Asynchronous Consensus ADMM for Multi-Agent Task Assignment: Distributed and Decentralized Architectures

Abstract

This paper proposes a distributed asynchronous consensus ADMM (C-ADMM) algorithm for multi-agent task assignment. The strict global synchronization requirements of the conventional C-ADMM are relaxed through the introduction of partial barriers and bounded delays. The algorithm is implemented in two variants: a Ground Control Station (GCS)-based Distributed Asynchronous C-ADMM and a Decentralized Asynchronous C-ADMM that exchanges state information directly among agents. Both variants discard delay information that exceeds a bounded delay threshold and improve computational efficiency by allowing each agent to update without waiting for simultaneous responses from neighbors. MATLAB simulations with 10 agents demonstrate that the Distributed Asynchronous C-ADMM reduces computation time by 40-45% with only a 4-10% loss in optimality compared to the original C-ADMM-based MUR-TAP. The Decentralized Asynchronous C-ADMM reduces computation time by 46-52% at the expense of a 12-37% loss in optimality. These results demonstrate a flexible trade-off between convergence speed and solution accuracy in realistic distributed environments subject to network delays and information loss.