Coverage path planning is a fundamental challenge in robotics, with diverse applications in aerial surveillance, manufacturing, cleaning, inspection, agriculture, and more. The main objective is to devise a trajectory for an agent that efficiently covers a given area, while minimizing time or energy consumption. Existing practical approaches often lack a solid theoretical foundation, relying on purely heuristic methods, or overly abstracting the problem to a simple Traveling Salesman Problem in Grid Graphs. Moreover, the considered cost functions only rarely consider turn cost, prize-collecting variants for uneven cover demand, or arbitrary geometric regions. In this paper, we describe an array of systematic methods for handling arbitrary meshes derived from intricate, polygonal environments. This adaptation paves the way to compute efficient coverage paths with a robust theoretical foundation for real-world robotic applications. Through comprehensive evaluations, we demonstrate that the algorithm also exhibits low optimality gaps, while efficiently handling complex environments. Furthermore, we showcase its versatility in handling partial coverage and accommodating heterogeneous passage costs, offering the flexibility to trade off coverage quality and time efficiency.

翻译：覆盖路径规划是机器人领域的一项基础挑战，广泛应用于空中监视、制造、清洁、检测、农业等领域。其主要目标是为智能体设计一条能高效覆盖给定区域并最小化时间或能量消耗的轨迹。现有实用方法往往缺乏扎实的理论基础，要么依赖纯启发式方法，要么将问题过度抽象为网格图中的简单旅行商问题。此外，所考虑的成本函数极少涉及转弯成本、针对非均匀覆盖需求的奖励收集变体或任意几何区域。本文描述了一系列系统化方法，用于处理源自复杂多边形环境中的任意网格。这一适应为基于稳健理论基础计算高效覆盖路径铺平了道路，可应用于实际机器人任务。通过全面评估，我们证明该算法在高效处理复杂环境的同时，也具有低最优性间隙。此外，我们展示了其在处理部分覆盖和适应异质通行成本方面的多功能性，提供了在覆盖质量与时间效率之间进行权衡的灵活性。