Natural disasters often damage ground infrastructure, making unmanned aerial vehicles (UAVs) essential for emergency supply delivery. Yet safe operation in complex post-disaster environments requires reliable command-and-control (C2) links; link instability can cause loss of control, delay rescue, and trigger severe secondary harm. To provide continuous three-dimensional (3D) C2 coverage during dynamic missions, we propose a Heterogeneous Dual-Network Framework (HDNF) for safe and reliable emergency delivery. HDNF tightly couples an Emergency Communication Support Network (ECSN), formed by hovering UAV base stations, with a Delivery Path Network (DPN), formed by fast-moving delivery UAVs. The ECSN dynamically safeguards mission-critical flight corridors, while the DPN aligns trajectories with reliable coverage regions. We formulate a joint optimization problem over task assignment, 3D UAV-BS deployment, and DPN path planning to maximize end-to-end C2 reliability while minimizing UAV flight energy consumption and base-station deployment cost. To solve this computationally intractable NP-hard problem, we develop a layered strategy with three components: (i) a multi-layer C2 service model that overcomes 2D-metric limitations and aligns UAV-BS deployment with mission-critical 3D phases; (ii) a 3D coverage-aware multi-agent reinforcement learning algorithm that addresses the high-dimensional search space and improves both training efficiency and topology resilience; and (iii) a 3D communication-aware A* planner that jointly optimizes C2 quality and flight energy, mitigating trajectory--coverage mismatch and improving routing safety. Extensive simulations show that HDNF markedly improves C2 reliability, eliminates outages in critical phases, and sustains high task success rates while reducing hardware deployment cost.

翻译：自然灾害常导致地面基础设施损毁，使无人机成为应急物资投送的关键工具。然而，在复杂灾后环境中安全运行需要可靠的指挥控制链路；链路不稳定可能导致失控、延误救援，甚至引发严重的次生危害。为在动态任务期间提供连续的三维指挥控制覆盖，我们提出了一种异构双网络框架，用于安全可靠的应急投送。该框架紧密耦合了由悬停无人机基站构成的应急通信支撑网络与由快速移动投送无人机组成的投送路径网络。应急通信支撑网络动态保障关键任务飞行走廊，而投送路径网络则使航迹与可靠覆盖区域对齐。我们针对任务分配、无人机基站三维部署及投送路径网络路径规划建立联合优化问题，以最大化端到端指挥控制可靠性，同时最小化无人机飞行能耗与基站部署成本。为求解这一计算上棘手的NP困难问题，我们开发了一种分层策略，包含三个组成部分：(i) 一种多层指挥控制服务模型，克服了二维度量的局限性，使无人机基站部署与关键任务三维阶段对齐；(ii) 一种三维覆盖感知的多智能体强化学习算法，应对高维搜索空间，提升训练效率与拓扑韧性；(iii) 一种三维通信感知A*规划器，联合优化指挥控制质量与飞行能量，缓解航迹-覆盖失配，提升路由安全性。大量仿真表明，该框架显著提升了指挥控制可靠性，消除了关键阶段的中断，在降低硬件部署成本的同时维持了高任务成功率。