Trajectory Optimization Strategy for Networked UAVs Based on Deep Learning

The rapid advancement of networked UAV technology has broadened its applications in logistics, disaster relief, agriculture, and military. However, precise trajectory planning in complex dynamic environments remains a key challenge. Traditional methods suffer from low computational efficiency and poor real-time response, limiting practical use. This paper proposes a deep learning-based trajectory optimization strategy for networked UAVs, integrating reinforcement learning and deep neural networks to autonomously learn environmental information and collaborative patterns. Simulation experiments in realistic scenarios validate its effectiveness: the method enhances trajectory planning accuracy and robustness while reducing computational complexity and improving real-time response. This research provides new theoretical and technical foundations for promoting networked UAV applications.