无人机群协同搜救，提升未知环境下的定位与导航效率
GLIDE: A Coordinated Aerial-Ground Framework for Search and Rescue in Unknown Environments
- 两架无人机分工协作：一搜寻目标，一勘察地形
- 地面车融合空中信息，实时规划路径并动态调整
- 实测与仿真验证，显著缩短搜救时间并提升安全性
我们提出一种协同式空中-地面搜救框架（GLIDE），由两架无人机与一架地面无人车组成，用于在未知环境中快速定位幸存者并实现避障导航。其中，目标搜索无人机负责实时检测与地理定位幸存者，为地面平台指定目标；地形侦察无人机提前飞越规划路径，提供中层可通行性更新。地面车融合空中提示与本地感知数据，采用高效A*算法进行实时路径规划与持续重规划。实验使用GEM e6高尔夫球车作为地面平台，搭配两架X500无人机进行硬件演示，并通过仿真消融实验评估规划模块独立性能。结果表明，无人机间明确的角色分工结合地形预勘与引导规划，显著提升了搜救任务中的到达时间与导航安全性。
原文摘要 · Abstract (English)
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.
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