部分未知室内环境中基于无人机的表面检测自适应规划框架
Adaptive Planning Framework for UAV-Based Surface Inspection in Partially Unknown Indoor Environments
AI总结：
针对部分未知室内环境中基于无人机的表面检测因参考地图过时导致效率低的问题，提出整合全局覆盖规划与局部反应式自适应的框架，实验证明其可提升检测效率与覆盖率。
摘要：
检测隧道、工业设施和建筑工地等室内环境对基础设施监测和维护至关重要。尽管这些环境中的人工检测通常耗时且具有潜在危险，但无人机（UAVs）可通过自主处理检测任务来提高效率。此类检测任务通常依赖参考地图进行覆盖规划。然而，在工业应用中，通常仅能获取平面图。平面图中未包含的不可预见障碍物会导致参考地图过时，以及检测轨迹低效或不安全。在这项工作中，我们提出一种将全局覆盖规划与局部反应式自适应相结合的自适应检测框架，以提高部分未知室内环境中基于无人机的检测的覆盖率和效率。结构化室内场景中的实验结果表明，所提方法在检测效率和通过自适应障碍物处理实现高覆盖率方面有效，突显了其在提高室内设施检测效率方面的潜力。
英文摘要：
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 The unforeseen obstacles not included in the floor plans will result in outdated reference maps and inefficient or unsafe inspection trajectories.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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
