Optimal Threshold-Based Control Policies for Persistent Monitoring on Graphs

TL;DR

This paper introduces a distributed threshold-based control policy for multi-agent persistent monitoring on graphs, enabling near-optimal, real-time decision-making to minimize node uncertainties.

Contribution

It proposes a novel class of distributed threshold-based controllers and applies Infinitesimal Perturbation Analysis for online optimization of these policies.

Findings

Threshold-based controllers effectively reduce node uncertainty.

IPA gradient is monotonic in single-agent scenarios.

Simulation results compare favorably with dynamic programming solutions.

Abstract

We consider the optimal multi-agent persistent monitoring problem defined by a team of cooperating agents visiting a set of nodes (targets) on a graph with the objective of minimizing a measure of overall node state uncertainty. The solution to this problem involves agent trajectories defined both by the sequence of nodes to be visited by each agent and the amount of time spent at each node. Since such optimal trajectories are generally intractable, we propose a class of distributed threshold-based parametric controllers through which agent transitions from one node to the next are controlled by threshold parameters on the node uncertainty states. The resulting behavior of the agent-target system can be described by a hybrid dynamic system. This enables the use of Infinitesimal Perturbation Analysis (IPA) to determine on line (locally) optimal threshold parameters through gradient descent methods and thus obtain optimal controllers within this family of threshold-based policies. We further show that in a single-agent case the IPA gradient is monotonic, which implies a simple structure whereby an agent visiting a node should reduce the uncertainty state to zero before moving to the next node. Simulation examples are included to illustrate our results and compare them to optimal solutions derived through dynamic programming when this is possible.