236 research outputs found
Subsea cable tracking by an unmanned surface vehicle
Subsea cable localisation is a demanding task that requires a lot of time, effort and expense. In the present paper the authors propose a methodology that is automated and inexpensive, based on magnetic detection from a small unmanned surface vehicle (USV) and the use of a batch particle filter (BPF) algorithm. A dynamic path planning algorithm for the USV is also developed so that adequate samples of the magnetic field readings can be gathered for processing by the BPF. All of these elements work together online as the cable is tracked, which was demonstrated in a simulated mission
Surge-varying LOS based path following of under actuated surface vehicles
1048-1055Subject to harsh ocean environment, a novel path following control scheme with accurate guidance and high anti-disturbance ability for under actuated surface vehicles is proposed. The innovative work is as follow: (1) Based on the traditional line-of-sight (LOS), a surge-varying LOS (SVLOS) guidance law is designed to achieve double guidance of speed and heading, which enhances the flexibility and precision of the previous LOS; (2) Unknown disturbances are exactly estimated by an exact disturbance observer (EDO), wherein the limitations of bounded and asymptotic observations can be avoided; (3) The EDO-based robust tracking controllers enable accurate disturbance compensation and guided signal tracking in harsh ocean environment. Rigorous theoretical analysis and significant simulation comparison have been done to demonstrate superiority of the EDO-SVLOS scheme
OBJECT PERCEPTION IN UNDERWATER ENVIRONMENTS: A SURVEY ON SENSORS AND SENSING METHODOLOGIES
Underwater robots play a critical role in the marine industry. Object perception is the foundation for the automatic
operations of submerged vehicles in dynamic aquatic environments. However, underwater perception
encounters multiple environmental challenges, including rapid light attenuation, light refraction, or backscattering
effect. These problems reduce the sensing devices’ signal-to-noise ratio (SNR), making underwater
perception a complicated research topic. This paper describes the state-of-the-art sensing technologies and
object perception techniques for underwater robots in different environmental conditions. Due to the current
sensing modalities’ various constraints and characteristics, we divide the perception ranges into close-range,
medium-range, and long-range. We survey and describe recent advances for each perception range and suggest
some potential future research directions worthy of investigating in this field
MECHANICAL DESIGN OF AN AUTONOMOUS MARINE ROBOTIC SYSTEM FOR INTERACTION WITH DIVERS
SCUBA diving, professional or recreational, remains one of the most hazardous activities known by man, mostly due to the fact that the human survival in the underwater environment requires use of technical equipment such as breathing regulators. Loss of breathing gas supply, burst eardrum, decompression sickness and nitrogen narcosis are just a few problems which can occur during an ordinary dive and result in injuries, long-term illnesses or even death. Most common way to reduce the risk of diving is to dive in pairs, thus allowing divers to cooperate with each other and react when uncommon situation occurs. Having the ability to react before an unwanted situation happens would improve diver safety.
This paper describes an autonomous marine robotic system that replaces a human dive buddy. Such a robotic system, developed within an FP7 project “CADDY – Cognitive Autonomous Diving Buddy” provides a symbiotic link between robots and human divers in the underwater. The proposed concept consists of a diver, an autonomous underwater vehicle (AUV) Buddy and an autonomous surface vehicle (ASV) PlaDyPos, acting within a cooperative network linked via an acoustic communication channel. This is a first time that an underwater human-robot system of such a scale has ever been developed. In this paper, focus is put on mechanical characteristics of the robotic vehicles
Probablistic approaches for intelligent AUV localisation
This thesis studies the problem of intelligent localisation for an autonomous underwater
vehicle (AUV). After an introduction about robot localisation and specific
issues in the underwater domain, the thesis will focus on passive techniques for AUV
localisation, highlighting experimental results and comparison among different techniques.
Then, it will develop active techniques, which require intelligent decisions
about the steps to undertake in order for the AUV to localise itself. The undertaken
methodology consisted in three stages: theoretical analysis of the problem, tests with
a simulation environment, integration in the robot architecture and field trials. The
conclusions highlight applications and scenarios where the developed techniques have
been successfully used or can be potentially used to enhance the results given by current
techniques. The main contribution of this thesis is in the proposal of an active
localisation module, which is able to determine the best set of action to be executed,
in order to maximise the localisation results, in terms of time and efficiency
Underwater Robots Part I: Current Systems and Problem Pose
International audienceThis paper constitutes the first part of a general overview of underwater robotics. The second part is titled: Underwater Robots Part II: existing solutions and open issues
Guidance and control of an autonomous underwater vehicle
Merged with duplicate record 10026.1/856 on 07.03.2017 by CS (TIS)A cooperative project between the Universities of Plymouth and Cranfield was aimed
at designing and developing an autonomous underwater vehicle named Hammerhead.
