1,936 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
Intelligent Navigation for a Solar Powered Unmanned Underwater Vehicle
In this paper, an intelligent navigation system for
an unmanned underwater vehicle powered by renewable
energy and designed for shadow water inspection in
missions of a long duration is proposed. The system is
composed of an underwater vehicle, which tows a surface
vehicle. The surface vehicle is a small boat with
photovoltaic panels, a methanol fuel cell and
communication equipment, which provides energy and
communication to the underwater vehicle. The underwater
vehicle has sensors to monitor the underwater
environment such as sidescan sonar and a video camera in
a flexible configuration and sensors to measure the
physical and chemical parameters of water quality on
predefined paths for long distances. The underwater
vehicle implements a biologically inspired neural
architecture for autonomous intelligent navigation.
Navigation is carried out by integrating a kinematic
adaptive neuro‐controller for trajectory tracking and an
obstacle avoidance adaptive neuro‐ controller. The
autonomous underwater vehicle is capable of operating
during long periods of observation and monitoring. This
autonomous vehicle is a good tool for observing large areas
of sea, since it operates for long periods of time due to the
contribution of renewable energy. It correlates all sensor
data for time and geodetic position. This vehicle has been
used for monitoring the Mar Menor lagoon.Supported by the Coastal Monitoring
System for the Mar Menor (CMS‐ 463.01.08_CLUSTER)
project founded by the Regional Government of Murcia,
by the SICUVA project (Control and Navigation System
for AUV Oceanographic Monitoring Missions. REF:
15357/PI/10) founded by the Seneca Foundation of
Regional Government of Murcia and by the DIVISAMOS
project (Design of an Autonomous Underwater Vehicle
for Inspections and oceanographic mission‐UPCT: DPI‐
2009‐14744‐C03‐02) founded by the Spanish Ministry of
Science and Innovation from Spain
Digital Cognitive Companions for Marine Vessels : On the Path Towards Autonomous Ships
As for the automotive industry, industry and academia are making extensive efforts to create autonomous ships. The solutions for this are very technology-intense. Many building blocks, often relying on AI technology, need to work together to create a complete system that is safe and reliable to use. Even when the ships are fully unmanned, humans are still foreseen to guide the ships when unknown situations arise. This will be done through teleoperation systems.In this thesis, methods are presented to enhance the capability of two building blocks that are important for autonomous ships; a positioning system, and a system for teleoperation.The positioning system has been constructed to not rely on the Global Positioning System (GPS), as this system can be jammed or spoofed. Instead, it uses Bayesian calculations to compare the bottom depth and magnetic field measurements with known sea charts and magnetic field maps, in order to estimate the position. State-of-the-art techniques for this method typically use high-resolution maps. The problem is that there are hardly any high-resolution terrain maps available in the world. Hence we present a method using standard sea-charts. We compensate for the lower accuracy by using other domains, such as magnetic field intensity and bearings to landmarks. Using data from a field trial, we showed that the fusion method using multiple domains was more robust than using only one domain. In the second building block, we first investigated how 3D and VR approaches could support the remote operation of unmanned ships with a data connection with low throughput, by comparing respective graphical user interfaces (GUI) with a Baseline GUI following the currently applied interfaces in such contexts. Our findings show that both the 3D and VR approaches outperform the traditional approach significantly. We found the 3D GUI and VR GUI users to be better at reacting to potentially dangerous situations than the Baseline GUI users, and they could keep track of the surroundings more accurately. Building from this, we conducted a teleoperation user study using real-world data from a field-trial in the archipelago, where the users should assist the positioning system with bearings to landmarks. The users experienced the tool to give a good overview, and despite the connection with the low throughput, they managed through the GUI to significantly improve the positioning accuracy
An intelligent navigation system for an unmanned surface vehicle
Merged with duplicate record 10026.1/2768 on 27.03.2017 by CS (TIS)A multi-disciplinary research project has been carried out at the University of Plymouth to design
and develop an Unmanned Surface Vehicle (USV) named ýpringer. The work presented herein
relates to formulation of a robust, reliable, accurate and adaptable navigation system to enable
opringei to undertake various environmental monitoring tasks. Synergistically, sensor
mathematical modelling, fuzzy logic, Multi-Sensor Data Fusion (MSDF), Multi-Model Adaptive
Estimation (MMAE), fault adaptive data acquisition and an user interface system are combined to
enhance the robustness and fault tolerance of the onboard navigation system.
This thesis not only provides a holistic framework but also a concourse of computational
techniques in the design of a fault tolerant navigation system. One of the principle novelties of this
research is the use of various fuzzy logic based MSDF algorithms to provide an adaptive heading
angle under various fault situations for Springer. This algorithm adapts the process noise
covariance matrix ( Q) and measurement noise covariance matrix (R) in order to address one of
the disadvantages of Kalman filtering. This algorithm has been implemented in Spi-inger in real
time and results demonstrate excellent robustness qualities. In addition to the fuzzy logic based
MSDF, a unique MMAE algorithm has been proposed in order to provide an alternative approach
to enhance the fault tolerance of the heading angles for Springer.
To the author's knowledge, the work presented in this thesis suggests a novel way forward in the
development of autonomous navigation system design and, therefore, it is considered that the work
constitutes a contribution to knowledge in this area of study. Also, there are a number of ways in
which the work presented in this thesis can be extended to many other challenging domains.DEVONPORT MANAGEMENT LTD, J&S MARINE LTD
AND
SOUTH WEST WATER PL
A deep reinforcement learning based homeostatic system for unmanned position control
Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/
Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and object-based image analysis (OBIA)
Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resource
Towards Autonomous Ship Hull Inspection using the Bluefin HAUV
URL is to paper listed on conference scheduleIn this paper we describe our effort to automate ship hull inspection for security
applications. Our main contribution is a system that is capable of drift-free
self-localization on a ship hull for extended periods of time. Maintaining accurate
localization for the duration of a mission is important for navigation and for
ensuring full coverage of the area to be inspected. We exclusively use onboard
sensors including an imaging sonar to correct for drift in the vehicle’s navigation
sensors. We present preliminary results from online experiments on a ship hull. We
further describe ongoing work including adding capabilities for change detection
by aligning vehicle trajectories of different missions based on a technique recently
developed in our lab.United States. Office of Naval Research (grant N00014-06- 10043
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