On Game based Distributed Approach for General Multi-agent Optimal Coverage with Application to UAV Networks

TL;DR

This paper introduces a game-based distributed approach for multi-agent optimal coverage, specifically applied to UAV networks, achieving near-optimal coverage with reduced computational costs and faster convergence.

Contribution

It proposes a novel decentralized game-theoretic algorithm for multi-agent coverage that requires only local information and converges efficiently to near-optimal solutions.

Findings

Requires less computational time than existing distributed algorithms.

Achieves faster convergence compared to other methods.

Provides approximate solutions close to centralized optimization.

Abstract

This paper focuses on the optimal coverage problem (OCP) for multi-agent systems with a decentralized optimization mechanism. A game based distributed decision-making method for the multi-agent OCP is proposed to address the high computational costs arising from the large scale of the multi-agent system and to ensure that the game's equilibrium achieves the global performance objective's maximum value. In particular, a distributed algorithm that needs only local information is developed and proved to converge to near-optimal global coverage. Finally, the proposed method is applied to maximize the coverage area of the UAV network for a target region. The simulation results show that our method can require much less computational time than other typical distributed algorithms in related work, while achieving a faster convergence rate. Comparison with centralized optimization also demonstrates that the proposed method has approximate optimization results and high computation efficiency.