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

통합형 무인 수상선-케이블-수중선 시스템의 다물체동역학 거동 및 제어

Underwater exploration is becoming more and more important, since a vast range of unknown resources in the deep ocean remain undeveloped. This dissertation thus presents a modeling of the coupled dynamics of an Unmanned Surface Vehicle (USV) system with an Underwater Vehicles (UV) connected by an underwater cable (UC). The complexity of this multi-body dynamics system and ocean environments is very difficult to model. First, for modeling this, dynamics analysis was performed on each subsystem and further total coupled system dynamics were studied. The UV which is towed by a UC is modeled with 6-DOF equations of motion that reflects its hydrodynamic characteristic was studied. The 4th-order Runge–Kutta numerical method was used to analyze the motion of the USV with its hydrodynamic coefficients which were obtained through experiments and from the literature. To analyze the effect of the UC, the complicated nonlinear and coupled UC dynamics under currents forces, the governing equations of the UC dynamics are established based on the catenary equation method, then it is solved by applying the shooting method. The new formulation and solution of the UC dynamics yields the three dimensional position and forces of the UC end point under the current forces. Also, the advantage of the proposed method is that the catenary equations using shooting method can be solved in real time such that the calculated position and forces of UC according to time can be directly utilized to calculate the UV motion. The proposed method offers advantages of simple formulation, convenient use, and fast calculation time with exact result. Some simple numerical simulations were conducted to observe the dynamic behaviors of AUV with cable effects. The simulations results clearly reveal that the UC can greatly influence the motions of the vehicles, especially on the UV motions. Based on both the numerical model and simulation results developed in the dissertation, we may offer some valuable information for the operation of the UV and USV. Secondly, for the design controller, a PD controller and its application to automatic berthing control of USV are also studied. For this, a nonlinear mathematical model for the maneuvering of USV in the presence of environmental forces was firstly established. Then, in order to control rudder and propeller during automatic berthing process, a PD control algorithm is applied. The algorithm consists of two parts, the forward velocity control and heading angle control. The control algorithm was designed based on the longitudinal and yaw dynamic models of USV. The desired heading angle was obtained by the so-called “Line of Sight” method. To support the validity of the proposed method, the computer simulations of automatic USV berthing are carried out. The results of simulation showed good performance of the developed berthing control system. Also, a hovering-type AUV equipped with multiple thrusters should maintain the specified position and orientation in order to perform given tasks by applying a dynamic positioning (DP) system. Besides, the control allocation algorithm based on a scaling factor is presented for distributing the forces required by the control law onto the available set of actuators in the most effective and energy efficient way. Thus, it is necessary for the robust control algorithm to conduct successfully given missions in spite of a model uncertainty and a disturbance. In this dissertation, the robust DP control algorithm based on a sliding mode theory is also addressed to guarantee the stability and better performance despite the model uncertainty and disturbance of current and cable effects. Finally, a series of simulations are conducted to verify the availability of the generated trajectories and performance of the designed robust controller. Thirdly, for the navigation of UV, a method for designing the path tracking controller using a Rapidly-exploring Random Trees (RRT) algorithm is proposed. The RRT algorithm is firstly used for the generation of collision free waypoints. Next, the unnecessary waypoints are removed by a simple path pruning algorithm generating a piecewise linear path. After that, a path smoothing algorithm utilizing cubic Bezier spiral curves to generate a continuous curvature path that satisfies the minimum radius of curvature constraint of underwater is implemented. The angle between two waypoints is the only information required for the generation of the continuous curvature path. In order to underwater vehicle follow the reference path, the path tracking controller using the global Sliding Mode Control (SMC) approach is designed. To verify the performance of the proposed algorithm, some simulation results are performed. Simulation results showed that the RRT algorithm could be applied to generate an optimal path in a complex ocean environment with multiple obstacles.Acknowledgement .................................................................................................. vi Abstract……. ....................................................................................... ………….viii Nomenclature ....................................................................................................... xvi List of Abbreviations ........................................................................................... xxi List of Tables ...................................................................................................... xxiii List of Figures ..................................................................................................... xxiv Chapter 1: Introduction ......................................................................................... 1 1.1 Background .................................................................................................. 1 1.1.1 Unmanned Surface Vehicles (USVs) ...................................................... 1 1.1.2 Umbilical Cable ....................................................................................... 4 1.1.3 Unmanned Underwater Vehicles (UUVs) ............................................... 5 1.1.4 Literature on Modeling of Marine Vehicles ............................................ 9 1.1.5 Literature on Control and Guidance of Marine Vehicles ...................... 