DDPG-Based UAV Path Planning for Data Collection in Emergency IoT Networks

With the rapid development of VAV technology and the Internet of Things (IoT), the application of VAVs in emergency scenarios has become a research hotspot. In emergency situations, how to efficiently plan UAV flight paths to assist in collecting data from ground sensors has become a key issue to ensure a swift re-sponse to emergency actions. Traditional path planning methods often fail to adapt to complex and dynamic emergency environments. Therefore, based on the Deep Deterministic Policy Gradient (DDPG) algorithm, we introduce a Prioritized Experience Re-play (PER) network to improve learning efficiency, and propose a novel end-to-end reinforcement learning (RL) UAV path planning algorithm for data collection from IoT devices in emergency sce-narios.