Distributed Algorithm with Emergent Area Partitioning and Base Station's Situation Awareness for Multi-Robot Patrolling

TL;DR

This paper introduces LR-PT, a novel multi-robot patrolling algorithm that improves efficiency, situation awareness, and robustness through emergent area partitioning and local decision-making.

Contribution

The study presents the LR-PT algorithm, which autonomously emerges area partitions and enhances patrol efficiency and operator awareness in multi-robot systems.

Findings

LR-PT outperforms existing methods in simulation.

Robots select patrol targets based on local information.

The algorithm demonstrates robustness to communication constraints.

Abstract

Patrolling with multiple robots offers efficient surveillance to detect and manage undesired situations. This necessitates improved patrol efficiency and operator situation awareness at base stations. Enhanced situation awareness enables operators to predict robots' behaviors, support recognition and decision-making, and execute emergency interventions. This study presents the Local Reactive and Partition (LR-PT) algorithm, a novel multi-robot patrolling approach. In simulations, LR-PT outperformed existing methods by ensuring frequent patrols of all locations of interest and enhancing the situation awareness of the base station. Robots independently select patrol targets based on locally available information, integrating patrol needs and the urgency of reporting mission progress to the base station into a unified utility function. This locality also contributes to robustness against communication constraints and robot failures, as demonstrated in this research. The algorithm further autonomously emerged the area partition, which can avoid falling into local optima and realize the comprehensive patrol over the whole mission area. The simulation results demonstrated the superior performance of LR-PT for multi-robot patrolling, utilizing the advantages of swarm robotics and addressing real-world operational challenges.