11 1.2 Our System Architecture ........................................................................... 12 1.3 Motivation ................................................................................................. 13 1.4 Contribution ............................................................................................... 16 1.5 Publications Associated to the Dissertation .............................................. 17 1.6 Structure of the Dissertation ...................................................................... 18 Chapter 2: Mathematical Model of Unmanned Surface Vehicle (USV) ......... 20 2.1 Basic Assumptions .................................................................................... 20 2.2 Three Coordinate Systems ......................................................................... 20 2.3 Variable Notation ...................................................................................... 22 2.4 Kinematics ................................................................................................. 23 2.5 Kinetics ...................................................................................................... 26 2.5.1 Rigid Body Equations of Motion ........................................................... 26 2.5.2 Hydrodynamic Forces and Moments ..................................................... 28 2.5.3 Restoring Forces and Moments ............................................................. 31 2.5.4 Environmental Disturbances .................................................................. 32 2.5.5 Propulsion Forces and Moments ........................................................... 35 2.6 Nonlinear 6DOF Dynamics ....................................................................... 35 2.7 Mathematical Model of USV in 3 DOF .................................................... 36 2.7.1 Planar Kinematics .................................................................................. 36 2.7.2 Planar Nonlinear 3 DOF Dynamics ....................................................... 38 2.8 Configuration of Thrusters ........................................................................ 40 2.9 General Structure and Model Parameters .................................................. 41 2.9.1 Structure of USV ................................................................................... 41 2.9.2 Control System of USV ......................................................................... 42 2.9.3 Winch Control System ........................................................................... 43 Chapter 3: Mathematical Model of the Umbilical Cable (UC) ........................ 45 3.1 Basic Assumptions for UC ........................................................................ 45 3.2 Analysis on Forces of UV ......................................................................... 47 3.3 Relation for UC Equilibrium ..................................................................... 50 3.4 Catenary Equation in the Space Case ........................................................ 51 3.5 Shooting Method ....................................................................................... 55 3.6 Boundary Conditions ................................................................................. 57 3.7 Cable Effects ............................................................................................. 58 3.8 Model Parameters and Simulation ............................................................. 59 Chapter 4: Mathematical Model of Underwater Vehicle (UV) ........................ 63 4.1 Background ................................................................................................ 63 4.1.1 Basic Assumptions................................................................................. 63 4.1.2 Reference Frames .................................................................................. 64 4.1.3 Notations ................................................................................................ 65 4.2 Kinematics Equations ................................................................................ 66 4.3 Kinetic Equations ...................................................................................... 67 4.3.1 Rigid-Body Kinetics .............................................................................. 67 4.3.2 Hydrostatic Terms ................................................................................. 69 4.3.3 Hydrodynamic Terms ............................................................................ 70 4.3.4 Actuator Modeling ................................................................................. 75 4.3.5 Umbilical Cable Forces ......................................................................... 75 4.4 Nonlinear Equations of Motion (6DOF) ................................................... 76 4.5 Simplification of UV Dynamic Model ...................................................... 77 4.5.1 Simplifying the Mass and Inertia Matrix ............................................... 78 4.5.2 Simplifying the Hydrodynamic Damping Matrix.................................. 79 4.5.3 Simplifying the Gravitational and Buoyancy Vector ............................ 80 4.6 Thruster Modeling ..................................................................................... 80 4.7 Current Modeling ...................................................................................... 83 4.8 Dynamic Model Including Ocean Currents ............................................... 84 4.9 Complete Motion Equations of AUV (6DOF) .......................................... 89 4.10 Dynamics Model Parameter Identification ................................................ 91 4.11 Numerical Solution for Equations of Motion ............................................ 93 4.12 General Structure and Model Parameters .................................................. 94 4.12.1 Structure of AUV ............................................................................... 94 4.12.2 Control System of AUV ..................................................................... 96 Chapter 5: Guidance Theory ............................................................................... 