3,502 research outputs found
An ensemble of online estimation methods for one degree-of-freedom models of unmanned surface vehicles: applied theory and preliminary field results with eight vehicles
In this paper we report an experimental evaluation of three popular methods
for online system identification of unmanned surface vehicles (USVs) which were
implemented as an ensemble: certifiably stable shallow recurrent neural network
(RNN), adaptive identification (AID), and recursive least squares (RLS). The
algorithms were deployed on eight USVs for a total of 30 hours of online
estimation. During online training the loss function for the RNN was augmented
to include a cost for violating a sufficient condition for the RNN to be stable
in the sense of contraction stability. Additionally we described an efficient
method to calculate the equilibrium points of the RNN and classify the
associated stability properties about these points. We found the AID method had
lowest mean absolute error in the online prediction setting, but a weighted
ensemble had lower error in offline processing.Comment: v1) 8 Pages, 5 Figures, To appear at 2023 RSJ/IEEE Conference on
Intelligent Robotics and Systems (IROS) in Detroit, Michigan, USA, v2)
corrected error in reference
The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators
Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft
State-of-the-Art System Solutions for Unmanned Underwater Vehicles
Unmanned Underwater Vehicles (UUVs) have gained popularity for the last decades, especially for the purpose of not risking human life in dangerous operations. On the other hand, underwater environment introduces numerous challenges in navigation, control and communication of such vehicles. Certainly, this fact makes the development of these vehicles more interesting and engineering-wise more attractive. In this paper, we first revisit the existing technology and methodology for the solution of aforementioned problems, then we try to come up with a system solution of a generic unmanned underwater vehicles
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa
Optimized Custom Dataset for Efficient Detection of Underwater Trash
Accurately quantifying and removing submerged underwater waste plays a
crucial role in safeguarding marine life and preserving the environment. While
detecting floating and surface debris is relatively straightforward,
quantifying submerged waste presents significant challenges due to factors like
light refraction, absorption, suspended particles, and color distortion. This
paper addresses these challenges by proposing the development of a custom
dataset and an efficient detection approach for submerged marine debris. The
dataset encompasses diverse underwater environments and incorporates
annotations for precise labeling of debris instances. Ultimately, the primary
objective of this custom dataset is to enhance the diversity of litter
instances and improve their detection accuracy in deep submerged environments
by leveraging state-of-the-art deep learning architectures
Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter
The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.This research was partially funded by the Campus de Excelencia Internacional Andalucia Tech, University of Malaga, Malaga, Spain. Partial funding for open access charge: Universidad de Málag
Summary of Research 2000, Department of Mechanical Engineering
The views expressed in this report are those of the authors and do not reflect the official policy or position of the
Department of Defense or U.S. Government.This report contains project summaries of the research projects in the Department of Mechanical Engineering. A list of recent publications is also
included, which consists of conference presentations and publications, books, contributions to books, published journal papers, and technical reports.
Thesis abstracts of students advised by faculty in the Department are also included
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