Decentralized Event-Driven Algorithms for Multi-Agent Persistent Monitoring

TL;DR

This paper develops decentralized event-driven algorithms for multi-agent persistent monitoring, enabling agents to optimize target monitoring with minimal communication, based on hybrid system analysis and Infinitesimal Perturbation Analysis.

Contribution

It introduces a novel decentralized approach to optimal multi-agent monitoring using hybrid system analysis and event-driven gradient optimization.

Findings

The decentralized algorithm closely approximates centralized solutions.

Agents can optimize trajectories with mostly local information.

Simulation results demonstrate effectiveness of the proposed methods.

Abstract

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 movement in order to minimize an uncertainty metric associated with the targets. In a one-dimensional setting, it has been shown that the optimal solution can be reduced to a simpler parametric optimization problem 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 a complete on-line solution through an event-driven centralized gradient-based algorithm. We show that the IPA gradient can be recovered in a distributed manner in which each agent optimizes its trajectory 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.