Abstract
Autonomous navigation is a critical component in the development of self-driving vehicles, drones, and robots. Deep learning techniques have shown great potential in enabling autonomous systems to navigate through complex environments by processing vast amounts of sensory data. This article explores how deep learning is applied in autonomous navigation, focusing on algorithms, challenges, and future prospects. We discuss the integration of computer vision, reinforcement learning, and deep neural networks in enabling navigation systems to understand and interact with dynamic, uncertain environments, and how these technologies are paving the way for the future of autonomous mobility.
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