Communication-Aware Multi-robot Coordination with Submodular Maximization

TL;DR

This paper introduces a novel approach to multi-robot task planning that balances submodular maximization objectives with communication connectivity constraints, ensuring effective coordination without sacrificing network connectivity.

Contribution

It formulates the Communication-aware Submodular Maximization (CSM) problem and proposes a two-stage heuristic algorithm to jointly optimize task performance and communication connectivity.

Findings

The proposed algorithm maintains near-greedy performance levels.

Numerical simulations validate the effectiveness of the approach.

The method balances submodular maximization with connectivity constraints.

Abstract

Submodular maximization has been widely used in many multi-robot task planning problems including information gathering, exploration, and target tracking. However, the interplay between submodular maximization and communication is rarely explored in the multi-robot setting. In many cases, maximizing the submodular objective may drive the robots in a way so as to disconnect the communication network. Driven by such observations, in this paper, we consider the problem of maximizing submodular function with connectivity constraints. Specifically, we propose a problem called Communication-aware Submodular Maximization (CSM), in which communication maintenance and submodular maximization are jointly considered in the decision-making process. One heuristic algorithm that consists of two stages, i.e. \textit{topology generation} and \textit{deviation minimization} is proposed. We validate the formulation and algorithm through numerical simulation. We find that our algorithm on average suffers only slightly performance decrease compared to the pure greedy strategy.