The work presented herein is to formulate an advance guidance and control system
and to implement it in the Hammerhead. This involves the description of Hammerhead
hardware from a control system perspective. In addition to the control system,
an intelligent navigation scheme and a state of the art vision system is also developed.
However, the development of these submodules is out of the scope of this thesis.
To model an underwater vehicle, the traditional way is to acquire painstaking mathematical
models based on laws of physics and then simplify and linearise the models to
some operating point. One of the principal novelties of this research is the use of system
identification techniques on actual vehicle data obtained from full scale in water
experiments. Two new guidance mechanisms have also been formulated for cruising
type vehicles. The first is a modification of the proportional navigation guidance for
missiles whilst the other is a hybrid law which is a combination of several guidance
strategies employed during different phases of the Right.
In addition to the modelling process and guidance systems, a number of robust control
methodologies have been conceived for Hammerhead. A discrete time linear
quadratic Gaussian with loop transfer recovery based autopilot is formulated and integrated
with the conventional and more advance guidance laws proposed. A model
predictive controller (MPC) has also been devised which is constructed using artificial
intelligence techniques such as genetic algorithms (GA) and fuzzy logic. A GA
is employed as an online optimization routine whilst fuzzy logic has been exploited
as an objective function in an MPC framework. The GA-MPC autopilot has been
implemented in Hammerhead in real time and results demonstrate excellent robustness
despite the presence of disturbances and ever present modelling uncertainty. To
the author's knowledge, this is the first successful application of a GA in real time
optimization for controller tuning in the marine sector and thus the thesis makes an
extremely novel and useful contribution to control system design in general. The
controllers are also integrated with the proposed guidance laws and is also considered
to be an invaluable contribution to knowledge. Moreover, the autopilots are used in
conjunction with a vision based altitude information sensor and simulation results
demonstrate the efficacy of the controllers to cope with uncertain altitude demands.J&S MARINE LTD., QINETIQ,
SUBSEA 7 AND SOUTH WEST WATER PL
Underwater robotics in the future of arctic oil and gas operations
Master's thesis in Petroleum engineeringArctic regions have lately been in the centre of increasing attention due to high vulnerability to climate change and the retreat in sea ice cover. Commercial actors are exploring the Arctic for new shipping routes and natural resources while scientific activity is being intensified to provide better understanding of the ecosystems. Marine surveys in the Arctic have traditionally been conducted from research vessels, requiring considerable resources and involving high risks where sea ice is present. Thus, development of low-cost methods for collecting data in extreme areas is of interest for both industrial purposes and environmental management.
The main objective of this thesis is to investigate the use of underwater vehicles as sensor platforms for oil and gas industry applications with focus on seabed mapping and monitoring. Theoretical background and a review of relevant previous studies are provided prior to presentation of the fieldwork, which took place in January 2017 in Kongsfjorden (Svalbard). The fieldwork was a part of the Underwater Robotics and Polar Night Biology course offered at the University Centre in Svalbard. Applied unmanned platforms included remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs) and an autonomous surface vehicle (ASV). They were equipped with such sensors as side-scan sonar, multi-beam echo sounder, camera and others. The acquired data was processed and used to provide information about the study area.
The carried out analysis of the vehicle performance gives an insight into challenges specific to marine surveys in the Arctic regions, especially during the period of polar night. The discussion is focused on the benefits of underwater robotics and integrated platform surveying in remote and harsh environment. Recommendations for further research and suggestions for application of similar vehicles and sensors are also given in the thesis
Robot Navigation in Distorted Magnetic Fields
This thesis investigates the utilization of magnetic field distortions for the localization and navigation of robotic systems. The work comprehensively illuminates the various aspects that are relevant in this context. Among other things, the characteristics of magnetic field environments are assessed and examined for their usability for robot navigation in various typical mobile robot deployment scenarios. A strong focus of this work lies in the self-induced static and dynamic magnetic field distortions of complex kinematic robots, which could hinder the use of magnetic fields because of their interference with the ambient magnetic field. In addition to the examination of typical distortions in robots of different classes, solutions for compensation and concrete tools are developed both in hardware (distributed magnetometer sensor systems) and in software. In this context, machine learning approaches for learning static and dynamic system distortions are explored and contrasted with classical methods for calibrating magnetic field sensors. In order to extend probabilistic state estimation methods towards the localization in magnetic fields, a measurement model based on Mises-Fisher distributions is developed in this thesis. Finally, the approaches of this work are evaluated in practice inside and outside the laboratory in different environments and domains (e.g. office, subsea, desert, etc.) with different types of robot systems
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