针对复杂地形，提出更高效的无人机巡检路径分解方法。
Coverage Path Planning for Holonomic UAVs via Uniaxial-Feasible, Gap-Severity Guided Decomposition
- 基于双轴单调性递归分割，按凹陷严重度引导切分。
- 路径长度与完成时间均优于15种现有方法。
- 适合应急响应中不规则区域的高效无人机覆盖任务。
现代全向无人机在应急响应中的覆盖路径规划需应对多样环境，其中兴趣区域（ROIs）常呈高度不规则多边形，具有非对称形状、密集凹陷簇及多个内孔。现有规划流程通常采用过度分割策略，将此类多边形分解为大量子区域，导致扫掠段和连接段数量增加，引入更多跨区域移动与频繁转向，最终延长完成时间并降低轨迹质量。本文提出一种基于递归双轴单调性准则的分解策略，切分方向由累积缺口严重度指标引导。该方法可更均匀分布凹陷簇，生成最少数量且仍满足平行扫掠约束的分区。同时结合全局优化器，联合选择扫掠路径与分区间过渡，以最小化总路径长度、过渡开销与转向次数。实验表明，本方法在15种其他CPP流程中实现最低平均路径长度与完成时间开销。
原文摘要 · Abstract (English)
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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