Adaptive Sampling of Surface Fronts in the Arctic Using an Autonomous Underwater Vehicle

Author(s):  
Trygve Olav Fossum ◽  
Petter Norgren ◽  
Ilker Fer ◽  
Frank Nilsen ◽  
Zoe Koenig ◽  
...  
2012 ◽  
Vol 29 (11) ◽  
pp. 1689-1703 ◽  
Author(s):  
Mario Brito ◽  
Gwyn Griffiths ◽  
James Ferguson ◽  
David Hopkin ◽  
Richard Mills ◽  
...  

Abstract The deployment of a deep-diving long-range autonomous underwater vehicle (AUV) is a complex operation that requires the use of a risk-informed decision-making process. Operational risk assessment is heavily dependent on expert subjective judgment. Expert judgments can be elicited either mathematically or behaviorally. During mathematical elicitation experts are kept separate and provide their assessment individually. These are then mathematically combined to create a judgment that represents the group view. The limitation with this approach is that experts do not have the opportunity to discuss different views and thus remove bias from their assessment. In this paper, a Bayesian behavioral approach to estimate and manage AUV operational risk is proposed. At an initial workshop, behavioral aggregation, that is, reaching agreement on the distributions of risks for faults or incidents, is followed by an agreed upon initial estimate of the likelihood of success of the proposed risk mitigation methods. Postexpedition, a second workshop assesses the new data and compares observed to predicted risk, thus updating the prior estimate using Bayes’ rule. This feedback further educates the experts and assesses the actual effectiveness of the mitigation measures. Applying this approach to an AUV campaign in ice-covered waters in the Arctic showed that the maximum error between the predicted and the actual risk was 9% and that the experts’ assessments of the effectiveness of risk mitigation led to a maximum of 24% in risk reduction.


Author(s):  
Signe Moe ◽  
Walter Caharija ◽  
Kristin Y. Pettersen ◽  
Ingrid Schjølberg

The use of autonomous marine vehicles, and especially autonomous underwater vehicles, is rapidly increasing within several fields of study. In particular, such vehicles can be applied for sea floor mapping, oceanography, environmental monitoring, inspection and maintenance of underwater structures (for instance within the oil and gas industry) and military purposes. They are also highly suitable for operations below ice-covered areas in the Arctic. However, there are still many challenges related to making such underwater vehicles autonomous. A fundamental task of an autonomous underwater vehicle vessel is to follow a general path in the presence of unknown ocean currents. There exist several results for underwater vehicles to follow a general path when no ocean currents are present [1] and to follow a geometrically simple path such as a straight line when ocean currents affect the vehicle [2, 3], but the problem of general path following in the presence of unknown ocean currents has not been solved yet. This paper presents a method to achieve this. The results are an extension of the results in [1], and introduce a virtual Serret-Frenet reference frame that is anchored in and propagates along the desired path. The closed-loop system consists of an ocean current observer, a guidance law, a controller and an update law to drive the Serret-Frenet frame along the path, and is shown to be asymptotically stable given that certain assumptions are fulfilled. This guarantees that the autonomous underwater vehicle will converge to the desired path and move along it with the desired velocity. Simulation results are presented to verify and illustrate the theoretical results.


2020 ◽  
Vol 8 (8) ◽  
pp. 618
Author(s):  
Jimin Hwang ◽  
Neil Bose ◽  
Hung Duc Nguyen ◽  
Guy Williams

We introduce an adaptive sampling 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.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 198021-198034
Author(s):  
Jimin Hwang ◽  
Neil Bose ◽  
Hung Duc Nguyen ◽  
Guy Williams

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 109
Author(s):  
Baoju Wu ◽  
Xiaowei Han ◽  
Nanmu Hui

Autonomous underwater vehicle is an effective tool for humans to explore the ocean. It can be used for the monitoring of underwater structures and facilities, which puts forward more accurate and stable requirements for the system operation of the autonomous underwater vehicle. This paper studies the system and structural design, including the parameter identification design and control system design, of a novel autonomous underwater vehicle called “Arctic AUV”. The dynamic mathematical model of the “Arctic AUV” was established, and the system parameter identification method based on the multi-sensor least squares centralized fusion algorithm was proposed. The simplification of the mathematical model of the robot was theoretically derived, and the online parameter identification and motion control were combined, so that the robot could cope with the influence of the arctic water velocity and external turbulence. Based on the hybrid control scheme of adaptive PID and predictive control, the accurate motion control of the “Arctic AUV” was realized. A prototype of “Arctic AUV” was developed, and system parameter identification experiments were carried out in indoor pool water. Hybrid adaptive and predictive control experiments were also carried out. The validity of the parametric design method in this paper was verified, and by comparative experiment, the effect of the control method proposed in this paper was better than the traditional method.


Sign in / Sign up

Export Citation Format

Share Document