MARCoPlan: MultiAgent Remote Control for Robot Motion Planning

  • Authors:
  • Sonia Kefi;Ines Barhoumi;Ilhem Kallel;Adel M. Alimi

  • Affiliations:
  • REGIM : REsearch Group on Intelligent Machines Engineering School of Sfax, University of Sfax, Tunisia and Department of Computer Science, High Institute of Computer Science and Management of Kair ...;REGIM : REsearch Group on Intelligent Machines Engineering School of Sfax, University of Sfax, Tunisia and Department of Computer Science, High Institute of Computer Science and Management of Kair ...;REGIM : REsearch Group on Intelligent Machines Engineering School of Sfax, University of Sfax, Tunisia and Department of Computer Science, High Institute of Computer Science and Management of Kair ...;Department of Computer Science, High Institute of Computer Science and Management of Kairouan, University of Kairouan, Tunisia

  • Venue:
  • ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
  • Year:
  • 2006

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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. MARCoPlanis simulated and tested using randomly and dynamically generated problem instances with different distributions of obstacles. The tests verify some robustness of MARCoPlanwith 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.