MARCoPlan: MultiAgent Remote Control for Robot Motion Planning
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.
KeywordsAgentification multiagent model planning cooperation mobile robot sub-goal MARCoPlan
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- 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.Durfee, E.H.: Distributed Continual Planning for Unmanned Ground Vehicle Teams. AI Magazine 20(4), 55–61 (1999)Google Scholar
- 5.Hendler, J.A., Tate, A., Drummond, M.: AI Planning: Systems and Techniques. AI Magazine 11(2), 61–77 (1990)Google Scholar
- 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.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.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
- 12.Wooldridge, M.: An introduction to multiagent systems. John Wiley and Sons, Chichester (2002)Google Scholar
- 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.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