Hybrid Fuzzy-MutiAgent planning for robust mobile robot motion

This paper presents an intelligent hybrid system to support the planning for a mobile robot motion in unknown and dynamic environment. Called Fuzzy-MARCoPlan (Fuzzy-MultiAgent Remote Control motion Planning), this system optimizes the path by the introduction of sub-goals and through a multiagent cooperation based on fuzzy reasoning. In fact, we propose to agentify the surrounding zones of the robot; these zone agents compete for attracting the sub-goal. A planning agent, fortified with a fuzzy rule based system, decides on the best sub-goal to reach. Fuzzy-MARCoPlan is simulated and tested on several navigation environments which are generated randomly under the multiagent platform MadKit. These tests confirm the robustness of the proposed system in terms of path optimality in a dynamic environment. Moreover, the obtained results reinforce the advantage of a multiagent planning hybridized with fuzzy reasoning for mobile robot motion planning.