A Decentralized Learning Strategy to Restore Connectivity During Multi-Agent Formation Control

In this paper, we propose a decentralized learning algorithm to restore communication connectivity during multi-agent formation control. The time-varying connectivity profile of a mobile multi-agent system represents the dynamic information exchange capabilities among agents. While connected to the neighbors, each mobile agent in the proposed scheme learns to raise the team connectivity. When the inter-agent communication is lost, the associated trained neural network generates appropriate control actions to restore connectivity. The proposed learning technique leverages an adaptive control formalism, wherein a neural network tries to mimic the negative gradient of a value that relies on the agent-to-neighbor distances. All agents use the conventional consensus protocol during the connected multi-agent dynamics, and under communication loss, only the lost agent executes the neural network predicted actions to come back to the fleet. Simulation results demonstrate the effectiveness of our proposed approach for single/multiple agent loss even in the presence of velocity disturbances.