多智能体水下机器人实时构建通信成功率地图
Decentralized Gaussian Process Classification and an Application in Subsea Robotics
- 各机器人共享部分通信数据，协同构建通信成功率分布图
- 实测验证：在真实水下环境中显著提升通信预测精度
- 适合水下集群导航与通信优化研究者参考
多台自主水下航行器（AUV）依赖声学通信进行协作，但该通信方式受限于传输距离短、多径效应和低带宽。为应对通信不确定性，本文提出一种实时学习通信环境的方法，解决团队机器人在运行中实时构建从一位置到另一位置的通信成功概率地图的问题。这是一个去中心化的分类任务——通信事件非成功即失败——各AUV共享其通信测量数据的一部分以共同构建地图。本工作的核心贡献是推导出一种严格的测量数据共享策略，用于选择最优共享数据。通过使用弗吉尼亚理工690型AUV团队采集的真实声学通信数据进行实验验证，证明了该策略在实际水下环境中的有效性。
原文摘要 · Abstract (English)
Teams of cooperating autonomous underwater vehicles (AUVs) rely on acoustic communication for coordination, yet this communication medium is constrained by limited range, multi-path effects, and low bandwidth. One way to address the uncertainty associated with acoustic communication is to learn the communication environment in real-time. We address the challenge of a team of robots building a map of the probability of communication success from one location to another in real-time. This is a decentralized classification problem -- communication events are either successful or unsuccessful -- where AUVs share a subset of their communication measurements to build the map. The main contribution of this work is a rigorously derived data sharing policy that selects measurements to be shared among AUVs. We experimentally validate our proposed sharing policy using real acoustic communication data collected from teams of Virginia Tech 690 AUVs, demonstrating its effectiveness in underwater environments.
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