A review of the application of fuzzy mathematical algorithm-based approach in autonomous vehicles and drones

Abstract

Autonomous vehicles (AVs) and unmanned aerial vehicles (UAVs) have brought about transformative changes in transportation and aviation. However, making these systems fully autonomous and able to navigate safely in unpredictable real-world situations remains a big challenge. Fuzzy logic and related mathematical algorithms have emerged as practical tools to tackle uncertainty and complex decision-making in these systems. This paper reviews how fuzzy logic and mathematical approaches are applied in areas like navigation, control, avoiding obstacles, planning routes, and decision-making for AVs and UAVs. It delves into the key methods, designs, pros, and cons of using fuzzy logic in autonomous vehicles. The paper also compares fuzzy logic with other AI techniques. The review shows that fuzzy logic manages the uncertainties and imprecision involved in how autonomous vehicles perceive and navigate dynamic environments. Fuzzy controllers often perform better than traditional methods in vehicle control and UAV direction control. High-level decisions and route planning in AVs have also benefited from fuzzy inference systems. Still, challenges like computational efficiency, adaptability, and integrating fuzzy logic with other AI components remain. The paper concludes with suggestions for future research to make autonomous vehicles and drones smarter and safer using fuzzy logic. This review is a useful guide for anyone developing intelligent autonomous systems.