Computation and Communication Co-Design for Real-Time Monitoring and Control in Multi-Agent Systems

TL;DR

This paper presents a co-design framework for computation and communication in multi-agent systems, optimizing local processing and scheduling to improve real-time monitoring and control under resource constraints.

Contribution

It introduces a novel formulation using Age of Information and develops efficient algorithms with Whittle index for joint optimization of processing and scheduling.

Findings

Achieved 18-82% performance improvement in practical applications

Developed a general formulation for delay-accuracy tradeoff in multi-agent systems

Demonstrated effectiveness in occupancy mapping and autonomous vehicle ride sharing

Abstract

We investigate the problem of co-designing computation and communication in a multi-agent system (e.g. a sensor network or a multi-robot team). We consider the realistic setting where each agent acquires sensor data and is capable of local processing before sending updates to a base station, which is in charge of making decisions or monitoring phenomena of interest in real time. Longer processing at an agent leads to more informative updates but also larger delays, giving rise to a delay-accuracy-tradeoff in choosing the right amount of local processing at each agent. We assume that the available communication resources are limited due to interference, bandwidth, and power constraints. Thus, a scheduling policy needs to be designed to suitably share the communication channel among the agents. To that end, we develop a general formulation to jointly optimize the local processing at the agents and the scheduling of transmissions. Our novel formulation leverages the notion of Age of Information to quantify the freshness of data and capture the delays caused by computation and communication. We develop efficient resource allocation algorithms using the Whittle index approach and demonstrate our proposed algorithms in two practical applications: multi-agent occupancy grid mapping in time-varying environments, and ride sharing in autonomous vehicle networks. Our experiments show that the proposed co-design approach leads to a substantial performance improvement (18-82% in our tests).