Multi-robot systems are widely used for coverage tasks that require efficient coordination across large environments. In Multi-Robot Coverage Path Planning (MCPP), the objective is typically to minimize the makespan by generating non-overlapping paths for full-area coverage. However, most existing methods assume uniform importance across regions, limiting their effectiveness in scenarios where some zones require faster attention. We introduce the Priority-Aware MCPP (PA-MCPP) problem, where a subset of the environment is designated as prioritized zones with associated weights. The goal is to minimize, in lexicographic order, the total priority-weighted latency of zone coverage and the overall makespan. To address this, we propose a scalable two-phase framework combining (1) greedy zone assignment with local search, spanning-tree-based path planning, and (2) Steiner-tree-guided residual coverage. Experiments across diverse scenarios demonstrate that our method significantly reduces priority-weighted latency compared to standard MCPP baselines, while maintaining competitive makespan. Sensitivity analyses further show that the method scales well with the number of robots and that zone coverage behavior can be effectively controlled by adjusting priority weights.

翻译：多机器人系统广泛用于需要在大范围环境中高效协调的覆盖任务。在多机器人覆盖路径规划（MCPP）中，目标通常是通过为全区域覆盖生成无重叠路径来最小化总完成时间。然而，现有方法大多假设各区域具有同等重要性，这限制了其在某些区域需要更快关注的场景中的有效性。我们引入了优先级感知的MCPP（PA-MCPP）问题，其中环境的一个子集被指定为具有相关权重的优先区域。目标是以字典序最小化区域覆盖的总优先级加权延迟以及整体完成时间。为解决此问题，我们提出了一个可扩展的两阶段框架，该框架结合了（1）结合局部搜索的贪婪区域分配、基于生成树的路径规划，以及（2）斯坦纳树引导的剩余区域覆盖。在不同场景下的实验表明，与标准的MCPP基线方法相比，我们的方法显著降低了优先级加权延迟，同时保持了有竞争力的完成时间。敏感性分析进一步表明，该方法能很好地随机器人数量扩展，并且可以通过调整优先级权重有效控制区域覆盖行为。