In this paper, we propose a novel model-based coverage path planner for the 3D reconstruction of a target structure with an unmanned aerial vehicle (UAV). The proposed method rapidly calculates initial viewpoints considering the ground sampling distance (GSD) by partitioning a structure by height. Then, optimal viewpoints are selected by checking the collision and calculating overlaps and coverage. Next, the newly developed collision-aware Traveling Salesman Problem (CTSP) is used to connect the optimal viewpoints while guaranteeing the shortest distance and obstacle avoidance. Finally, the resulting path is refined as a control-efficient trajectory that considers the dynamics of UAVs. The performance of the proposed algorithm is verified by experiments on diverse structures.
CEO-MLCPP: Control-Efficient and Obstacle-Aware Multi-Layer Coverage Path Planner for 3D Reconstruction with UAVs
Lect. Notes in Networks, Syst.
International Conference on Robot Intelligence Technology and Applications ; 2022 ; Daejeon, Korea (Republic of) December 07, 2022 - December 09, 2022
01.03.2023
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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