<![CDATA[This research examines the integration of Artificial Intelligence (AI) in enhancing autonomous navigation systems within robotics, focusing on developing adaptive machine learning algorithms for high-dimensional data processing. The primary objective is to advance AI-based navigation systems that outperform traditional methods in terms of accuracy, obstacle avoidance, and efficiency. By leveraging deep learning for intricate visual perception and reinforcement learning for agile decision-making and path optimization, the study achieves a substantial increase in navigation precision and obstacle detection in both simulated and real-world settings. The findings reveal that these AI-driven systems surpass conventional rule-based systems and exhibit superior adaptability in dynamic and unstructured environments. Future efforts will concentrate on refining these algorithms to enhance environmental recognition and extend AI applications to more complex robotic operations. This research supports Sustainable Development Goals (SDGs) by promoting innovative infrastructure (SDG 9) and fostering industry innovation and infrastructure development, which are vital for sustainable economic growth and environmental protection.]]>
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