7,830 research outputs found
Multiple Autonomous Systems in Underwater Mine Countermeasures Mission Using Various Information Fusion as Navigation Aid
Autonomous bottom mine neutralization systems have a challenging task of mine reacquisition and navigation in the demanding underwater environment. Even after mine reacquisition, the neutralization payload has to be autonomously deployed near the mine, and before any action the verification (classification) of the existence of a mine has to be determined. The mine intervention vehicle can be an expendable (self-destroyed during the mine neutralization) or a vehicle that deploys the neutralization payload and it is retrieved at the end of the mission. Currently the systems developed by the research community are capable of remotely navigating a mine intervention underwater vehicle in the vicinity of the mine by using remote sonar aided navigation from a master vehicle. However, the task of successfully navigating the vehicle that carries the neutralization payload near the bottom and around the mine remains a challenge due to sea bottom clutter and the target signature interfering with the sonar detection. We seek a solution by introducing navigation via visual processing near the mine location. Using an onboard camera the relative distance to the mine-like object can be estimated. This will improve the overall vehicle navigation and rate of successful payload delivery close to the mine. The paper will present the current navigation system of the mine intervention underwater vehicle and the newly developed visual processing for relative position estimation
Control and visual navigation for unmanned underwater vehicles
Ph. D. Thesis.Control and navigation systems are key for any autonomous robot. Due to environmental
disturbances, model uncertainties and nonlinear dynamic systems, reliable functional control is
essential and improvements in the controller design can significantly benefit the overall
performance of Unmanned Underwater Vehicles (UUVs). Analogously, due to electromagnetic
attenuation in underwater environments, the navigation of UUVs is always a challenging
problem.
In this thesis, control and navigation systems for UUVs are investigated. In the control field,
four different control strategies have been considered: Proportional-Integral-Derivative Control
(PID), Improved Sliding Mode Control (SMC), Backstepping Control (BC) and customised
Fuzzy Logic Control (FLC). The performances of these four controllers were initially simulated
and subsequently evaluated by practical experiments in different conditions using an underwater
vehicle in a tank. The results show that the improved SMC is more robust than the others with
small settling time, overshoot, and error.
In the navigation field, three underwater visual navigation systems have been developed in the
thesis: ArUco Underwater Navigation systems, a novel Integrated Visual Odometry with
Monocular camera (IVO-M), and a novel Integrated Visual Odometry with Stereo camera
(IVO-S). Compared with conventional underwater navigation, these methods are relatively
low-cost solutions and unlike other visual or inertial-visual navigation methods, they are able to
work well in an underwater sparse-feature environment. The results show the following: the
ArUco underwater navigation system does not suffer from cumulative error, but some segments
in the estimated trajectory are not consistent; IVO-M suffers from cumulative error (error ratio is
about 3 - 4%) and is limited by the assumption that the “seabed is locally flat”; IVO-S suffers
from small cumulative errors (error ratio is less than 2%).
Overall, this thesis contributes to the control and navigation systems of UUVs, presenting the
comparison between controllers, the improved SMC, and low-cost underwater visual navigation
methods
An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
This paper presents a novel tightly-coupled keyframe-based Simultaneous
Localization and Mapping (SLAM) system with loop-closing and relocalization
capabilities targeted for the underwater domain. Our previous work, SVIn,
augmented the state-of-the-art visual-inertial state estimation package OKVIS
to accommodate acoustic data from sonar in a non-linear optimization-based
framework. This paper addresses drift and loss of localization -- one of the
main problems affecting other packages in underwater domain -- by providing the
following main contributions: a robust initialization method to refine scale
using depth measurements, a fast preprocessing step to enhance the image
quality, and a real-time loop-closing and relocalization method using bag of
words (BoW). An additional contribution is the addition of depth measurements
from a pressure sensor to the tightly-coupled optimization formulation.
Experimental results on datasets collected with a custom-made underwater sensor
suite and an autonomous underwater vehicle from challenging underwater
environments with poor visibility demonstrate performance never achieved before
in terms of accuracy and robustness
Autonomous homing and docking tasks for an underwater vehicle
This paper briefly introduces a strategy for autonomous homing and docking tasks using an autonomous underwater vehicle. The control and guidance based path following for those tasks are described in this work. A standard sliding mode for controller design is briefly given. The method provides robust motion control efforts for an underwater vehicle’s decoupled system whilst minimising chattering effects. In a guidance system, the vector field based on a conventional artificial potential field method gives a desired trajectory with a use of existing information from sensors in the network. A well structured Line-of-Sight method is used for an AUV to follow the path. It provides guidance for an AUV to follow the predefined trajectory to a required position with the final desired orientation at the dock. Integration of a control and guidance system provides a complete system for this application. Simulation studies are illustrated in the paper
Low cost underwater acoustic localization
Over the course of the last decade, the cost of marine robotic platforms has
significantly decreased. In part this has lowered the barriers to entry of
exploring and monitoring larger areas of the earth's oceans. However, these
advances have been mostly focused on autonomous surface vehicles (ASVs) or
shallow water autonomous underwater vehicles (AUVs). One of the main drivers
for high cost in the deep water domain is the challenge of localizing such
vehicles using acoustics. A low cost one-way travel time underwater ranging
system is proposed to assist in localizing deep water submersibles. The system
consists of location aware anchor buoys at the surface and underwater nodes.
This paper presents a comparison of methods together with details on the
physical implementation to allow its integration into a deep sea micro AUV
currently in development. Additional simulation results show error reductions
by a factor of three.Comment: 73rd Meeting of the Acoustical Society of Americ
A Robust Model Predictive Control Approach for Autonomous Underwater Vehicles Operating in a Constrained workspace
This paper presents a novel Nonlinear Model Predictive Control (NMPC) scheme
for underwater robotic vehicles operating in a constrained workspace including
static obstacles. The purpose of the controller is to guide the vehicle towards
specific way points. Various limitations such as: obstacles, workspace
boundary, thruster saturation and predefined desired upper bound of the vehicle
velocity are captured as state and input constraints and are guaranteed during
the control design. The proposed scheme incorporates the full dynamics of the
vehicle in which the ocean currents are also involved. Hence, the control
inputs calculated by the proposed scheme are formulated in a way that the
vehicle will exploit the ocean currents, when these are in favor of the
way-point tracking mission which results in reduced energy consumption by the
thrusters. The performance of the proposed control strategy is experimentally
verified using a Degrees of Freedom (DoF) underwater robotic vehicle inside
a constrained test tank with obstacles.Comment: IEEE International Conference on Robotics and Automation (ICRA-2018),
Accepte
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