Abstract
The Unmanned Aerial Vehicle (UAV) equipped with a Light Detection and Ranging (LiDAR) scanner provides a versatile and efficient platform for mobile laser scanning in construction-related scenarios. Recent studies on UAVs have demonstrated point-to-point navigation, yet there has been sparse investigation on scan coverage planning to fully explore a construction site, and on kinodynamic motion planning to ensure energy-efficient trajectories. This study develops a Building Information Model (BIM)-supported framework to facilitate scan planning and motion planning of autonomous LiDAR-carrying UAVs. The proposed framework selectively integrates the geometry and semantics from BIM to construct a probabilistic 3D voxel map. Then, a greedy algorithm is developed to iteratively generate waypoints with optimized coverage. After that, a collision-free guiding path is computed for traversing all the waypoints before it is further transformed into a high-degree polynomial trajectory. The proposed framework was validated in a simulated construction scenario of water treatment facilities using MATLAB and Unreal Engine 4 (UE4). The planned trajectory demonstrated smoothness, energy efficiency, and sufficient coverage. It reduced 86.17% of required moments from motors over the regular A* algorithm and achieved 91.67% scan coverage on the target facility.
Published Version
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