68 research outputs found
Localization, Mapping and SLAM in Marine and Underwater Environments
The use of robots in marine and underwater applications is growing rapidly. These applications share the common requirement of modeling the environment and estimating the robots’ pose. Although there are several mapping, SLAM, target detection and localization methods, marine and underwater environments have several challenging characteristics, such as poor visibility, water currents, communication issues, sonar inaccuracies or unstructured environments, that have to be considered. The purpose of this Special Issue is to present the current research trends in the topics of underwater localization, mapping, SLAM, and target detection and localization. To this end, we have collected seven articles from leading researchers in the field, and present the different approaches and methods currently being investigated to improve the performance of underwater robots
OIL SPILL MODELING FOR IMPROVED RESPONSE TO ARCTIC MARITIME SPILLS: THE PATH FORWARD
Maritime shipping and natural resource development in the Arctic are projected to increase as sea ice coverage decreases, resulting in a greater probability of more and larger oil spills. The increasing risk of Arctic spills emphasizes the need to identify the state-of-the-art oil trajectory and sea ice models and the potential for their integration. The Oil Spill Modeling for Improved Response to Arctic Maritime Spills: The Path Forward (AMSM) project, funded by the Arctic Domain Awareness Center (ADAC), provides a structured approach to gather expert advice to address U.S. Coast Guard (USCG) Federal On-Scene Coordinator (FOSC) core needs for decision-making. The National Oceanic & Atmospheric Administration (NOAA) Office of Response & Restoration (OR&R) provides scientific support to the USCG FOSC during oil spill response. As part of this scientific support, NOAA OR&R supplies decision support models that predict the fate (including chemical and physical weathering) and transport of spilled oil. Oil spill modeling in the Arctic faces many unique challenges including limited availability of environmental data (e.g., currents, wind, ice characteristics) at fine spatial and temporal resolution to feed models. Despite these challenges, OR&R’s modeling products must provide adequate spill trajectory predictions, so that response efforts minimize economic, cultural and environmental impacts, including those to species, habitats and food supplies. The AMSM project addressed the unique needs and challenges associated with Arctic spill response by: (1) identifying state-of-the-art oil spill and sea ice models, (2) recommending new components and algorithms for oil and ice interactions, (3) proposing methods for improving communication of model output uncertainty, and (4) developing methods for coordinating oil and ice modeling efforts
Analysis and Synthesis of Effective Human-Robot Interaction at Varying Levels in Control Hierarchy
Robot controller design is usually hierarchical with both high-level task and motion planning and low-level control law design. In the presented works, we investigate methods for low-level and high-level control designs to guarantee joint performance of human-robot interaction (HRI). In the first work, a low-level method using the switched linear quadratic regulator (SLQR), an optimal control policy based on a quadratic cost function, is used. By incorporating measures of robot performance and human workload, it can be determined when to utilize the human operator in a method that improves overall task performance while reducing operator workload. This method is demonstrated via simulation using the complex dynamics of an autonomous underwater vehicle (AUV), showing this method can successfully overcome such scenarios while maintaining reduced workload. An extension of this work to path planning is also presented for the purposes of obstacle avoidance with simulation showing human planning successfully guiding the AUV around obstacles to reach its goals. In the high-level approach, formal methods are applied to a scenario where an operator oversees a group of mobile robots as they navigate an unknown environment. Autonomy in this scenario uses specifications written in linear temporal logic (LTL) to conduct symbolic motion planning in a guaranteed safe, though very conservative, approach. A human operator, using gathered environmental data, is able to produce a more efficient path. To aid in task decomposition and real-time switching, a dynamic human trust model is used. Simulations are given showing the successful implementation of this method
3D reconstruction and motion estimation using forward looking sonar
Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains
including archaeology, oil and gas industry, coral reef monitoring, harbour’s security,
and mine countermeasure missions. As electromagnetic signals do not penetrate
underwater environment, GPS signals cannot be used for AUV navigation, and optical
cameras have very short range underwater which limits their use in most underwater
environments.
Motion estimation for AUVs is a critical requirement for successful vehicle recovery
and meaningful data collection. Classical inertial sensors, usually used for AUV motion
estimation, suffer from large drift error. On the other hand, accurate inertial sensors are
very expensive which limits their deployment to costly AUVs. Furthermore, acoustic
positioning systems (APS) used for AUV navigation require costly installation and
calibration. Moreover, they have poor performance in terms of the inferred resolution.
Underwater 3D imaging is another challenge in AUV industry as 3D information is
increasingly demanded to accomplish different AUV missions. Different systems have
been proposed for underwater 3D imaging, such as planar-array sonar and T-configured
3D sonar. While the former features good resolution in general, it is very expensive and
requires huge computational power, the later is cheaper implementation but requires
long time for full 3D scan even in short ranges.
In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by
proposing relatively affordable methodologies and study different parameters affecting
their performance. We introduce a new motion estimation framework for AUVs which
relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an
Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on
forward looking sonars; the proposed system features cheaper implementation than
planar array sonars and solves the delay problem in T configured 3D sonars
Underwater Vehicles
For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties
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
A Comprehensive Review on Autonomous Navigation
The field of autonomous mobile robots has undergone dramatic advancements
over the past decades. Despite achieving important milestones, several
challenges are yet to be addressed. Aggregating the achievements of the robotic
community as survey papers is vital to keep the track of current
state-of-the-art and the challenges that must be tackled in the future. This
paper tries to provide a comprehensive review of autonomous mobile robots
covering topics such as sensor types, mobile robot platforms, simulation tools,
path planning and following, sensor fusion methods, obstacle avoidance, and
SLAM. The urge to present a survey paper is twofold. First, autonomous
navigation field evolves fast so writing survey papers regularly is crucial to
keep the research community well-aware of the current status of this field.
Second, deep learning methods have revolutionized many fields including
autonomous navigation. Therefore, it is necessary to give an appropriate
treatment of the role of deep learning in autonomous navigation as well which
is covered in this paper. Future works and research gaps will also be
discussed
Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), volume 1
This document contains papers presented at the Space Operations, Applications and Research Symposium (SOAR) Symposium hosted by NASA/Johnson Space Center (JSC) on August 3-5, 1993, and held at JSC Gilruth Recreation Center. SOAR included NASA and USAF programmatic overview, plenary session, panel discussions, panel sessions, and exhibits. It invited technical papers in support of U.S. Army, U.S. Navy, Department of Energy, NASA, and USAF programs in the following areas: robotics and telepresence, automation and intelligent systems, human factors, life support, and space maintenance and servicing. SOAR was concerned with Government-sponsored research and development relevant to aerospace operations. More than 100 technical papers, 17 exhibits, a plenary session, several panel discussions, and several keynote speeches were included in SOAR '93
Feature relative navigation for automous underwater vehicles
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1997.Includes bibliographical references (leaves 184-196).by Andrew Arnold Bennett.Ph.D
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