Learning and detecting abnormal speed of marine robots

Sungjin Cho,Catherine R Edwards,Fumin Zhang

doi:10.1177/1729881421999268

Abstract

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.

Highlights

Detection for marine robots is an important practical problem because marine robots are often used in distant and hostile environments such as the deep sea and the polar oceans
We propose anomaly detection algorithms for marine robots based on their trajectory data
The remainder of this article has been organized into the following sections: in the second and the third sections, we present vehicle motion model and controlled Lagrangian localization error of marine robots

Summary

Introduction

Detection for marine robots is an important practical problem because marine robots are often used in distant and hostile environments such as the deep sea and the polar oceans. During long-range or long-period missions, marine creatures and biofouling may harm robot sensors and thrusters.[1] Monitoring sensors have been installed to evaluate components vulnerable to faults.[2] For example, damaged propellers impair propulsive efficiency to control vehicle speed. These faults could be detected with rotational speed sensors installed at the propellers; this approach requires increased hardware complexity and cost,[3] and it may not detect unexpected external disturbances (e.g. white shark attack).

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Methods

Conclusion