3D coverage path planning for UAVs is a crucial problem in diverse practical applications. However, existing methods have shown unsatisfactory system simplicity, computation efficiency, and path quality in large and complex scenes. To address these challenges, we propose FC-Planner, a skeleton-guided planning framework that can achieve fast aerial coverage of complex 3D scenes without pre-processing. We decompose the scene into several simple subspaces by a skeleton-based space decomposition (SSD). Additionally, the skeleton guides us to effortlessly determine free space. We utilize the skeleton to efficiently generate a minimal set of specialized and informative viewpoints for complete coverage. Based on SSD, a hierarchical planner effectively divides the large planning problem into independent sub-problems, enabling parallel planning for each subspace. The carefully designed global and local planning strategies are then incorporated to guarantee both high quality and efficiency in path generation. We conduct extensive benchmark and real-world tests, where FC-Planner computes over 10 times faster compared to state-of-the-art methods with shorter path and more complete coverage. The source code will be open at https://github.com/HKUST-Aerial-Robotics/FC-Planner.

翻译：无人机三维覆盖路径规划是众多实际应用中的关键问题。然而，现有方法在处理大规模复杂场景时，在系统简洁性、计算效率和路径质量方面表现不佳。为应对这些挑战，我们提出FC-Planner——一种无需预处理的骨架引导规划框架，可实现复杂三维场景的快速空中覆盖。我们通过基于骨架的空间分解（SSD）将场景分解为多个简单子空间。此外，骨架还引导我们轻松确定自由空间。我们利用骨架高效生成一组最少且信息丰富的专门视点以实现完全覆盖。基于SSD，分层规划器能将大规模规划问题有效分解为独立的子问题，从而对每个子空间进行并行规划。随后，精心设计的全局与局部规划策略被整合，以同时保证路径生成的高质量与高效率。我们进行了广泛的基准测试和实景测试，结果表明FC-Planner的计算速度比最先进方法快10倍以上，且路径更短、覆盖更完整。源代码将开源在https://github.com/HKUST-Aerial-Robotics/FC-Planner。