Safety-Constrained UAV Trajectory Planning for AoI Minimization in Post-Disaster IoT Networks

Abstract

Unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection is a promising solution for timely information acquisition in post-disaster scenarios with damaged terrestrial infrastructure. However, freshness-aware UAV trajectory planning is challenging due to the coupled effects of heterogeneous ground node priorities, Age of Information (AoI) evolution, continuous UAV control, and safety risks caused by no-fly zones and initially unknown obstacles. In this letter, we formulate the safety-constrained weighted AoI minimization problem as a constrained Markov decision process (CMDP) and propose a safety-constrained twin delayed deep deterministic policy gradient (SC-TD3) algorithm with Lagrangian safety optimization to decouple the AoI-oriented objective from long-term safety-risk control and adaptively balance information freshness and safety risk during policy learning. Simulation results show that SC-TD3 achieves higher accumulated reward and reduces mean weighted AoI by 64.3%–78.2% and 67.1%–73.6% in the CN-ratio and GN-scale tests, respectively, while reducing mean total safety cost by 61.4%–75.6% compared with the strongest benchmark algorithm.