ORCID Profile
0000-0002-9641-0631
Current Organisation
Memorial University of Newfoundland
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: MDPI AG
Date: 02-08-2019
DOI: 10.3390/APP9153145
Abstract: 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 s ling 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, s ling criteria can be adjusted, and target features can be traced. This paper presents an overview of existing adaptive s ling 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.
Publisher: MDPI AG
Date: 17-08-2020
DOI: 10.3390/JMSE8080618
Abstract: We introduce an adaptive s ling method that has been developed to support the Backseat Driver control architecture of the Memorial University of Newfoundland (MUN) Explorer autonomous underwater vehicle (AUV). The design is based on an acoustic detection and in-situ analysis program that allows an AUV to perform automatic detection and autonomous tracking of an oil plume. The method contains acoustic image acquisition, autonomous triggering, and thresholding in the search stage. A new biomimetic search pattern, the bumblebee flight path, was designed to maximize the spatial coverage in the oil plume detection phase. The effectiveness of the developed algorithm was validated through simulations using a two-dimensional planar plume model and a 90-degree scanning sensor model. The results demonstrate that the bumblebee search design combined with a genetic solution for the Traveling Salesperson Problem outperformed a conventional lawnmower survey, reducing the AUV travel distance by up to 75.3%. Our plume detection strategy, using acoustic sensing, provided data of plume location, distribution, and density, over a sector in contrast with traditional chemical oil sensors that only provide readings at a point.
Publisher: IEEE
Date: 30-09-2020
Publisher: MDPI AG
Date: 27-01-2021
DOI: 10.3390/JMSE9020126
Abstract: To overcome the environmental impacts of releasing oil into the ocean for testing acoustic methods in field experiments using autonomous underwater vehicles (AUVs), environmentally friendly gas bubble plumes with low rise velocities are proposed in this research to be used as proxies for oil. An experiment was conducted to test the performance of a centrifugal-type microbubble generator in generating microbubble plumes and their practicability to be used in field experiments. Sizes of bubbles were measured with a Laser In-Situ Scattering and Transmissometry sensor. Residence time of bubble plumes was estimated by using a Ping360 sonar. Results from the experiment showed that a larger number of small bubbles were found in deeper water as larger bubbles rose quickly to the surface without staying in the water column. The residence time of the generated bubble plumes at the depth of 0.5 m was estimated to be over 5 min. The microbubble generator is planned to be applied in future field experiments, as it is effective in producing relatively long-endurance plumes that can be used as potential proxies for oil plumes in field trials of AUVs for delineating oil spills.
Publisher: EJournal Publishing
Date: 2022
Publisher: EJournal Publishing
Date: 2021
Publisher: IEEE
Date: 11-2018
Publisher: MDPI AG
Date: 16-10-2023
No related grants have been discovered for Jimin Hwang.