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

TL;DR

This paper introduces BBFM, a behavior-based navigation system for mobile robots in unknown environments, utilizing fuzzy logic and multi-objective optimization to improve obstacle avoidance and path efficiency.

Contribution

It presents a novel architecture that combines fuzzy controllers with multi-objective optimization for coordinated robot navigation in unknown settings.

Findings

Outperforms existing architectures in accuracy and smoothness

Reduces traveled distance and response time

Demonstrated through simulations and experiments

Abstract

This study proposes behavior-based navigation architecture, named BBFM, to deal with the problem of navigating the mobile robot in unknown environments in the presence of obstacles and local minimum regions. In the architecture, the complex navigation task is split into principal sub-tasks or behaviors. Each behavior is implemented by a fuzzy controller and executed independently to deal with a specific problem of navigation. The fuzzy controller is modified to contain only the fuzzification and inference procedures so that its output is a membership function representing the behavior's objective. The membership functions of all controllers are then used as the objective functions for a multi-objective optimization process to coordinate all behaviors. The result of this process is an overall control signal, which is Pareto-optimal, used to control the robot. A number of simulations, comparisons, and experiments were conducted. The results show that the proposed architecture outperforms some popular behavior-based architectures in term of accuracy, smoothness, traveled distance, and time response.