无人机巡检遇未知障碍时能自动调整路径，提升效率与覆盖率。
Adaptive Planning Framework for UAV-Based Surface Inspection in Partially Unknown Indoor Environments
- 结合全局规划与局部响应，动态适应未知障碍
- 在结构化室内场景中实现高效覆盖与安全路径
- 适合工业设施、隧道等部分已知环境的智能巡检
隧道、工业厂房和施工现场等室内环境的检测对基础设施维护至关重要。人工巡检耗时且存在安全隐患，而无人机可自主完成任务。传统方法依赖参考地图进行路径规划，但工业现场通常仅有平面图，未预见的障碍物会导致地图过时，引发低效或危险的飞行轨迹。本文提出一种自适应巡检框架，融合全局覆盖规划与局部反应式调整，提升部分未知室内环境中无人机巡检的覆盖率与效率。在结构化室内场景中的实验表明，该方法能有效应对动态障碍，实现高覆盖率与高效巡检，具备显著应用潜力。
原文摘要 · Abstract (English)
Inspecting indoor environments such as tunnels, industrial facilities, and construction sites is essential for infrastructure monitoring and maintenance. While manual inspection in these environments is often time-consuming and potentially hazardous, Unmanned Aerial Vehicles (UAVs) can improve efficiency by autonomously handling inspection tasks. Such inspection tasks usually rely on reference maps for coverage planning. However, in industrial applications, only the floor plans are typically available. The unforeseen obstacles not included in the floor plans will result in outdated reference maps and inefficient or unsafe inspection trajectories. In this work, we propose an adaptive inspection framework that integrates global coverage planning with local reactive adaptation to improve the coverage and efficiency of UAV-based inspection in partially unknown indoor environments. Experimental results in structured indoor scenarios demonstrate the effectiveness of the proposed approach in inspection efficiency and achieving high coverage rates with adaptive obstacle handling, highlighting its potential for enhancing the efficiency of indoor facility inspection.
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