# Multi-Robot Patrolling with Sensing Idleness and Data Delay Objectives

## TL;DR

This paper addresses multi-robot patrolling by optimizing idleness and data delay, analyzing movement strategies, proving NP-hardness, and proposing heuristics that improve data delivery efficiency in surveillance tasks.

## Contribution

It introduces a joint optimization framework for idleness and data delay in multi-robot patrolling, including complexity analysis and heuristic solutions.

## Key findings

- Heuristic methods outperform uncooperative strategies in simulations.
- Minimizing data delay is NP-hard, requiring heuristic approaches.
- Cooperative data exchange improves overall patrolling efficiency.

## Abstract

Multi-robot patrolling represents a fundamental problem for many monitoring and surveillance applications and has gained significant interest in recent years. In patrolling, mobile robots repeatedly travel through an environment, capture sensor data at certain sensing locations and deliver this data to the base station in a way that maximizes the changes of detection. Robots move on tours, exchange data when they meet with robots on neighboring tours and so eventually deliver data to the base station. In this paper we jointly consider two important optimization criteria of multi-robot patrolling: (i) idleness, i.e. the time between consecutive visits of sensing locations, and (ii) delay, i.e. the time between capturing data at the sensing location and its arrival at the base station. We systematically investigate the effect of the robots' moving directions along their tours and the selection of meeting points for data exchange. We prove that the problem of determining the movement directions and meeting points such that the data delay is minimized is NP-hard. We propose heuristics and provide a simulation study which shows that the cooperative approach can outperform an uncooperative approach where every robot delivers the captured data individually to the base station.