解决水下机器人通信延迟下的实时协同导航问题
An Asynchronous Two-Speed Kalman Filter for Real-Time UUV Cooperative Navigation Under Acoustic Delays
- 分快慢两条线并行处理：快速线用高斯过程补偿航迹推算，慢速线处理延迟的协作信息
- 在30秒延迟下仍保持与批量优化相当的定位精度，计算耗时不足1毫秒
- 适合对实时性要求高的水下自主系统，尤其适用于声学信道延迟严重的场景
在无全球导航卫星系统（GNSS）的水下环境中，单个无人潜航器（UUV）会因航迹推算漂移而无法准确定位，因此协同导航（CN）至关重要。然而，水下声学信道固有的严重通信延迟给实时状态估计带来巨大挑战。传统滤波器如扩展卡尔曼滤波器（EKFs）或无迹卡尔曼滤波器（UKFs）通常需阻塞主控回路等待延迟数据，或直接丢弃乱序测量（OOSMs），导致严重漂移。为此，我们提出一种异步双速卡尔曼滤波器（TSKF），并引入新颖的投影机制——变分历史蒸馏（VHD）。该架构将估计过程解耦为两条并行线程：高速线利用高斯过程（GP）补偿的航迹推算保证高频实时控制，低速线专门处理异步延迟的协作信息。通过引入有限长度环形状态缓冲器（FLCSB），算法将延迟测量应用到对应的历史状态，并利用基于VHD的投影，无需大量重计算即可快速前推修正至当前时刻。仿真结果表明，在高达30秒延迟条件下，所提TSKF的轨迹误差与计算开销巨大的批处理优化方法相当，且执行时间低于1毫秒，显著优于标准EKF/UKF。结果验证了一种有效的控制、通信与计算（3C）协同设计，显著提升了自主海洋自动化系统的鲁棒性。
原文摘要 · Abstract (English)
In Global Navigation Satellite System (GNSS)-denied underwater environments, individual unmanned underwater vehicles (UUVs) suffer from unbounded dead-reckoning drift, making collaborative navigation (CN) crucial for accurate state estimation. However, the severe communication delay inherent in underwater acoustic channels poses serious challenges to real-time state estimation. Traditional filters, such as Extended Kalman Filters (EKFs) or Unscented Kalman Filters (UKFs), usually block the main control loop while waiting for delayed data, or effectively discard Out-of-Sequence Measurements (OOSMs), resulting in serious drift. To address this, we propose an Asynchronous Two-Speed Kalman Filter (TSKF) enhanced by a novel projection mechanism, which we term Variational History Distillation (VHD). The proposed architecture decouples the estimation process into two parallel threads: a fast-rate thread that utilizes Gaussian Process (GP) compensated dead reckoning to guarantee high-frequency real-time control, and a slow-rate thread dedicated to processing asynchronously delayed collaborative information. By introducing a Finite-Length Circular State Buffer (FLCSB), the algorithm applies delayed measurements to their corresponding historical states, and utilizes a VHD-based projection to fast-forward the correction to the current time without computationally heavy recalculations. Simulation results demonstrate that the proposed TSKF maintains a trajectory error comparable to computationally intensive batch-optimization methods under severe delays (up to 30\,s). Executing in sub-millisecond time, it significantly outperforms standard EKF/UKF. The results demonstrate an effective control, communication, and computing (3C) co-design that significantly enhances the resilience of autonomous marine automation systems.
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