Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Source-likelihood Mapping Using POMDP in Autonomous Hydrothermal Vent Prospecting
Jinlong Lia, Shuo Pangb, Haifeng Jiuc, Yin Jiangd

Marine Design & Research Institute of China
Shanghai, China
bDepartment of Electrical Engineering and Computer science, Embry-Riddle Aeronautical University
Florida, USA
cDepartment of Mechanics, Harbin University of CommerceHarbin, ChinadChina Eastern Technology Application Research and Development Center Co., LTD
Shanghai, Chinaavent location by autonomous underwater vehicle (AUV) in the height ofnon-buoyant plumes.

ABSTRACT
Chemical signals contained in hydrothermal fluid provide the clues toinfer the location of hydrothermal vent by autonomous underwatervehicle (AUV). A partially observable Markov decision process(POMDP) is introduced to model the searching area and observation
behavior in non-buoyant plume height. A POMDP-based belief state
estimation method is introduced to maintain source-likelihood mapping.
A real-time simulation platform is built to verify the source-likelihood
mapping approach which provides a novel solution to effectively infer
the vent location. The influences of memory length of flow sensor
information on mapping is studied, and the length is not recommended
to be very long.

There are different approaches which can guide the AUV to further
investigate the searching area when sensing chemical signals ofhydrothermal fluid. An initial approach attempts to compute theconcentration gradient and follow the gradient. However, gradient based
approach is not feasible in environments with medium to high Reynolds
numbers (Farrell, 2003; Elkinton, 1984; Jones, 1983). The second
approach is bio-inspired (Shigaki, 2017). Animals are capable of using
olfaction for foraging or reproductive activities, such as lobsters, ants
and moths. A sequence of search speed and heading commands for theAUV are generated when the chemical signals are sensed or not such that
the AUV trajectories is likely to locate the odor source (Li, 2001). Wei
Li adopted moth-inspired behavior-based adaptive mission planner(AMP) to trace the chemical plume to its source and reliably declare thesource location (Li, 2006). The third approach performs on-line mapping
when the chemical signals are sensed or not (Pang, 2006), and the AUV
reacts based on on-line mapping instead of the chemical sensor directly.
On-line mapping integrates the necessary historical information, andconstructs an intermediate layer between the chemical sensor and AUVplanning. Farrell (2003) suggested a typical vehicle hardware, control,guidance, mapping and planning architecture. The advantage ofcomputational capabilities and information storage capabilities areutilized by the third approach compared with the former two approaches.
Hidden Markov methods (HMMs) are proposed to provide efficientalgorithms for predicting the likelihood of odor detection versus position
and the most likely path taken by the odor to a given location (Farrell,2003). Pang (2006) proposed a source-likelihood mapping approachbased on Bayesian inference methods, and the source-likelihood map is
propagated in response to both detection and non-detection events.
Source-likelihood mapping is to maintain the online map which
represents the vent probability distribution. The AUV can further
investigate the searching area by using the source-likelihood map.

KEY WORDS: Hydrothermal vent prospecting; AUV; POMDP;
source-likelihood mapping.

INTRODUCTION
Hydrothermal vent prospecting is important to broaden ourunderstanding of enormously valuable vent ecosystems and deep-sea
mineral resources. The hydrothermal vent along mid-ocean ridges emitshydrothermal fluid like black-smoker geysers. The hydrothermal fluid
consists of hydrothermal plumes. Hydrothermal plumes discharged fromthe vent typically rise above sea floor to a height of 100-400m, and thenspread laterally along ocean current at the scales of 1000km (Jakuba,2005). Hydrothermal plumes which rise upward are called buoyant
plumes, and hydrothermal plumes which move laterally are called nonbuoyant plumes. In a time-averaged sense, buoyant plumes usually
expand laterally from on the order of a few centimeters to a diameter on
the order of about 100m (Jakuba, 2005). Non-buoyant plumes can bedetected far away from the vent site. The hydrothermal fluid ischaracterized by different physical and chemical factors. The chemicalsignals contained in the hydrothermal fluid provide the clues to infer the16