97 5.1 Configuration of GNC System .................................................................. 97 5.1.1 Guidance ................................................................................................ 98 5.1.2 Navigation .............................................................................................. 98 5.1.3 Control ................................................................................................... 98 5.2 Maneuvering Problem Statement .............................................................. 99 5.3 Guidance Objectives ................................................................................ 100 5.3.1 Target Tracking ................................................................................... 100 5.3.2 Trajectory Tracking ............................................................................. 100 5.4 Waypoint Representation ........................................................................ 101 5.5 Path Following ......................................................................................... 102 5.6 Line of Sight (LOS) Waypoint Guidance ................................................ 102 5.6.1 Enclosure-Based Steering .................................................................... 104 5.6.2 Look-ahead Based Steering ................................................................. 105 5.6.3 LOS Control......................................................................................... 106 5.7 Cubic Polynomial for Path-Following ..................................................... 107 Chapter 6: Control Algorithm Design and Analysis ....................................... 110 6.1 Proportional Integral Differential (PID) Controller ................................ 110 6.1.1 General Theory .................................................................................... 110 6.1.2 Stability of General PID Controller ..................................................... 112 6.1.3 PID Tuning .......................................................................................... 114 6.1.4 Nonlinear PID for Marine Vehicles ..................................................... 116 6.1.5 Nonlinear PD for Marine Vehicles ...................................................... 117 6.1.6 Stability of Designed PD Controller .................................................... 117 6.2 Sliding Mode Controller .......................................................................... 118 6.2.1 Tracking Error and Sliding Surface ..................................................... 119 6.2.2 Chattering Situation ............................................................................. 120 6.2.3 Control Law and Stability .................................................................... 121 6.3 Allocation Control ................................................................................... 124 6.3.1 Linear Quadratic Unconstrained Control Allocation Using Lagrange Multipliers ................................................................................................ 125 6.3.2 Thruster Allocation with a Constrained Linear Model ........................ 127 6.4 Simulation Results and Discussion ......................................................... 131 6.4.1 Berthing (parking) Control of USV ..................................................... 133 6.4.2 Motion Control of UV ......................................................................... 136 Chapter 7: Obstacle Avoidance and Path Planning for Vehicle Using Rapidly-Exploring Random Trees Algorithm.................................................................. 168 7.1 Path Planning and Guidance: Two Interrelated Problems ....................... 168 7.2 RRT Algorithm for Exploration .............................................................. 171 7.2.1 Random Node Selection ...................................................................... 172 7.2.2 Nearest Neighbor Node Selection ....................................................... 173 7.2.3 RRT Exploration with Obstacles ......................................................... 174 7.3 RRT Algorithm for Navigation of AUV ................................................. 176 7.3.1 Basic RRT Algorithm .......................................................................... 176 7.3.2 Biased-Greedy RRT Algorithm ........................................................... 178 7.3.3 Synchronized Biased-Greedy RRT Algorithm .................................... 179 7.4 Path Pruning ............................................................................................ 182 7.4.1 Path Pruning Using LOS ..................................................................... 182 7.4.2 Global Path Pruning ............................................................................. 183 7.5 Summarize the Proposed RRT Algorithm ............................................... 185 7.6 Simulation for Path Following of AUV .................................................. 187 Chapter 8: Simulation of Complete USV-UC-UV Systems ............................ 196 8.1 Simulation Procedure .............................................................................. 196 8.2 Simulation Results and Discussion ......................................................... 201 8.2.1 Dynamic Behaviors of Complete USV (Stable)-Cable- AUV (Turning Motion) ..................................................................................................... 201 8.2.2 Dynamic Behaviors of Complete USV (Forward motion)-Cable- AUV (Turning Motion) ...................................................................................... 207 8.2.3 Applied Controller to Complete USV -Cable- AUV ........................... 215 Chapter 9: Conclusions and Future Works ..................................................... 238 9.1 Modeling of Complete USV-Cable-AUV System .................................. 238 9.2 Motion Control ........................................................................................ 239 9.3 Cable Force and Moment at the Tow Points ........................................... 239 9.4 Path Planning ........................................................................................... 239 9.5 Future Works ........................................................................................... 240Docto

