Next Article in Journal
AR Displays: Next-Generation Technologies to Solve the Vergence–Accommodation Conflict
Next Article in Special Issue
Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning
Previous Article in Journal
Swing Vibration Control of Suspended Structure Using Active Rotary Inertia Driver System: Parametric Analysis and Experimental Verification
Previous Article in Special Issue
Autonomous Path Planning of AUV in Large-Scale Complex Marine Environment Based on Swarm Hyper-Heuristic Algorithm

AUV Adaptive Sampling Methods: A Review

Australian Maritime College, University of Tasmania, Launceston 7250, TAS, Australia
Department of Ocean and Naval Architectural Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(15), 3145;
Received: 16 July 2019 / Revised: 26 July 2019 / Accepted: 29 July 2019 / Published: 2 August 2019
(This article belongs to the Special Issue Underwater Robots in Ocean and Coastal Applications)
Autonomous underwater vehicles (AUVs) are unmanned marine robots that have been used for a broad range of oceanographic missions. They are programmed to perform at various levels of autonomy, including autonomous behaviours and intelligent behaviours. Adaptive sampling is one class of intelligent behaviour that allows the vehicle to autonomously make decisions during a mission in response to environment changes and vehicle state changes. Having a closed-loop control architecture, an AUV can perceive the environment, interpret the data and take follow-up measures. Thus, the mission plan can be modified, sampling criteria can be adjusted, and target features can be traced. This paper presents an overview of existing adaptive sampling techniques. Included are adaptive mission uses and underlying methods for perception, interpretation and reaction to underwater phenomena in AUV operations. The potential for future research in adaptive missions is discussed. View Full-Text
Keywords: autonomous underwater vehicle(s); maritime robotics; adaptive sampling; underwater feature tracking; in-situ sensors; sensor fusion autonomous underwater vehicle(s); maritime robotics; adaptive sampling; underwater feature tracking; in-situ sensors; sensor fusion
Show Figures

Figure 1

MDPI and ACS Style

Hwang, J.; Bose, N.; Fan, S. AUV Adaptive Sampling Methods: A Review. Appl. Sci. 2019, 9, 3145.

AMA Style

Hwang J, Bose N, Fan S. AUV Adaptive Sampling Methods: A Review. Applied Sciences. 2019; 9(15):3145.

Chicago/Turabian Style

Hwang, Jimin; Bose, Neil; Fan, Shuangshuang. 2019. "AUV Adaptive Sampling Methods: A Review" Appl. Sci. 9, no. 15: 3145.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop