2 research outputs found
Markov Chain-Based Stochastic Strategies for Robotic Surveillance
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
Stochastic Strategies for Robotic Surveillance as Stackelberg Games
This paper studies a stochastic robotic surveillance problem where a mobile
robot moves randomly on a graph to capture a potential intruder that
strategically attacks a location on the graph. The intruder is assumed to be
omniscient: it knows the current location of the mobile agent and can learn the
surveillance strategy. The goal for the mobile robot is to design a stochastic
strategy so as to maximize the probability of capturing the intruder. We model
the strategic interactions between the surveillance robot and the intruder as a
Stackelberg game, and optimal and suboptimal Markov chain based surveillance
strategies in star, complete and line graphs are studied. We first derive a
universal upper bound on the capture probability, i.e., the performance limit
for the surveillance agent. We show that this upper bound is tight in the
complete graph and further provide suboptimality guarantees for a natural
design. For the star and line graphs, we first characterize dominant strategies
for the surveillance agent and the intruder. Then, we rigorously prove the
optimal strategy for the surveillance agent