ASIP-Planner: 面向部分已知室内结构的自适应规划
ASIP-Planner: Adaptive Planning for UAV Surface Inspection in Partially Known Indoor Environments
AI总结：
本文提出ASIP-Planner框架，通过全局覆盖规划与局部视角调整模块，提升无人机在部分已知室内环境中的表面检测效率与适应性。
摘要：
室内基础设施检测，如隧道和工业设施，需要系统性的表面覆盖以确保所有检测目标被充分观察。无人机提供了一种替代手动检测的方法，通过使用先前的结构模型进行地图引导的表面检测。然而，在实践中，室内检测往往依赖于基于平面图的参考地图，这些地图可能无法反映突发障碍物，如临时结构或设备，导致视点被遮挡和检测质量下降。现有覆盖规划方法通常假设完全已知的检测环境，并基于准确的先验地图进行确定性全局视点优化，使其在执行过程中容易受到环境差异的影响。本文提出了一种自适应无人机检测框架，用于部分已知的结构化室内环境。所提出的方法整合了基于段的全局覆盖规划器与面向检测的局部视角适应模块。全局规划器将平面检测目标组织成表面对齐的簇，以生成紧凑的视点序列，提高方向一致性。局部规划器生成无碰撞轨迹，并在线调整视向以缓解遮挡引起的覆盖损失，同时保持规划轨迹结构。在随机场景配置的仿真结果中，所提出的全局规划器在减少轨迹长度的同时实现了接近完全的覆盖，相比代表性的基线方法。实际飞行实验进一步验证了该框架生成可用于后续分析的可用检测数据。这些结果表明，所提出的框架在部分已知的结构化室内环境中提高了检测效率和适应性。
英文摘要：
Indoor infrastructure inspection, such as tunnels and industrial facilities, requires systematic surface coverage to ensure that all inspection targets are properly observed. Unmanned Aerial Vehicles (UAVs) offer an alternative to manual inspection by conducting map-guided surface inspection using prior structural models. However, in practice, indoor inspection often relies on floorplan-derived reference maps that may not reflect unforeseen obstacles, such as temporary structures or equipment, leading to occluded viewpoints and degraded inspection quality. Existing coverage planning methods typically assume a fully known inspection environment and perform deterministic global viewpoint optimization based on accurate prior maps, making them vulnerable to environmental discrepancies during execution. This work presents an adaptive UAV inspection framework for partially known structured indoor environments. The proposed method integrates a segment-based global coverage planner with an inspection-oriented local view-angle adaptation module. The global planner organizes planar inspection targets into surface-aligned clusters to generate compact viewpoint sequences with improved orientation consistency. The local planner generates collision-free trajectories and adjusts the viewing direction online to mitigate occlusion-induced coverage loss while preserving the planned trajectory structure. The simulation results across randomized scene configurations demonstrate that the proposed global planner achieves near-complete coverage while reducing trajectory length compared to representative baselines. Real-world flight experiments further validate that the framework produces usable inspection data for downstream analysis. These results indicate that the proposed framework improves inspection efficiency and adaptability in partially known structured indoor environments.
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。
