让机器人在未知环境中安全导航，通过实时验证视野范围内的空间安全。
SCOPE: Field-of-View-Aware Path Planning in Unknown Space via Safety-Volume Certification
- 构建可认证的安全体积图，只在完全确认安全时才执行动作。
- 在100个任务中100%到达目标，进入未认证区域接近零。
- 适合需要高安全性的机器人自主导航场景，如搜救或工业巡检。
带有固定视场传感器的机器人在未知空间中安全导航，要求其运动路径所覆盖的机器人膨胀体积在执行前必须被完整观测并验证为空闲。本文将此需求建模为未知体素地图中的在线安全体积认证问题，并构建一个顶点对应于安全体积完全可知且空闲的位置的可认证图。基于该表示，提出SCOPE（通过观察规划与执行实现安全认证）框架，将乐观导向引导与可认证执行解耦。该框架将乐观路径上首个不确定点转化为显式的观察义务，通过以目标为中心的视角搜索解决，并在有用视角尚未可认证可达时递归清除中间义务。结合可认证预览机制和面向观察的轨迹优化后端，实现平滑执行。理论上证明了条件完备性：在理想单调感知和穷尽有限域图搜索条件下，只要存在有限可行的可认证感知序列，SCOPE就能到达目标。在五个未知三维环境中的100个随机任务中，SCOPE全部成功到达目标，且进入未认证膨胀空间的比例近似为零；消融实验表明，可认证预览机制使平均任务时间减少27%。最后，通过四类真实场景的机器人实测验证了系统的完整性。
原文摘要 · Abstract (English)
Safe navigation with a body-mounted limited-field-of-view sensor requires the complete robot-inflated volume of an intended motion to be observed and verified free before execution. We formulate this requirement as online safety-volume certification in an unknown voxel map and construct a certified graph whose vertices correspond exactly to positions with fully known-free safety volumes. Based on this representation, we propose SCOPE (Safety Certification through Observation Planning and Execution), a planning framework that decouples optimistic goal-directed guidance from certified execution. SCOPE converts the first uncertified point along an optimistic route into an explicit observation obligation, resolves it through target-centric viewpoint search, and recursively clears intermediate obligations when useful viewpoints are not yet certified-reachable. A certified preview mechanism and an observation-aware trajectory optimization backend enable smooth execution. We prove conditional completeness: under ideal monotone sensing and exhaustive finite-domain graph search, SCOPE reaches the goal whenever a finite feasible sequence of certified sensing actions exists within its planning primitives. Across 100 randomized tasks in five unknown 3D environments, SCOPE reaches every goal while maintaining near-zero entry into non-certified inflated space, and an ablation shows that the certified preview mechanism reduces mean mission time by 27%. Finally, we validate the complete system through real-robot demonstrations in four scenarios.
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