Learning and detecting abnormal speed of marine robots
Learning and detecting abnormal speed of marine robots
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DOI：
10.1177/1729881421999268
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发表时间：
2021-03-01
影响因子：
2.3
通讯作者：
Edwards, Catherine R.
中科院分区：
文献类型：
--
作者：
Cho, Sungjin;Zhang, Fumin;Edwards, Catherine R.
本文提出了基于海洋机器人在未知洋流影响下的轨迹的异常检测算法。学习算法识别流场并估计海洋机器人的穿水速度。通过将对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情况。识别的海洋流场用于消除误报，其中异常轨迹可能是由意外的流引起的。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限制和不准确的流量建模具有鲁棒性。实验结果在Georgia Tech微型自主飞艇和Georgia Tech测风机器人组成的室内试验台上采集，同时对海洋流场进行模拟研究。两项研究中收集的数据证实了算法在识别通过水速度和检测速度异常方面的有效性，同时避免了误报。
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.