带有移动传感器的预算扫描覆盖问题的常数近似算法
A Constant-Approximation Algorithm for Budgeted Sweep Coverage with Mobile Sensors
AI总结：
本文提出了首个针对预算扫描覆盖问题（BSC）的常数近似算法，通过开发多定向问题（MOP）的常数近似算法并将其应用于BSC，为优化移动传感器部署及相关组合优化任务开辟了新可能。
摘要：
本文提出了首个针对预算扫描覆盖问题（BSC）的常数近似算法。BSC旨在为多个移动传感器（又称机器人）设计路线，以从兴趣点（PoIs）周期性地收集尽可能多的信息。为解决该问题，我们提出首先研究多定向问题（MOP）。MOP旨在寻找一组 $m$ 条顶点不相交的路径，在满足预算约束 $B$ 的条件下覆盖尽可能多的顶点。我们为MOP开发了一种常数近似算法，并利用它实现BSC的常数近似。我们的发现为优化移动传感器部署及相关组合优化任务开辟了新可能。
英文摘要：
In this paper, we present the first constant-approximation algorithm for {\em budgeted sweep coverage problem} (BSC). The BSC involves designing routes for a number of mobile sensors (a.k.a. robots) to periodically collect information as much as possible from points of interest (PoIs). To approach this problem, we propose to first examine the {\em multi-orienteering problem} (MOP). The MOP aims to find a set of $m$ vertex-disjoint paths that cover as many vertices as possible while adhering to a budget constraint $B$. We develop a constant-approximation algorithm for MOP and utilize it to achieve a constant-approximation for BSC. Our findings open new possibilities for optimizing mobile sensor deployments and related combinatorial optimization tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
