论文标题
带有多个机器人的线覆盖范围：算法和实验
Line Coverage with Multiple Robots: Algorithms and Experiments
论文作者
论文摘要
线覆盖范围问题涉及通过一个或多个资源约束机器人找到线性特征的有效途径。线性具有模型环境，例如道路网络，电力线以及石油和天然气管道。为机器人定义了两种旅行模式：维修和陷入困境。机器人服务功能如果它执行特定于任务的操作，例如拍摄图像，则它可以遍历该功能；否则，它是无聊的。遍历环境会产生成本（例如旅行时间）和对资源的需求（例如电池寿命）。维修和无人机的成本和需求功能可能取决于方向。环境被建模为图形，并提供了整数线性程序。由于问题是NP-HARD，我们设计了一种快速有效的启发式算法，即合并 - 结合物（MEM）。利用MEM算法的建设性特性，开发了具有多个仓库的大图的线覆盖算法。此外，有效地纳入了算法中的转折成本和非语言限制。该算法是在道路网络上基准测试的，并在使用空中机器人的实验中证明了这一点。
The line coverage problem involves finding efficient routes for the coverage of linear features by one or more resource-constrained robots. Linear features model environments like road networks, power lines, and oil and gas pipelines. Two modes of travel are defined for robots: servicing and deadheading. A robot services a feature if it performs task-specific actions, such as 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, which can be direction-dependent. The environment is modeled as a graph, and an integer linear program is provided. As the problem is NP-hard, we design a fast and efficient heuristic algorithm, Merge-Embed-Merge (MEM). Exploiting the constructive property of the MEM algorithm, algorithms for line coverage of large graphs with multiple depots are developed. Furthermore, turning costs and nonholonomic constraints are efficiently incorporated into the algorithm. The algorithms are benchmarked on road networks and demonstrated in experiments with aerial robots.
