Submodular Optimization for Coupled Task Allocation and Intermittent Deployment Problems

Abstract

In this paper, we demonstrate a formulation for optimizing coupled submodular maximization problems with provable sub-optimality bounds. In robotics applications, it is quite common that optimization problems are coupled with one another and therefore cannot be solved independently. Specifically, we consider two problems coupled if the outcome of the first problem affects the solution of a second problem that operates over a longer time scale. For example, in our motivating problem of environmental monitoring, we posit that multi-robot task allocation will potentially impact environmental dynamics and thus influence the quality of future monitoring, here modeled as a multi-robot intermittent deployment problem. The general theoretical approach for solving this type of coupled problem is demonstrated through this motivating example. Specifically, we propose a method for solving coupled problems modeled by submodular set functions with matroid constraints. A greedy algorithm for solving this class of problem is presented, along with sub-optimality guarantees. Finally, practical optimality ratios are shown through Monte Carlo simulations to demonstrate that the proposed algorithm can generate near-optimal solutions with high efficiency.

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• Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization
• Distributed Resilient Submodular Action Selection in Adversarial Environments
• Monitoring Over the Long Term: Intermittent Deployment and Sensing Strategies for Multi-Robot Teams
• Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection