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This study proposes behavior-based navigation architecture, named BBFM, todeal with the problem of navigating the mobile robot in unknown environments inthe presence of obstacles and local minimum regions. In the architecture, thecomplex navigation task is split into principal sub-tasks or behaviors. Eachbehavior is implemented by a fuzzy controller and executed independently todeal with a specific problem of navigation. The fuzzy controller is modified tocontain only the fuzzification and inference procedures so that its output is amembership function representing the behavior's objective. The membershipfunctions of all controllers are then used as the objective functions for amulti-objective optimization process to coordinate all behaviors. The result ofthis process is an overall control signal, which is Pareto-optimal, used tocontrol the robot. A number of simulations, comparisons, and experiments wereconducted. The results show that the proposed architecture outperforms somepopular behavior-based architectures in term of accuracy, smoothness, traveleddistance, and time response.

Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization