A Novel Indicator for Quantifying and Minimizing Information Utility Loss of Robot Teams
🤖 AI Summary
To address the degradation of information timeliness and collaborative utility in multi-robot systems under constrained wireless bandwidth, this paper introduces the Loss of Information Utility (LoIU) metric—the first unified measure jointly quantifying information freshness and task-level collaborative utility. Building upon LoIU, we propose a semi-decentralized multi-agent Deep Deterministic Policy Gradient (DDPG) framework that jointly optimizes Device-to-Device (D2D) transmission scheduling and spectrum resource allocation. Within this framework, each robot makes distributed decisions based on its local belief distribution, while a central controller coordinates policy updates to accommodate dynamic network topologies. Simulation results demonstrate that the proposed method improves the joint performance of information freshness and collaborative utility by 98%, significantly outperforming existing approaches based solely on Age of Information (AoI) or isolated utility modeling.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Quantify information utility loss in robot teams
Optimize transmission under bandwidth constraints
Enhance information freshness and utility
Innovation
Methods, ideas, or system contributions that make the work stand out.
LoIU metric quantifies information freshness and utility
Semi-decentralized MADDPG optimizes transmission scheduling
Belief distributions estimate LoIU for resource allocation
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