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.

翻译：多机器人决策是指多个机器人协调行动的过程。本文旨在实现高效且有效的多机器人决策，尽管机器人机载资源有限且任务复杂度通常对资源要求较高。我们提出了首个算法，使机器人能够选择与少数其他机器人进行协调，并能够可证明地平衡集中式与分散式协调之间的权衡。具体而言，集中式有利于实现全局近似最优的决策，但代价是增加了机载资源需求；而分散式则有利于最小化资源需求，但需承担全局次优性的代价。因此，无论资源如何，所有机器人都能负担得起我们的算法。我们的研究动机源于自主性的未来，即多个机器人协调行动以完成资源密集型任务，例如目标跟踪、区域覆盖和监测。为了提供封闭形式的保证，我们专注于涉及单调和二阶次模函数的最大化问题。为了捕捉分散化的代价，我们引入了非邻居间信息集中化的概念。我们在图像覆盖的模拟场景中验证了我们的算法。