AoI- and DS-Enhanced Cooperative Search for Multi-UAV Systems Under Spatially Structured Communication Constraints

Multi-UAV cooperative search is important for applications such as target reconnaissance, environmental monitoring, and emergency response. In practice, communication is often spatially heterogeneous due to terrain occlusion and environmental interference, which may delay information sharing and weaken coordination efficiency when UAVs traverse communication-blocked areas. To address this issue, we propose an Age of Information (AoI)- and Dempster–Shafer (DS)-enhanced cooperative search framework for multi-UAV systems under spatially structured communication constraints. Specifically, a DS belief map is introduced to fuse uncertain observations, while AoI is used to characterize the freshness of delayed information. An AoI-aware update mechanism further integrates buffered observations into the global belief map after communication recovery. The search process is then formulated as a communication-aware multi-agent sequential decision-making problem and solved using reinforcement learning. To demonstrate the generality of the proposed framework, we instantiate it with Proximal Policy Optimization (PPO), Multi-Agent Proximal Policy Optimization (MAPPO), and Q-value Mixing Network (QMIX). Experimental results show that the proposed framework consistently outperforms the baseline methods under heterogeneous environments and different communication conditions. Among all variants, AoI-DS-MAPPO achieves the best overall performance, improving average reward, success rate, and the number of detected targets by 26.13%, 24.32%, and 3.65%, respectively, while reducing episode length by 31.96% relative to the strongest baseline.