Communication-Free Distributed Coverage for Networked Systems

TL;DR

This paper introduces a decentralized, communication-free algorithm enabling mobile agents to collaboratively cover a network graph by relying solely on local sensing, formulated through a game-theoretic approach.

Contribution

It presents a novel communication-free learning algorithm for distributed network coverage, advancing decentralized control methods for mobile agents.

Findings

Algorithm effectively maximizes coverage without communication.

Agents adaptively optimize their positions based on local sensing.

The approach is validated through theoretical analysis and simulations.

Abstract

In this paper, we present a communication-free algorithm for distributed coverage of an arbitrary network by a group of mobile agents with local sensing capabilities. The network is represented as a graph, and the agents are arbitrarily deployed on some nodes of the graph. Any node of the graph is covered if it is within the sensing range of at least one agent. The agents are mobile devices that aim to explore the graph and to optimize their locations in a decentralized fashion by relying only on their sensory inputs. We formulate this problem in a game theoretic setting and propose a communication-free learning algorithm for maximizing the coverage.