多无人机协同侦察，降低地面车暴露风险38%
UGV-Conditioned Multi-UAV Informative Planning on a Shared Exposure Belief
- 共享暴露信念实现空地协同决策
- 无人机按区域分配避免重复探测
- 适合复杂威胁环境下的自主导航
在大型威胁环境中安全进行地面导航需要空中支持主动降低地面车辆路径上的风险。现有空中侦察系统侧重于地图构建或环境覆盖，但未针对地面车辆安全相关区域进行感知引导。本文解决多架无人飞行器（UAV）协同问题，以提升无人地面车辆（UGV）穿越未知威胁区域的安全性。核心在于构建一个在线更新的共享暴露信念，由无人机团队与地面车辆共同使用，从而将空中感知聚焦于路径相关区域，并允许地面车辆根据新发现的威胁重新规划路线。通过空间区域分配协调无人机团队，避免冗余感知。仿真结果表明，相比不考虑危险等级的系统，本方法使累计暴露减少38%；在多无人机协同方案下，冗余空中覆盖从38.8%降至3.7%。
原文摘要 · Abstract (English)
Safe ground navigation in large, threat-augmented environments requires aerial support that actively reduces the risks that a ground vehicle faces along its route. Existing aerial reconnaissance systems focus on mapping or covering the environment, but do not direct sensing toward regions that are most relevant for ground vehicle safety. In this paper, we address the problem of coordinating a team of unmanned aerial vehicles (UAVs) to improve the safety of an unmanned ground vehicle (UGV) navigating through unknown threat zones. A key aspect of our approach is a shared exposure belief that is updated online from aerial observations and used jointly by the UAV team and the ground vehicle. This enables us to direct aerial sensing towards route-relevant regions while allowing the UGV to replan around newly revealed threats. We coordinate the UAV team through spatial region assignment to avoid redundant sensing. Simulation experiments show that our approach reduces cumulative UGV exposure by 38% compared to a system that does not account for hazard levels, and reduces redundant aerial coverage from 38.8% to 3.7% under our multi-UAV coordination scheme.
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