Event-Driven Receding Horizon Control of Energy-Aware Dynamic Agents For Distributed Persistent Monitoring

TL;DR

This paper presents an event-driven receding horizon control approach for energy-aware agents to perform persistent monitoring on networks, optimizing energy use and node state uncertainty in a distributed, efficient manner.

Contribution

It extends existing RHC methods by incorporating agent motion dynamics and energy consumption, providing a more realistic and efficient control strategy.

Findings

Improved energy efficiency over energy-agnostic methods

Distributed and computationally efficient control solution

Enhanced monitoring performance with dynamic agent models

Abstract

This paper addresses the persistent monitoring problem defined on a network where a set of nodes (targets) needs to be monitored by a team of dynamic energy-aware agents. The objective is to control the agents' motion to jointly optimize the overall agent energy consumption and a measure of overall node state uncertainty, evaluated over a finite period of interest. To achieve these objectives, we extend an established event-driven Receding Horizon Control (RHC) solution by adding an optimal controller to account for agent motion dynamics and associated energy consumption. The resulting RHC solution is computationally efficient, distributed and on-line. Finally, numerical results are provided highlighting improvements compared to an existing RHC solution that uses energy-agnostic first-order agents.