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.
Abstract (translated)
团队中的机器人之间及时交换信息至关重要，但这种交流可能会受到有限无线容量的限制。无法及时传递信息会导致估算错误，进而影响机器人的协作效果。在本文中，我们提出了一种新的衡量标准，称为信息效用损失（Loss of Information Utility, LoIU），用于量化对合作至关重要的信息的新鲜度和实用性。该指标使机器人能够在带宽约束下优先处理信息传输。我们还提出了使用信念分布来估计LoIU，并相应地优化设备间传输的传输计划和资源分配策略，以最小化团队中机器人的时间平均LoIU。为了实现这一目标，开发了一种半分散式的多智能体深度确定性政策梯度框架，在该框架下每个机器人作为一个行动者负责在协作伙伴之间调度传输，而一个中央评判者会定期评估并改进这些行动者以响应移动性和干扰的变化。仿真结果验证了我们方法的有效性，与替代方法相比，信息的新鲜度和实用性提高了98%。
URL
https://arxiv.org/abs/2506.14237