Autonomous Drone Landings on an Unmanned Marine Vehicle using Deep Reinforcement Learning

This thesis describes with the integration of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV, also commonly known as drone) in a single Multi-Agent System (MAS). In marine robotics, the advantage offered by a MAS consists of exploiting the key features of a single robot to compensate for the shortcomings in the other. In this way, a USV can serve as the landing platform to alleviate the need for a UAV to be airborne for long periods time, whilst the latter can increase the overall environmental awareness thanks to the possibility to cover large portions of the prevailing environment with a camera (or more than one) mounted on it. There are numerous potential applications in which this system can be used, such as deployment in search and rescue missions, water and coastal monitoring, and reconnaissance and force protection, to name but a few. The theory developed is of a general nature. The landing manoeuvre has been accomplished mainly identifying, through artificial vision techniques, a fiducial marker placed on a flat surface serving as a landing platform. The raison d'etre for the thesis was to propose a new solution for autonomous landing that relies solely on onboard sensors and with minimum or no communications between the vehicles. To this end, initial work solved the problem while using only data from the cameras mounted on the in-flight drone. In the situation in which the tracking of the marker is interrupted, the current position of the USV is estimated and integrated into the control commands. The limitations of classic control theory used in this approached suggested the need for a new solution that empowered the flexibility of intelligent methods, such as fuzzy logic or artificial neural networks. The recent achievements obtained by deep reinforcement learning (DRL) techniques in end-to-end control in playing the Atari video-games suite represented a fascinating while challenging new way to see and address the landing problem. Therefore, novel architectures were designed for approximating the action-value function of a Q-learning algorithm and used to map raw input observation to high-level navigation actions. In this way, the UAV learnt how to land from high latitude without any human supervision, using only low-resolution grey-scale images and with a level of accuracy and robustness. Both the approaches have been implemented on a simulated test-bed based on Gazebo simulator and the model of the Parrot AR-Drone. The solution based on DRL was further verified experimentally using the Parrot Bebop 2 in a series of trials. The outcomes demonstrate that both these innovative methods are both feasible and practicable, not only in an outdoor marine scenario but also in indoor ones as well

AUV planning and calibration method considering concealment in uncertain environments

