The line coverage problem is to find efficient routes for coverage of linear features by one or more resource-constrained robots. Linear features model environments such as road networks, power lines, and oil and gas pipelines. We define two modes of travel for robots: servicing and deadheading. A robot services a feature if it performs task-specific actions, e.g., taking images, as it traverses the feature; otherwise, it is deadheading. Traversing the environment incurs costs (e.g., travel time) and demands on resources (e.g., battery life). Servicing and deadheading can have different cost and demand functions, and we further permit them to be direction dependent. We model the environment as a graph and provide an integer linear program. As the problem is NP-hard, we develop a fast and efficient heuristic algorithm, Merge-Embed-Merge (MEM). By exploiting the constructive property of the MEM algorithm, we develop algorithms for line coverage of large graphs with multiple depots. Furthermore, we efficiently incorporate turning costs and nonholonomic constraints into the algorithm. We benchmark the algorithms on road networks and demonstrate them in experiments using aerial robots.

翻译：线性覆盖问题旨在为单个或多个资源受限的机器人寻找覆盖线性特征的高效路径。线性特征可模拟道路网络、输电线路及油气管道等环境。我们定义机器人的两种行进模式：作业行驶与空载行驶。机器人在行进过程中执行特定任务操作（如拍摄图像）时即为作业行驶，否则为空载行驶。环境遍历会产生成本（如行驶时间）和资源消耗（如电池寿命）。作业行驶与空载行驶可具有不同的成本和消耗函数，且允许其具有方向依赖性。我们将环境建模为图结构，并构建整数线性规划模型。由于该问题属于NP难问题，我们开发了一种快速高效的启发式算法——合并-嵌入-合并（MEM）。通过利用MEM算法的构造特性，我们提出了适用于多基地大规模图的线性覆盖算法。此外，我们高效地将转弯成本和非完整约束融入算法。我们在道路网络上进行算法基准测试，并利用空中机器人进行实验验证。