The Price of Decentralization: Event-Driven Optimization for Multiagent Persistent Monitoring Tasks

In persistent monitoring tasks, the objective is to control the movements of cooperating agents in order to minimize an uncertainty metric associated with a finite number of targets. We formulate an optimal control problem and show that the optimal solution can be reduced to or approximated by parametric agent trajectory families. The behavior of agents and targets under optimal control can be described by a hybrid system. This enables the use of infinitesimal perturbation analysis to obtain an online centralized solution through a gradient-based algorithm. We identify conditions under which this centralized solution to the parametric optimization problems can be recovered in a decentralized and event-driven manner. In the decentralized scheme, each agent optimizes its performance based on local information, except for one type of nonlocal event requiring communication from a nonneighbor agent, giving rise to a quantifiable price of decentralization. Simulation examples are included to illustrate the effectiveness of this almost decentralized optimization algorithm and compare it to its fully decentralized counterpart where the aforementioned nonlocal event is ignored.

Automated summary

This study explores the optimal control problem of cooperating agents in persistent monitoring tasks, proposing a method to describe the behavior of agents and targets using a hybrid system and obtaining an online centralized solution through infinitesimal perturbation analysis.