论文标题
在随机场中的机器人游览算法的近似算法，并保证了估计精度
Approximation Algorithms for Robot Tours in Random Fields with Guaranteed Estimation Accuracy
论文作者
论文摘要
我们研究了由绘制以固定随机字段建模的映射环境现象的机器人的样品放置和最短的旅行问题。目的是最大程度地减少所使用的资源（样品或旅行长度），同时保证估计准确性。我们为凸环境中两个问题提供了近似算法。这些改善了以前已知的结果，无论是在理论保证还是模拟中。此外，我们在文献中关于下限的现有主张，以解决样本放置问题的解决方案。
We study the sample placement and shortest tour problem for robots tasked with mapping environmental phenomena modeled as stationary random fields. The objective is to minimize the resources used (samples or tour length) while guaranteeing estimation accuracy. We give approximation algorithms for both problems in convex environments. These improve previously known results, both in terms of theoretical guarantees and in simulations. In addition, we disprove an existing claim in the literature on a lower bound for a solution to the sample placement problem.
