Markov Chain-Based Stochastic Strategies for Robotic Surveillance
It provides a review of incremental advancements in strategy design for persistent robotic surveillance tasks, targeting researchers in robotics and stochastic control.
The paper surveys stochastic strategies for robotic surveillance, focusing on Markov chain-based motion models and optimization problems related to hitting times for efficiency metrics like speed and unpredictability.
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.