination in an optimized way will be easier. MARCoPlan is simulated and tested using randomly and dynamically generated problem instances with different distributions of obstacles. The tests verify some robustness of MARCoPlan with regard to environment changes. Moreover, the results highlight that the agentification and the cooperation improve the choice of the best path to the sub goals, then to the final goal.

Keywords

Agentification multiagent model planning cooperation mobile robot sub-goal MARCoPlan 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    DesJardins, M., Durfee, E.H., Ortiz Jr., C.L., Wolverton, M.: A Survey of Research in Distributed, Continual Planning. AI Magazine 20(4), 13–22 (1999)Google Scholar
  2. 2.
    Durfee, E.H.: Distributed Continual Planning for Unmanned Ground Vehicle Teams. AI Magazine 20(4), 55–61 (1999)Google Scholar
  3. 3.
    El Fallah-Seghrouchni, A., Degirmenciyan-Cartault, I., Marc, F.: Framework for Multi-agent Planning Based on Hybrid Automata. In: Mařík, V., Müller, J.P., Pěchouček, M. (eds.) CEEMAS 2003. LNCS (LNAI), vol. 2691, pp. 226–235. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Fischer, T., Gehring, H.: Planning vehicle transhipment in a seaport automobile terminal using a multi-agent system. European Journal of Operational Research 166, 726–740 (2005)zbMATHCrossRefGoogle Scholar
  5. 5.
    Hendler, J.A., Tate, A., Drummond, M.: AI Planning: Systems and Techniques. AI Magazine 11(2), 61–77 (1990)Google Scholar
  6. 6.
    Kammoun, H.M., Kallel, I., Alimi, A.M.: RoSMAS2: Road Supervision based Multi Agent System Simulation. In: Proc of the International Conference on Machine Intelligence, Tozeur-Tunisia, pp. 203–210 (November 2005)Google Scholar
  7. 7.
    Kallel, I., Baklouti, N., Alimi, A.M.: Accuracy Preserving Interpretability with Hybrid Hierarchical Genetic Fuzzy Modeling: Case of Motion Planning Robot Controller. In: Proc. of the International Symposium on Evolving Fuzzy Systems, Lake District, UK, pp. 312–317 (September 2006)Google Scholar
  8. 8.
    Kim, J., Pearce, R.A., Amato, N.M.: Robust geometric-based localization in indoor environments using sonar range sensors. In: Proceedings of International Conference on Intelligent Robots and System. IEEE/RSJ, vol. 1, pp. 421–426 (2002)Google Scholar
  9. 9.
    Kuu-Young, Y., Chi-Haur, W.: Path feasibility and modification. Journal of robotic systems (JRS) 9(5), 613–633 (1992)zbMATHCrossRefGoogle Scholar
  10. 10.
    Meignan, D., Simonin, O., Koukam, A.: Simulation and evaluation of urban bus networks using a mutiagent approach. Simulation Modelling Practice and Theory 15, 659–671 (2007)CrossRefGoogle Scholar
  11. 11.
    Rui, X., Ping-Yuan, C., Xiao-fei, X.: Realization of multi-agent planning system for autonomous spacecraft. Advances in Engineering Software 36, 266–272 (2005)CrossRefGoogle Scholar
  12. 12.
    Wooldridge, M.: An introduction to multiagent systems. John Wiley and Sons, Chichester (2002)Google Scholar
  13. 13.
    Zhang, J., Knoll, A.: Integrating deliberative and reactive strategies via fuzzy modular control. In: Saffotti Drainkov, A. (ed.) Fuzzy Logic techniques for autonomous vehicle navigation, Springer, Heidelberg (1999)Google Scholar
  14. 14.
    Zhang, J., Wang, H., Li, P.: Towards the applications of multi-agent techniques in intelligent transportation systems. In: IEEE Proc. of Intelligent Transportation Systems, vol. 36, pp. 1750–1754 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sonia Kefi
    • 1
    • 2
  • Ines Barhoumi
    • 1
    • 2
  • Ilhem Kallel
    • 1
    • 2
  • Adel M. Alimi
    • 2
  1. 1.REGIM : REsearch Group on Intelligent Machines Engineering School of SfaxUniversity of SfaxTunisia
  2. 2.Department of Computer Science, High Institute of Computer Science and Management of KairouanUniversity of KairouanTunisia

Personalised recommendations