让机器人在不确定环境中自主调整巡检路线，适应真实地形变化。
An Adaptive Inspection Planning Approach Towards Routine Monitoring in Uncertain Environments
- 分层规划：先基于历史地图生成全局巡检计划，再局部动态调整路线。
- 实测验证：在真实地下矿井中使用四足机器人完成巡检任务。
- 适合矿业、基建等复杂环境下的机器人自主巡检应用。
本文提出一种分层框架，用于在环境不确定条件下支持机器人巡检。现有方法依赖已知环境模型规划安全巡检路径，但自然或人为活动导致的模型与实际差异会改变表面形态或引入障碍物。为此，该框架将任务分为两步：(a) 基于历史地图生成区域兴趣点的初始全局视图计划；(b) 根据当前场景表面形态进行局部视图重规划。该分层设计在保持全局覆盖目标的同时，实现对局部地形变化的实时响应，使局部自主性具备应对环境不确定性的鲁棒性，保障巡检任务完成。通过在真实地下矿井中部署四足机器人进行验证，结果表明该方法有效可行。
原文摘要 · Abstract (English)
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.
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