Failure-Resilient Multi-Robot Coordination with $k$-Connectivity Constrained Submodular Maximization

Key points

• Multi-robot coordination is essential in applications such as coverage, target tracking, and information gathering, where robots must jointly select actions to maximize team performance.1
• Submodular maximization provides an effective framework for such coordination, and resilient variants further account for robot failures or adversarial removals.1
• Our method achieves a worst-case post-failure objective within a factor of $[\max(1-νf(\mathcal{I}),h(N,β))]^2 ρ\frac{1}{κf}(1-e^{-κf})$ of the optimal connectivity-respecting resilient assignment, while recovering the classical unconstrained guarantee in the appropriate limits.1
• Experiments on multi-robot target tracking validate the proposed method and characterize the practical impact of connectivity constraints.1

Sources (1)

• [1]Failure-Resilient Multi-Robot Coordination with $k$-Connectivity Constrained Submodular Maximization
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 06:04 AM

  To address this issue, we introduce Resilient $k$-Connected Submodular Maximization, which jointly considers task performance, resilience, and communication connectivity.

  Multi-robot coordination is essential in applications such as coverage, target tracking, and information gathering, where robots must jointly select actions to maximize team performance.

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