This article presents anomaly detection algorithms for marine robots based on their trajectories under the influence of unknown ocean flow. A learning algorithm identifies the flow field and estimates the through-water speed of a marine robot. By comparing the through-water speed with a nominal speed range, the algorithm is able to detect anomalies causing unusual speed changes. The identified ocean flow field is used to eliminate false alarms, where an abnormal trajectory may be caused by unexpected flow. The convergence of the algorithms is justified through the theory of adaptive control. The proposed strategy is robust to speed constraints and inaccurate flow modeling. Experimental results are collected on an indoor testbed formed by the Georgia Tech Miniature Autonomous Blimp and Georgia Tech Wind Measuring Robot, while simulation study is performed for ocean flow field. Data collected in both studies confirm the effectiveness of the algorithms in identifying the through-water speed and the detection of speed anomalies while avoiding false alarms.

本文介绍了在未知海流影响下，基于航迹的航海机器人异常检测算法。一种学习算法可识别流场并估算船用机器人的通水速度。通过将通水速度与标称速度范围进行比较，该算法能够检测导致异常速度变化的异常。识别出的海流场用于消除误报，在这种情况下，意外流量可能会引起异常轨迹。通过自适应控制理论证明了算法的收敛性。所提出的策略对于速度约束和不正确的流量建模具有鲁棒性。实验结果收集在由佐治亚理工学院微型自动飞艇和佐治亚理工学院风测量机器人组成的室内试验台上，同时进行了海洋流场的模拟研究。两项研究中收集到的数据证实了该算法在识别通水速度和速度异常检测中的有效性，同时避免了误报。