Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making

TL;DR

This paper presents a resource-aware algorithm for multi-robot decision-making that balances centralized and decentralized coordination, enabling robots with limited resources to perform near-optimal tasks involving submodular functions.

Contribution

It introduces the first algorithm that adaptively balances coordination trade-offs in resource-constrained multi-robot systems with theoretical guarantees.

Findings

Algorithm enables resource-efficient multi-robot coordination.

Provides provable guarantees for submodular maximization.

Validated in simulated image covering scenarios.

Abstract

Multi-robot decision-making is the process where multiple robots coordinate actions. In this paper, we aim for efficient and effective multi-robot decision-making despite the robots' limited on-board resources and the often resource-demanding complexity of their tasks. We introduce the first algorithm enabling the robots to choose with which few other robots to coordinate and provably balance the trade-off of centralized vs. decentralized coordination. Particularly, centralization favors globally near-optimal decision-making but at the cost of increased on-board resource requirements; whereas, decentralization favors minimal resource requirements but at a global suboptimality cost. All robots can thus afford our algorithm, irrespective of their resources. We are motivated by the future of autonomy that involves multiple robots coordinating actions to complete resource-demanding tasks, such as target tracking, area coverage, and monitoring. To provide closed-form guarantees, we focus on maximization problems involving monotone and 2nd-order submodular functions. To capture the cost of decentralization, we introduce the notion of Centralization Of Information among non-Neighbors (COIN). We validate our algorithm in simulated scenarios of image covering.