Soft Computing based Implementation of Autonomous Drone Vehicle using Fuzzy Logic based Control Systems

In recent years, there has been a lot of interest in the use of fuzzy logic-based control systems to construct autonomous drone vehicles. This method makes use of fuzzy logic's adaptability and flexibility to guide drones safely through challenging surroundings. This study gives a comprehensive framework for the design and implementation of fuzzy logic-based control systems for autonomous drone vehicles. The three primary parts of the proposed framework are perception, decision-making, and control. The perception module processes sensor input, including visual and depth data, to provide accurate representations of the world. The decision-making module interprets the perception data and uses fuzzy logic methods to arrive at sensible choices for navigation and obstacle avoidance. The fuzzy logic system's decisions are converted by the control module into the right control signals that the drone will follow. The fuzzy logic-based control system also shows resilience and adaptation to changing environmental circumstances and uncertainties, making it appropriate for real-time applications. New opportunities for autonomous drone applications, such as surveillance, delivery services, and search and rescue missions, are made possible by the adoption of this architecture. Using fuzzy logic-based control systems to create autonomous drone vehicles is a viable way to provide effective and precise drone navigation in challenging conditions. By offering a holistic framework that blends perception, decision-making, and control utilizing fuzzy logic approaches, this research advances the development of autonomous systems.