In this work, we present a hierarchical framework designed to support robotic inspection under environment uncertainty. By leveraging a known environment model, existing methods plan and safely track inspection routes to visit points of interest. However, discrepancies between the model and actual site conditions, caused by either natural or human activities, can alter the surface morphology or introduce path obstructions. To address this challenge, the proposed framework divides the inspection task into: (a) generating the initial global view-plan for region of interests based on a historical map and (b) local view replanning to adapt to the current morphology of the inspection scene. The proposed hierarchy preserves global coverage objectives while enabling reactive adaptation to the local surface morphology. This enables the local autonomy to remain robust against environment uncertainty and complete the inspection tasks. We validate the approach through deployments in real-world subterranean mines using quadrupedal robot. A supplementary media highlighting the proposed method can be found here https://youtu.be/6TxK8S_83Lw.

翻译：本文提出了一种分层框架，旨在支持环境不确定性下的机器人巡检任务。现有方法通常基于已知环境模型规划并安全跟踪巡检路径以访问兴趣点。然而，模型与实际现场条件之间的差异——无论是自然因素还是人为活动所致——都可能改变表面形态或引入路径障碍。为应对这一挑战，所提框架将巡检任务分解为：(a) 基于历史地图为兴趣区域生成初始全局视点规划；(b) 通过局部视点重规划适应巡检场景的当前形态。该分层结构在保持全局覆盖目标的同时，实现了对局部表面形态的实时自适应调整，使局部自主系统能够在环境不确定性下保持鲁棒性并完成巡检任务。我们通过四足机器人在真实地下矿场的部署验证了该方法的有效性。展示所提方法的补充媒体资料可见：https://youtu.be/6TxK8S_83Lw。