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

Multi-robot decision-making is the process where multiple robots coordinate actions. In this paper, we aim for scalable and reliable multi-robot decision-making despite the robots' limited on-board resources and the resource-demanding complexity of their tasks. We introduce the first algorithm that enables robots to choose with which other robots to coordinate, balancing 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 and area covering. To provide closed-form characterizations, we focus on maximization problems involving monotone and "doubly" 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.