论文标题
基于马尔可夫链的机器人监视的随机策略
Markov Chain-Based Stochastic Strategies for Robotic Surveillance
论文作者
论文摘要
本文调查了持续的机器人监视任务的策略设计的最新进步，重点是随机方法。该问题描述了移动机器人如何以有效的方式随机巡逻图形，以在相关的基础性能指标方面定义效率。首先，我们首先回顾马尔可夫链的基础，这是随机机器人监视的主要运动模型。然后讨论了有关监视策略的速度和不可预测性的两个主要标准。整个治疗过程中出现的中心物体是马尔可夫连锁店的打击时间，它们的分布和期望。我们根据不同场景中相关指标制定各种优化问题，并确定其各自的属性。
This article surveys recent advancements of strategy designs for persistent robotic surveillance tasks with the focus on stochastic approaches. The problem describes how mobile robots stochastically patrol a graph in an efficient way where the efficiency is defined with respect to relevant underlying performance metrics. We first start by reviewing the basics of Markov chains, which is the primary motion model for stochastic robotic surveillance. Then two main criteria regarding the speed and unpredictability of surveillance strategies are discussed. The central objects that appear throughout the treatment is the hitting times of Markov chains, their distributions and expectations. We formulate various optimization problems based on the concerned metrics in different scenarios and establish their respective properties.
