Memoryless Control Design for Persistent Surveillance under Safety Constraints

Abstract

This paper deals with the design of time-invariant memoryless control policies for robots that move in a finite two- dimensional lattice and are tasked with persistent surveillance of an area in which there are forbidden regions. We model each robot as a controlled Markov chain whose state comprises its position in the lattice and the direction of motion. The goal is to find the minimum number of robots and an associated time-invariant memoryless control policy that guarantees that the largest number of states are persistently surveilled without ever visiting a forbidden state. We propose a design method that relies on a finitely parametrized convex program inspired by entropy maximization principles. Numerical examples are provided.

References in corpus (1)

• Control Design for Markov Chains under Safety Constraints: A Convex Approach

Cited by in corpus (3)

• Correlated Orienteering Problem and it Application to Persistent Monitoring Tasks
• Persistent Monitoring of Events with Stochastic Arrivals at Multiple Stations
• Persistent Monitoring in Discrete Environments: Minimizing the Maximum Weighted Latency Between Observations