Type-2 Fuzzy Logic-Based Robot Navigation in Uncertain Environments: Simulation and Real-World Implementation

This study presents a type-2 fuzzy logic-based navigation system for mobile robots in uncertain environments, emphasizing both simulation and real-world implementation. The proposed system integrates two type-2 fuzzy logic controllers: one for path-following and another for handling uncertainty in dynamic surroundings. To evaluate the system’s effectiveness, numerical simulations are conducted in cluttered and unpredictable environments, followed by real-world tests. The evaluation considers success rates, path efficiency, and computational cost, demonstrating an improvement of up to 92% in navigation accuracy and 8% in handling environmental uncertainty compared to conventional fuzzy logic methods. Despite its robustness, the approach faces computational overhead and adaptability challenges in highly unstructured settings. The study highlights the scalability of the method, discussing its potential application to different robotic platforms and uncertain scenarios. The findings confirm that type-2 fuzzy logic enhances real-time decision-making in navigation while offering a resilient alternative to traditional path-planning methods.