Memoryless Control Design for Persistent Surveillance under Safety Constraints
This addresses the challenge of efficient and safe robot surveillance in constrained environments, but it appears incremental as it builds on existing control and optimization frameworks.
The paper tackles the problem of designing time-invariant memoryless control policies for robots performing persistent surveillance in a lattice with forbidden regions, aiming to minimize the number of robots while maximizing surveilled states without safety violations, and proposes a method based on a convex program with numerical examples.
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.