ASIP-Planner: Adaptive Planning for UAV Surface Inspection in Partially Known Indoor Environments

2026-05-11

AI Summary

This work addresses the challenge of autonomous drone inspection in structured yet partially unknown indoor environments, where reliance on precise prior maps renders conventional approaches vulnerable to temporary obstacles that cause view occlusions and incomplete coverage. The authors propose an adaptive inspection framework that innovatively integrates global viewpoint sequence optimization—based on surface-based clustering—with an online local viewpoint adjustment mechanism. This dual strategy preserves trajectory structure while dynamically mitigating occlusions through coordinated compact viewpoint generation, collision-aware path planning, and responsive local replanning. Extensive simulations and real-world flight experiments demonstrate that the proposed method achieves near-complete surface coverage with shorter trajectories, while the acquired data effectively supports downstream analysis, significantly enhancing both inspection efficiency and robustness to environmental uncertainty.

Abstract

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.

Problem

• UAV inspection
• partially known environments
• surface coverage
• occlusion
• indoor infrastructure

Innovation

• adaptive planning
• UAV inspection
• partial environment knowledge
• viewpoint optimization
• occlusion handling