Decentralized event-driven algorithms for multi-agent persistent monitoring tasks
Nan Run Zhou, Christos G. Cassandras, Xi Yu, Sean B. Andersson · 2017
We address the issue of identifying conditions under which the centralized solution to the optimal multi-agent persistent monitoring problem can be recovered in a decentralized event-driven manner. In this problem, multiple agents interact with a finite number of targets and the objective is to control their movements in order to minimize an uncertainty metric associated with the targets. In one-dimensional settings, it has been shown that the optimal solution can be reduced to a simpler parametric one and that the behavior of agents under optimal control is described by a hybrid system. This hybrid system can be analyzed using Infinitesimal Perturbation Analysis (IPA) to obtain an on-line solution through an event-driven centralized gradient-based algorithm. We show that the IPA gradient can be recovered in a distributed manner based on local information, except for one event requiring communication from a non-neighbor agent. Simulation examples are included to illustrate the effectiveness of this “almost decentralized” algorithm and its fully decentralized counterpart where the aforementioned non-local event is ignored.