IntroductionAutonomous underwater vehicles (AUVs) are required to thoroughly scan designated areas during underwater missions. They typically follow a zig-zag trajectory to achieve full coverage. However, effective coverage can be challenging in complex environments due to the accumulation and drift of navigation errors. Possible solutions include surfacing for satellite positioning or underwater acoustic positioning using transponders on other vehicles. Nevertheless, surfacing or active acoustics can compromise stealth during reconnaissance missions in hostile areas by revealing the vehicle’s location.MethodsWe propose calibration and planning strategies based on error models and acoustic positioning to address this challenge. Acoustic markers are deployed via surface ships to minimize navigation errors while maintaining stealth. And a new path planning method using a traceless Kalman filter and acoustic localization is proposed to achieve full-area coverage of AUVs. By analyzing the statistics of accumulated sensor errors, we optimize the positions of acoustic markers to communicate with AUVs and achieve better coverage. AUV trajectory concealment is achieved during detection by randomizing the USV navigation trajectory and irregularizing the locations of acoustic marker.ResultsThe proposed method enables the cumulative determination of the absolute position of a target with low localization error in a side-scan sonar-based search task. Simulations based on large-scale maps demonstrate the effectiveness and robustness of the proposed algorithm.DiscussionSolving the problem of accumulating underwater localization errors based on inertial navigation by error modeling and acoustic calibration is a typical way. In this paper, we have implemented a method to solve the localization error in a search scenario where stealth is considered

Underwater multi-target tracking with particle filters

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Robotic platforms communication and interoperability is of relevance for marine science and industrial monitoring. We present results of a particle filter study based on underwater Multi-Target Tracking (MTT) using Autonomous Underwater Vehicles (AUV). The main goal was to assess the viability of using a single surface vehicle as a mobile landmark to track and follow a fleet of underwater targets, each one equipped with an acoustic tag where the slant ranges between the surface vehicle and the underwater targets are the unique input for the filters.Peer ReviewedPostprint (published version

Social behaviours in rat models of autism

Neurodevelopmental disorders (NDDs) manifest during early childhood and have deleterious effects on development, leading to lifelong conditions affecting attention, cognition, motor abilities, communication and social domains, often alongside physical ailments such as gastrointestinal issues and epilepsy. With a worldwide reported prevalence of around 1%, NDDs either directly or indirectly affect a large proportion of the population. Rodent models of monogenic forms of NDD provide a means for unravelling mechanisms and developing targeted therapeutics for debilitating aspects of NDDs. However, modelling social and emotional facets of NDDs such as autism spectrum disorder (ASD) remains a challenge. The rat makes a good model species for the social and emotional facets of NDDs as rats are highly social, experience a sensitive period of social and emotional development, and exhibit a behavioural repertoire that is flexible and sensitive to context. The aim of this thesis was to develop a welfarefriendly, robust assay of socio-emotional phenotype in rat models of NDD. Play behaviour appears to be a critical aspect of the developmental process in many highly social species, including both rats and humans. Children develop social skills and emotional regulation through playful peer interactions. For individuals with NDDs, including ASD, social challenges often emerge from a very young age and impair playful interactions with peers. In juvenile rats, play experience is crucial for social, emotional, and sensorimotor development. Therefore, disruptions in social and emotional function in rat models of NDD may be observable in juvenile play behaviour. While juvenile play has been well-characterised in wild-type (WT) rats, it has not yet been thoroughly investigated in rat models of NDDs. Some aspects of rat social play can be mimicked during playful interactions with humans in which the rat is ‘pinned’ by gently flipping onto the back and tickling using fine-scale tickle movements of the fingers on the ventral surface. During tickle, rats produce ultrasonic vocalisations (USVs) indicative of a positive affective state which can be used as a proxy measure of tickle responsiveness. Number of successful pin and tickle events per tickle session was used as a behavioural measure of responsiveness as rats were only pinned if they engaged with the experimenter’s hand. As an assay of social responsiveness, I investigated behavioural and USV responses to tickle in three different rat models of NDD associated with ASD: Fragile X Syndrome, SYGNAP1 haploinsufficiency, and CDKL5 Deficiency Disorder. Tickle response varied between models of NDD. Tickle response in the Fragile X Syndrome model (Fmr1-/y) was very similar to WT littermate controls, with high rates of both USVs and pin/tickle events during tickle sessions in both genotypes. In contrast, for models of SYNGAP1 haploinsufficiency (Syngap1+/DGAP and Syngap1+/-), the tickle protocol was almost impossible to carry out due to climbing behaviour by the SYNGAP1 model rats, and very few USVs were emitted during tickle sessions. WTs were receptive to tickle and emitted USVs at high rates during tickle sessions. In the CDKL5 Deficiency Disorder model (Cdkl5-/y), both WT and Cdkl5-/y rats emitted very few USVs, and behavioural and USV responses were more variable in Cdkl5-/y than in WTs. Because the tickle paradigm involves habituation to a novel environment and experimenter handling, reduced tickle responsiveness may not be indicative of playfulness or social responsiveness in general but could instead reflect an impairment in habituation, since tickle response is highly sensitive to emotional state. To address this possibility, I developed a novel paradigm which allows all experimental manipulations and observations to be carried out in a spatially complex home environment, minimising handling and exposure to novel experimental environments. Play behaviour was observed for the first 2 hours of the dark phase over a 4-week period following three experimental conditions: 24hr isolation, a negative control condition, and an undisturbed condition. In WT rats, brief isolation reliably elicits a transient increase in play, termed the rebound effect. As isolation is stressful, the rebound effect is thought to reflect an immediate benefit of play as a behavioural stress-reduction mechanism. I hypothesised that Cdkl5-/y rats may not use this behavioural strategy to reduce stress following isolation, or alternatively, that they would not find social isolation as stressful as their WT littermates. I predicted that Cdkl5-/y pairs would show less of a play rebound effect than their WT littermates. Unexpectedly, my results suggest that both WT and Cdkl5-/y pairs exhibit the expected rebound effect in response to brief (24hr) isolation. Further characterisation of play behaviour revealed that pairs of Cdkl5-/y rats engage in more frequent play bouts, but play for a similar amount of time as WT littermate pairs, and these parameters were affected differently by treatment condition in WT and Cdkl5-/y pairs. Detailed snapshot and longitudinal analysis of play behaviour indicates that the temporal dynamics and sequencing of play in Cdkl5-/y pairs differs from WT littermates, and that the developmental trajectory of play behaviour may diverge from WT in Cdkl5-/y pairs. Overall, this thesis provides evidence that rat models of NDD behave differently in social contexts than WT animals and highlights the benefit of ethologically relevant outcome measures and minimally invasive test environments for uncovering subtle social and emotional phenotypes in rat models of NDD

