353 research outputs found
A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES
The work in this thesis is concerned with the development of a novel and practical collision
avoidance system for autonomous underwater vehicles (AUVs). Synergistically,
advanced stochastic motion planning methods, dynamics quantisation approaches,
multivariable tracking controller designs, sonar data processing and workspace representation,
are combined to enhance significantly the survivability of modern AUVs.
The recent proliferation of autonomous AUV deployments for various missions such
as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial
increase in vehicle autonomy. One matching requirement of such missions is
to allow all the AUV to navigate safely in a dynamic and unstructured environment.
Therefore, it is vital that a robust and effective collision avoidance system should be
forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously
increasing its autonomy.
This thesis not only provides a holistic framework but also an arsenal of computational
techniques in the design of a collision avoidance system for AUVs. The
design of an obstacle avoidance system is first addressed. The core paradigm is the
application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly
developed version for use as a motion planning tool. Later, this technique is merged
with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages
of the RRT. A novel multi-node version which can also address time varying
final state is suggested. Clearly, the reference trajectory generated by the aforementioned
embedded planner must be tracked. Hence, the feasibility of employing the
linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent
Ricatti equation (SDRE) controller as trajectory trackers are explored.
The obstacle detection module, which comprises of sonar processing and workspace
representation submodules, is developed and tested on actual sonar data acquired
in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing
techniques applied are fundamentally derived from the image processing perspective.
Likewise, a novel occupancy grid using nonlinear function is proposed for the
workspace representation of the AUV. Results are presented that demonstrate the
ability of an AUV to navigate a complex environment.
To the author's knowledge, it is the first time the above newly developed methodologies
have been applied to an A UV collision avoidance system, and, therefore, it is
considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT
PERFORMANCE EVALUATION AND REVIEW FRAMEWORK OF ROBOTIC MISSIONS (PERFORM): AUTONOMOUS PATH PLANNING AND AUTONOMY PERFORMANCE EVALUATION
The scope of this work spans two main areas of autonomy research 1) autonomous path planning and 2) test and evaluation of autonomous systems. Path planning is an integral part of autonomous decision-making, and a deep understanding in this area provides valuable perspective on approaching the problem of how to effectively evaluate vehicle behavior.
Autonomous decision-making capabilities must include reliability, robustness, and trustworthiness in a real-world environment. A major component of robot decision-making lies in intelligent path-planning. Serving as the brains of an autonomous system, an efficient and reliable path planner is crucial to mission success and overall safety. A hybrid global and local planner is implemented using a combination of the Potential Field Method (PFM) and A-star (A*) algorithms. Created using a layered vector field strategy, this allows for flexibility along with the ability to add and remove layers to take into account other parameters such as currents, wind, dynamics, and the International Regulations for Preventing Collisions at Sea (COLGREGS). Different weights can be attributed to each layer based on the determined level of importance in a hierarchical manner. Different obstacle scenarios are shown in simulation, and proof-of-concept validation of the path-planning algorithms on an actual ASV is accomplished in an indoor environment. Results show that the combination of PFM and A* complement each other to generate a successfully planned path to goal that alleviates local minima and entrapment issues. Additionally, the planner demonstrates the ability to update for new obstacles in real time using an obstacle detection sensor.
Regarding test and evaluation of autonomous vehicles, trust and confidence in autonomous behavior is required to send autonomous vehicles into operational missions. The author introduces the Performance Evaluation and Review Framework Of Robotic Missions (PERFORM), a framework for which to enable a rigorous and replicable autonomy test environment, thereby filling the void between that of merely simulating autonomy and that of completing true field missions. A generic architecture for defining the missions under test is proposed and a unique Interval Type-2 Fuzzy Logic approach is used as the foundation for the mathematically rigorous autonomy evaluation framework. The test environment is designed to aid in (1) new technology development (i.e. providing direct comparisons and quantitative evaluations of varying autonomy algorithms), (2) the validation of the performance of specific autonomous platforms, and (3) the selection of the appropriate robotic platform(s) for a given mission type (e.g. for surveying, surveillance, search and rescue). Several case studies are presented to apply the metric to various test scenarios. Results demonstrate the flexibility of the technique with the ability to tailor tests to the user’s design requirements accounting for different priorities related to acceptable risks and goals of a given mission
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
Risk analysis and decision making for autonomous underwater vehicles
Risk analysis for autonomous underwater vehicles (AUVs) is essential to enable AUVs to
explore extreme and dynamic environments. This research aims to augment existing risk
analysis methods for AUVs, and it proposes a suite of methods to quantify mission risks and to
support the implementation of safety-based decision making strategies for AUVs in harsh
marine environments. This research firstly provides a systematic review of past progress of risk
analysis research for AUV operations. The review answers key questions including fundamental
concepts and evolving methods in the domain of risk analysis for AUVs, and it highlights future
research trends to bridge existing gaps. Based on the state-of-the-art research, a copula-based
approach is proposed for predicting the risk of AUV loss in underwater environments. The
developed copula Bayesian network (CBN) aims to handle non-linear dependencies among
environmental variables and inherent technical failures for AUVs, and therefore achieve
accurate risk estimation for vehicle loss given various environmental observations. Furthermore,
path planning for AUVs is an effective decision making strategy for mitigating risks and
ensuring safer routing. A further study presents an offboard risk-based path planning approach
for AUVs, considering a challenging environment with oil spill scenarios incorporated. The
