Synchronization of Local Plans for Cooperative Distributed Decision-Making

In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans may be inconsistent to local plans of other agents. This inconsistency leads to infeasibility of the plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making of multi-agent systems. The algorithm consists of two iterative steps: planning and synchronization. In the local planning step, the agents compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using synchronization. The synchronized plans act as reference decisions to the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that feasible decisions are achieved.