通过单轴可行、间隙严重性引导分解的完整无人机覆盖路径规划
Coverage Path Planning for Holonomic UAVs via Uniaxial-Feasible, Gap-Severity Guided Decomposition
AI总结：
针对完整无人机在应急响应中覆盖路径规划面临的问题，提出用递归双轴单调性标准及累积间隙严重性度量引导切割的分解策略，搭配全局优化器，降低路径长度和完成时间开销，优于其他15种CPP管道。
摘要：
现代应急响应中完整无人机的覆盖路径规划（CPP）必须应对各种环境，其中感兴趣区域（ROI）通常是高度不规则的多边形，具有不对称形状、密集的凹坑簇和多个内部空洞。现代CPP管道通常依赖于将此类多边形过度分割成许多子区域的分解策略。这增加了扫描段和连接器的数量，进而增加了区域间行程并迫使更频繁地重新定向。这些影响最终导致完成时间延长和轨迹质量下降。我们采用一种分解策略来解决这个问题，该策略应用递归双轴单调性标准，并由累积间隙严重性度量引导切割。这种方法将凹坑簇更均匀地分布在子区域中，并产生一组最小的分区，这些分区在平行轨道机动下仍可扫描。我们将此与全局优化器配对，该优化器联合选择扫描路径和分区间过渡，以最小化总路径长度、过渡开销和转弯次数。我们证明，在其他15种CPP管道中，我们提出的方法实现了最低的平均路径长度和完成时间开销。
英文摘要：
Modern coverage path planning (CPP) for holonomic UAVs in emergency response must contend with diverse environments where regions of interest (ROIs) often take the form of highly irregular polygons, characterized by asymmetric shapes, dense clusters of concavities, and multiple internal holes. Modern CPP pipelines typically rely on decomposition strategies that overfragment such polygons into numerous subregions. This increases the number of sweep segments and connectors, which in turn adds inter-region travel and forces more frequent reorientation. These effects ultimately result in longer completion times and degraded trajectory quality. We address this with a decomposition strategy that applies a recursive dual-axis monotonicity criterion, with cuts guided by a cumulative gap severity metric. This approach distributes clusters of concavities more evenly across subregions and produces a minimal set of partitions that remain sweepable under a parallel-track maneuver. We pair this with a global optimizer that jointly selects sweep paths and inter-partition transitions to minimize total path length, transition overhead, and turn count. We demonstrate that our proposed approach achieves the lowest mean path-length and completion-time overhead among 15 other CPP pipelines.
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