Neural Network Architectures for Autonomous Drone Navigation

Abstract

The development of autonomous drones hinges on advancements in neural network architectures that can process complex environmental data in real time. This paper discusses innovative neural network models tailored for autonomous navigation, enabling drones to make split-second decisions and navigate safely through dynamic environments. By leveraging deep learning techniques, these models improve path-planning and obstacle avoidance capabilities. The research includes a series of experiments comparing traditional navigation systems with the proposed neural models, highlighting significant enhancements in performance and reliability.

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