发表机构
Carnegie Mellon University; Brigham Young University（卡内基梅隆大学; 杨百翰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SCOPE是一种在未知3D环境中规划路径的框架，通过安全体积认证实现视场感知，可到达所有目标、减少任务时间，真实机器人演示验证其有效性。
AI 中文摘要
搭载有限视场传感器的机器人安全导航，要求在执行预期运动前，完整观测并验证机器人膨胀后的运动体积无障碍物。我们将该需求建模为未知体素地图中的在线安全体积认证，构建认证图，其顶点恰好对应安全体积完全已知为自由的位置。基于此表示，我们提出SCOPE（Safety Certification through Observation Planning and Execution，即通过观测规划与执行实现安全认证）规划框架，将乐观的目标导向引导与认证执行解耦。SCOPE将乐观路径上第一个未认证的点转化为显式观测义务，通过以目标为中心的视点搜索解决该义务，并在有用视点尚未认证可达时递归清除中间义务。认证预览机制和感知观测的轨迹优化后端支持平滑执行。我们证明了条件完备规划：在理想单调感知和穷举有限域图搜索下，只要规划原语内存在有限可行的认证感知动作序列，SCOPE就能到达目标。在三个未知三维环境的60个随机任务中，SCOPE到达了所有目标，同时几乎未进入非认证膨胀空间；预览功能将平均任务时间缩短了27%，两个代表性场景的真实机器人演示验证了该完整系统的有效性。
英文摘要
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.
CommentsProject website: https://yuanjunbin.github.io/scope-planner/
