Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making
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We introduce the first algorithm for distributed decision-making that provably balances the trade-off of centralization, for global near-optimality, vs. decentralization, for near-minimal on-board computation, communication, and memory resources. We are motivated by the future of autonomy that involves heterogeneous robots collaborating in complex tasks, such as image covering, target tracking, and area monitoring. Current algorithms, such as consensus algorithms, are insufficient to fulfill this future: they achieve distributed communication only, at the expense of high communication, computation, and memory overloads. A shift to resource-aware algorithms is needed, that can account for each robot's on-board resources, independently. We provide the first resource-aware algorithm, Resource-Aware distributed Greedy (RAG). We focus on maximization problems involving monotone and "doubly" submodular functions, a diminishing returns property. RAG has near-minimal on-board resource requirements. Each agent can afford to run the algorithm by adjusting the size of its neighborhood, even if that means selecting actions in complete isolation. RAG has provable approximation performance, where each agent can independently determine its contribution. All in all, RAG is the first algorithm to quantify the trade-off of centralization, for global near-optimality, vs. decentralization, for near-minimal on-board resource requirements. To capture the trade-off, we introduce the notion of Centralization Of Information among non-Neighbors (COIN). We validate RAG in simulated scenarios of image covering with mobile robots.
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