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 dur-ing 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 neigh-bors, 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 pre-dicted 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.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a decentralized learning algorithm is proposed to restore communication connectivity in multi-agent formation control. The proposed scheme enables each mobile agent to raise the team connectivity by learning while connected to neighbors. When inter-agent communication is lost, a trained neural network generates control actions to restore connectivity. The approach leverages an adaptive control formalism and simulation results show its effectiveness even in the presence of velocity disturbances.