A Novel Indicator for Quantifying and Minimizing Information Utility Loss of Robot Teams

TL;DR

This paper introduces a new metric called Loss of Information Utility (LoIU) to quantify and minimize information loss in robot teams, optimizing communication scheduling under bandwidth constraints to improve cooperative performance.

Contribution

The paper proposes a novel LoIU metric, a belief-based estimation method, and a semi-decentralized deep reinforcement learning framework for optimizing robot communication strategies.

Findings

LoIU effectively quantifies information freshness and utility.

The proposed method improves information utility by 98% over alternatives.

Simulation results validate the approach's effectiveness in dynamic environments.

Abstract

The timely exchange of information among robots within a team is vital, but it can be constrained by limited wireless capacity. The inability to deliver information promptly can result in estimation errors that impact collaborative efforts among robots. In this paper, we propose a new metric termed Loss of Information Utility (LoIU) to quantify the freshness and utility of information critical for cooperation. The metric enables robots to prioritize information transmissions within bandwidth constraints. We also propose the estimation of LoIU using belief distributions and accordingly optimize both transmission schedule and resource allocation strategy for device-to-device transmissions to minimize the time-average LoIU within a robot team. A semi-decentralized Multi-Agent Deep Deterministic Policy Gradient framework is developed, where each robot functions as an actor responsible for scheduling transmissions among its collaborators while a central critic periodically evaluates and refines the actors in response to mobility and interference. Simulations validate the effectiveness of our approach, demonstrating an enhancement of information freshness and utility by 98%, compared to alternative methods.