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[2204.07520] Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making
Summary
This paper presents a novel algorithm for resource-aware distributed submodular maximization, enhancing multi-robot decision-making by balancing centralized and decentralized coordination.
Why It Matters
As robotics technology advances, efficient multi-robot coordination is crucial for tasks like monitoring and tracking. This research addresses the challenge of limited resources in robots, providing a framework that allows for effective decision-making while considering resource constraints.
Key Takeaways
- Introduces an algorithm that balances centralized and decentralized coordination for multi-robot systems.
- Focuses on maximizing monotone and 2nd-order submodular functions under resource constraints.
- Validates the algorithm through simulations, demonstrating its effectiveness in resource-demanding tasks.
Mathematics > Optimization and Control arXiv:2204.07520 (math) [Submitted on 15 Apr 2022 (v1), last revised 23 Feb 2026 (this version, v4)] Title:Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making Authors:Zirui Xu, Vasileios Tzoumas View a PDF of the paper titled Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making, by Zirui Xu and 1 other authors View PDF HTML (experimental) 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 ...