Synergistic Navigation Control for Mobile Robots: Integrating Type-2 Fuzzy Logic and Neural Networks.

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Synergistic Navigation Control for Mobile Robots: Integrating Type-2 Fuzzy Logic and Neural Networks.

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Abstract

Abstract: Intelligent mobile robots operate in environments characterized by various uncertain-ties, necessitating effective navigation strategies to accomplish tasks such as path tracking and obstacle avoidance. This research employs a omni drive mobile robot to autonomously reach predefined targets in diverse scenarios within static and dynamic environments. The study evaluates two distinct controllers, a fuzzy logic controller and a neural network controller, em-ployed to guide the mobile robot safely towards its destination while mitigating collision risks with obstacles. These controllers regulate the mobile robot linear and angular velocities, ensuring adaptive navigation in real-time. Experimental results underscore the efficacy and adaptability of each controller, particularly in addressing uncertainty challenges inherent in mobile robot nav-igation. Through systematic evaluation and comparison, insights are gained into the relative performance and suitability of fuzzy logic and neural network controllers in enhancing mobile robot autonomy and robustness. This research contributes to advancing the understanding of navigation techniques in mobile robotics, facilitating the development of more efficient and re-liable autonomous systems for real-world applications.

Keywords

Keywords: Omni drive robot static and dynamic obstacle-avoidance environment neural net-work controller Type-2 fuzzy logic controller wireless sensor network

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