Resilient and Decentralized Control of Multi-level Cooperative Robotic Networks to Maintain Connectivity under Adversarial Attacks

Network connectivity plays an important role in the information exchange between different agents in the multi-level networks. In this paper, we establish a game-theoretic framework to capture the uncoordinated nature of the decision making at different layers of the multi-level networks. To study the network resiliency, we introduce two adversarial attack models and quantify their impacts, and design a decentralized and resilient alternating-play algorithm that aims to maximize the algebraic connectivity of the global network under attack. We show that the designed algorithm converges to a Nash equilibrium in a finite number of steps, and yields an equilibrium network. Moreover, simulation results of a two-layer mobile robotic networks corroborate and show the interdependency between different layers of networks in the recovery process.