提出新指标量化机器人团队信息时效性损失，提升协作效率。
A Novel Indicator for Quantifying and Minimizing Information Utility Loss of Robot Teams
- 用信息效用损失度量评估协作关键信息的时效与价值
- 在带宽受限下使信息新鲜度与实用性提升98%
- 适合需高效协同通信的移动机器人系统研究者
机器人团队中信息的及时交换至关重要，但常受无线容量限制。信息传递延迟会导致估计误差，影响协作效果。本文提出一种名为信息效用损失（LoIU）的新指标，用于量化对合作至关重要的信息的新鲜度与实用性。该指标使机器人能在带宽受限条件下优先传输关键信息。我们基于信念分布估算LoIU，进而优化设备到设备通信下的传输调度与资源分配策略，以最小化团队的平均时间LoIU。开发了一种半分布式多智能体深度确定性策略梯度框架，每个机器人作为执行者负责协调其合作方的传输，中央评论者则周期性评估并根据移动性和干扰情况优化执行者。仿真验证了方法的有效性，相比其他方法，信息新鲜度和实用性提升了98%。
原文摘要 · Abstract (English)
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.
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