Markov Chain-Based Stochastic Strategies for Robotic Surveillance

TL;DR

This paper reviews recent stochastic strategy designs for robotic surveillance using Markov chains, focusing on optimizing speed and unpredictability through hitting times and related metrics.

Contribution

It provides a comprehensive survey of Markov chain-based stochastic strategies, analyzing their properties and formulating optimization problems for robotic surveillance.

Findings

Analysis of hitting times and their distributions.

Formulation of optimization problems for surveillance metrics.

Discussion of strategies balancing speed and unpredictability.

Abstract

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.