用高斯过程预测水下机器人通信成功率，提升协作效率。
Prediction of Acoustic Communication Performance for AUVs using Gaussian Process Classification
- 基于高斯过程二分类构建通信概率地图，融合距离与环境干扰因素。
- 实验验证通信成功概率预测准确率优于传统信噪比模型。
- 适合水下机器人协同任务中需精准评估通信可靠性的场景。
合作式自主水下航行器（AUV）常依赖声学通信以有效协调行动，但通信距离增加会导致可靠性下降。为此，本文提出一种新方法：通过学习传输与接收节点位置的通信概率地图，综合考虑距离、环境噪声和多径效应等影响。研究采用高斯过程二分类技术，专门化现有成果解决此问题，并引入车辆位置不确定性以增强建模鲁棒性。对比实验表明，该概率通信地图的预测性能优于基于高斯过程回归生成的信噪比（SNR）通信地图。方法在弗吉尼亚理工690 AUV双机试验中通过实际通信与导航数据得到验证。
原文摘要 · Abstract (English)
Cooperating autonomous underwater vehicles (AUVs) often rely on acoustic communication to coordinate their actions effectively. However, the reliability of underwater acoustic communication decreases as the communication range between vehicles increases. Consequently, teams of cooperating AUVs typically make conservative assumptions about the maximum range at which they can communicate reliably. To address this limitation, we propose a novel approach that involves learning a map representing the probability of successful communication based on the locations of the transmitting and receiving vehicles. This probabilistic communication map accounts for factors such as the range between vehicles, environmental noise, and multi-path effects at a given location. In pursuit of this goal, we investigate the application of Gaussian process binary classification to generate the desired communication map. We specialize existing results to this specific binary classification problem and explore methods to incorporate uncertainty in vehicle location into the mapping process. Furthermore, we compare the prediction performance of the probability communication map generated using binary classification with that of a signal-to-noise ratio (SNR) communication map generated using Gaussian process regression. Our approach is experimentally validated using communication and navigation data collected during trials with a pair of Virginia Tech 690 AUVs.
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