The navigation of autonomous mobile robots has in recent times gained interest from many researchers in different areas such as the industrial, agricultural, and military sectors. This paper aims at carefully investigating two advanced types of approaches for guiding a non‐holonomic mobile robot to navigate in an environment area cluttered with static obstacles. Firstly, a Fuzzy logic controller (FLC) was designed, using trapezoidal shape Membership functions (MF's). Secondly, an Adaptive neuro fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy controller. To validate the feasibility and effectiveness of the proposed models, V‐REP and MATLAB software are used. A comparative evaluation is, then, done on the basis of speed. The simulations results showed that the mobile robot could navigate successfully into maze environment with both proposed approaches but ANFIS controller provided better results in comparison to fuzzy controller.

中文翻译：

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基于模糊和神经模糊方法的迷宫路径预测和发现的自主智能机器人

近年来，自主移动机器人的导航引起了工业，农业和军事领域等不同领域的许多研究人员的兴趣。本文旨在仔细研究两种先进的方法，以引导非完整的移动机器人在充满静态障碍物的环境区域中导航。首先，使用梯形形状隶属度函数（MF's）设计了模糊逻辑控制器（FLC）。其次，使用自适应神经模糊推理系统（ANFIS）控制器来优化梯形模糊控制器获得的结果。为了验证所提出模型的可行性和有效性，使用了V‐REP和MATLAB软件。然后，根据速度进行比较评估。