We present a cooperative aerial-ground search-and-rescue (SAR) framework that pairs two unmanned aerial vehicles (UAVs) with an unmanned ground vehicle (UGV) to achieve rapid victim localization and obstacle-aware navigation in unknown environments. We dub this framework Guided Long-horizon Integrated Drone Escort (GLIDE), highlighting the UGV's reliance on UAV guidance for long-horizon planning. In our framework, a goal-searching UAV executes real-time onboard victim detection and georeferencing to nominate goals for the ground platform, while a terrain-scouting UAV flies ahead of the UGV's planned route to provide mid-level traversability updates. The UGV fuses aerial cues with local sensing to perform time-efficient A* planning and continuous replanning as information arrives. Additionally, we present a hardware demonstration (using a GEM e6 golf cart as the UGV and two X500 UAVs) to evaluate end-to-end SAR mission performance and include simulation ablations to assess the planning stack in isolation from detection. Empirical results demonstrate that explicit role separation across UAVs, coupled with terrain scouting and guided planning, improves reach time and navigation safety in time-critical SAR missions.

翻译：我们提出了一种空地协同搜救框架，该框架将两架无人机与一辆无人地面车辆配对，以实现未知环境中的快速受害者定位和障碍感知导航。我们将此框架称为“引导式长视距集成无人机护航”（GLIDE），突出无人地面车辆在长时域规划中对无人机引导的依赖。在该框架中，负责目标搜索的无人机执行实时机载受害者检测和地理配准，为地面平台指定目标；而负责地形侦察的无人机则沿无人地面车辆规划路径前方飞行，提供中层级可通行性更新。无人地面车辆将空中感知线索与本地传感信息融合，在信息到达时执行高效的A*规划与连续重规划。此外，我们通过硬件演示（以GEM e6高尔夫球车作为无人地面车辆，配备两架X500无人机）评估端到端搜救任务性能，并开展仿真消融实验以独立于检测环节评估规划栈。实验结果表明，在时间敏感的搜救任务中，通过无人机间的明确角色分工，结合地形侦察与引导式规划，能够有效缩短到达时间并提升导航安全性。