Research on Environment Perception and Path Planning Modeling of UAV Navigation Guided by Deep Learning

This paper proposes a deep learning-based environment perception and path planning modeling method for UAV navigation. First, this paper uses binocular vision system to obtain environmental information, and uses deep learning algorithm for image processing and feature extraction to realize real-time perception of the surrounding environment. Secondly, aiming at the local path planning problem, this paper designs an algorithm based on deep reinforcement learning to improve the computing efficiency and achieve fast and accurate path planning through parallel computing technology. In addition, this paper also proposes an autonomous navigation strategy based on deep learning to enable drones to navigate autonomously in complex environments. Finally, simulation experiments are carried out to verify the effectiveness and superiority of the proposed algorithm. The experimental results show that the proposed method can realize efficient and accurate environment perception and path planning in various scenarios, and significantly improve the navigation performance of UAVs. The research in this paper not only enriches the application of deep learning in the field of UAV navigation, but also provides a valuable reference for the research in related fields.