proposed global Risk-A* planner combines a Bayesian-based risk model for probabilistic risk
reasoning and an A*-based algorithm for path searching. However, global path planning
designed for static environments cannot handle the unpredictable situations that may emerge,
and real-time replanned solutions are required to account for dynamic environmental
observations. Therefore, a hybrid risk-aware decision making strategy is investigated for AUVs
to combine static global planning with dynamic local re-planning. A dynamic risk analysis
model based on the system theoretic process analysis (STPA) and BN is applied for generating
a real-time risk map in target mission areas. The dynamic window algorithm (DWA) serves for
local path planning to avoid moving obstacles. The proposed hybrid risk-aware decisionmaking
architecture is essential for the real-life implementation of AUVs, leading eventually to
a real-time adaptive path planning process onboard the AUV
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
Automatic Control and Routing of Marine Vessels
Due to the intensive development of the global economy, many problems are constantly emerging connected to the safety of ships’ motion in the context of increasing marine traffic. These problems seem to be especially significant for the further development of marine transportation services, with the need to considerably increase their efficiency and reliability. One of the most commonly used approaches to ensuring safety and efficiency is the wide implementation of various automated systems for guidance and control, including such popular systems as marine autopilots, dynamic positioning systems, speed control systems, automatic routing installations, etc. This Special Issue focuses on various problems related to the analysis, design, modelling, and operation of the aforementioned systems. It covers such actual problems as tracking control, path following control, ship weather routing, course keeping control, control of autonomous underwater vehicles, ship collision avoidance. These problems are investigated using methods such as neural networks, sliding mode control, genetic algorithms, L2-gain approach, optimal damping concept, fuzzy logic and others. This Special Issue is intended to present and discuss significant contemporary problems in the areas of automatic control and the routing of marine vessels
AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE
The work in this thesis concerns with the development of a novel multisensor data fusion
(MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic
and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous
underwater vehicle (AUV) navigation system, formed by an integration of global positioning
system and inertial navigation system (GPS/INS).
The Kalman filter has been a popular method for integrating the data produced
by the GPS and INS to provide optimal estimates of AUVs position and attitude. In
this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is
proposed. The former is used to fuse the data from a variety of INS sensors whose
output is used as an input to the later where integration with GPS data takes place.
The use of an adaptation scheme based on fuzzy logic approaches to cope with the
divergence problem caused by the insufficiently known a priori filter statistics is also
explored. The choice of fuzzy membership functions for the adaptation scheme is first
carried out using a heuristic approach. Single objective and multiobjective genetic
algorithm techniques are then used to optimize the parameters of the membership
functions with respect to a certain performance criteria in order to improve the overall
accuracy of the integrated navigation system. Results are presented that show
that the proposed algorithms can provide a significant improvement in the overall
navigation performance of an autonomous underwater vehicle navigation.
The proposed technique is known to be the first method used in relation to AUV
navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd.,
Qinetiq, Subsea 7 and South West Water PL
Single Input Fuzzy Logic Controller For Yaw Control Of Underwater Remotely Operated Crawler
Underwater Remotely Operated Crawler (ROC) is a class of the Unmanned Underwater Vehicle (UUV) that is tethered, unoccupied, manoeuvres on the seabed and remotely operated by a pilot from a platform. Underwater characteristic parameters such as added mass, buoyancy, hydrodynamic forces, underwater currents, including pressure could considerably affect and reduce the mobility of the ROC. The challenges faced by the ROCs are that the needs to reduce the overshoot in the system response, including, the time response and settling time. For yaw control (a motion around the z-axis), an occurrence of an overshoot in the system response is highly intolerable. Reducing the overshoot in the ROC trajectory is crucial since there are many challenging underwater natures and underwater vehicle control problems while studies on finding the solutions are still ongoing to find an improvement. Conventional Proportional-Integral-Derivative (PID) controller is not robust to be applied in the ROC due to the non-linear dynamic model of the ROC and underwater conditions. Besides that, by reducing the overshoot, the ROC mobility will be much more efficient and provided a reliable platform for underwater data mining. This study is focused to give an optimum performance of yaw control without overshoot in the system response and faster time response. This research begins by designing an underwater ROC as the research’s platform. Then, the designed ROC is simulated by using SolidWorks software obtain the analysis of structural integrity and hydrodynamic properties. System identification technique is conducted to obtain the empirical modelling design of the fabricated ROC which equipped with Inertial Measurement Unit (IMU) sensor. The fuzzy logic controller (FLC) is designed based on 5 by 5 rule matrix which has to deal with fuzzification, rule base, inference mechanism and defuzzification operations. A simplification of the FLC is proposed and the method is called Single Input Fuzzy Logic Controller (SIFLC). The simplification is achieved by applying the “signed distance method” where the SIFLC reduces the two-input FLC to a single input FLC. In other words, SIFLC is based on the signed distance method which eventually reduces the controller as single input-single output (SISO) controller. A PID controller is designed for the purpose of benchmarking with the FLC and SIFLC. SIFLC has the capability to adapt the non-linear underwater parameters (currents, waves and etc.). This research has discussed and compared the performance of PID, FLC and SIFLC. The algorithm is verified in MATLAB/Simulink software. Based on the results, SIFLC provides more robust and reliable control system. Based on the computation results, SIFLC reduces the percentage of overshoot (%OS) of the system and achieve 0.121%, while other controllers (PID and FLC) 4.4% and 1.7% respectively. Even that so, this does not mean that PID and FLC are not reliable but due to the presence of %OS
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