Optimal and receding-horizon path planning algorithms for communications relay vehicles in complex environments

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 97-100).This thesis presents new algorithms for path planning in a communications constrained environment for teams of unmanned vehicles. This problem involves a lead vehicle that must gather information from a set of locations and relay it back to its operator. In general, these locations and the lead vehicle's position are beyond line of-sight from the operator and non-stationary, which introduces several difficulties to the problem. The proposed solution is to use several additional unmanned vehicles to create a network linkage between the operator and the lead vehicle that can be used to relay information between the two endpoints. Because the operating environment is cluttered with obstacles that block both line-of-sight and vehicle movement, the paths of the vehicles must be carefully planned to meet all constraints. The core problem of interest that is addressed in this thesis is the path planning for these link vehicles. Two solutions are presented in this thesis. The first is a centralized approach based on a numerical solution of optimal control theory. This thesis presents an optimal control problem formulation that balances the competing objectives of minimizing overall mission time and minimizing energy expenditure. Also presented is a new modification of the Rapidly-Exploring Random Tree algorithm that makes it more efficient at finding paths that are applicable to the communications chaining problem. The second solution takes a distributed, receding-horizon approach, where each vehicle solves for its own path using a local optimization that helps the system as a whole achieve the global objective.(cont.) This solution is applicable to real-time use onboard a team of vehicles. To offset the loss of optimality from this approach, a new heuristic is developed for the linking vehicles. Finally, both solutions are demonstrated in simulation and in flight tests in MIT's RAVEN testbed. These simulations and flight tests demonstrate the performance of the two solution methods as well as their viability for use in real unmanned vehicle systems.by Karl Christian Kulling.S.M

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