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This paper presents an independent stereo-vision based positioning system for docking operations. The low-cost system consists of an object detector and different 3D reconstruction techniques. To address the challenge of robust detections in an unstructured and complex outdoor environment, a learning-based object detection model is proposed. The system employs a complementary modular approach that uses data-driven methods, utilizing data wherever required and traditional computer vision methods when the scope and complexity of the environment are reduced. Both, monocular and stereo-vision based methods are investigated for comparison. Furthermore, easily identifiable markers are utilized to obtain reference points, thus simplifying the localization task. A small unmanned surface vehicle (USV) with a LiDAR-based positioning system was exploited to verify that the proposed vision-based positioning system produces accurate measurements under various docking scenarios. Field experiments have proven that the developed system performs well and can supplement the traditional navigation system for safety-critical docking operations.

In this paper a multiple object detection, recognition, and tracking system for unmanned aerial vehicles (UAVs) has been studied. The system can be implemented on any UAVs platform, with the main requirement being that the UAV has a suitable onboard computational unit and a camera. It is intended to be used in a maritime object tracking system framework for UAVs, which enables a UAV to perform multiobject tracking and situational awareness of the sea surface, in real time, during a UAV operation. Using machine vision to automatically detect objects in the camera's image stream combined with the UAV's navigation data, the onboard computer is able to georeference each object detection to measure the location of the detected objects in a local North‐East (NE) coordinate frame. A tracking algorithm which uses a Kalman filter and a constant velocity motion model utilizes an object's position measurements, automatically found using the object detection algorithm, to track and estimate an object's position and velocity. Furthermore, a global‐nearest‐neighbor algorithm is applied for data association. This is achieved using a measure of distance that is based not only on the physical distance between an object's estimated position and the measured position, but also how similar the objects appear in the camera image. Four field tests were conducted at sea to verify the object detection and tracking system. One of the flight tests was a two‐object tracking scenario, which is also used in three scenarios with an additional two simulated objects. The tracking results demonstrate the effectiveness of using visual recognition for data association to avoid interchanging the two estimated object trajectories. Furthermore, real‐time computations performed on the gathered data show that the system is able to automatically detect and track the position and velocity of a boat. Given that the system had at least 100 georeferenced measurements of the boat's position, the position was estimated and tracked with an accuracy of 5–15 m from 400 m altitude while the boat was in the camera's field of view (FOV). The estimated speed and course would also converge to the object's true trajectories (measured by Global Positioning System, GPS) for the tested scenarios. This enables the system to track boats while they are outside the FOV of the camera for extended periods of time, with tracking results showing a drift in the boat's position estimate down to 1–5 m/min outside of the FOV of the camera. © 2020 The Authors. Journal of Field Robotics published by Wiley Periodicals LLC This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

This paper presents a method for estimating the air data parameters for a small fixed-wing, unmanned aerial vehicle (UAV) using an arrangement of low-cost Micro-electromechinal systems (MEMS)-based pressure sensors embedded in the surface of the UAV. The pressure measurements are used in a machine learning (ML) model to estimate the angle of attack, sideslip angle, and airspeed. Two ML algorithms based on artificial neural networks (NNs) and linear regression (LR) are implemented, tested, and assessed using data collected from wind tunnel experiments and a flight test, and the results are compared to a benchmark flight test. Training the ML algorithms using wind tunnel data was found to introduce several potential error sources that need to be addressed in order to provide accurate estimation on the benchmark flight test, whereas training the algorithms using flight data provides lower estimation RMSE values. The performance of the NN structures has been found to slightly outperform the LR algorithms in estimation accuracy. Finally, results from using different sensor configurations and a pseudo-Reynolds number are presented in an effort to evaluate the influence of sensor number and placement on the accuracy of the method.

An ever-increasing number of autonomous vehicles use bandwidth-greedy sensors such as cameras and LiDARs to sense and act to the world around us. Unfortunately, signal transmission in vehicles is vulnerable to passive and active cyber-physical attacks that may result in loss of intellectual property, or worse yet, the loss of control of a vehicle, potentially causing great harm. Therefore, it is important to investigate efficient cryptographic methods to secure signal transmission in such vehicles against outside threats. This study is motivated by the observation that previous publications have suggested legacy algorithms, which are either inefficient or insecure for vision-based signals. We show how stream ciphers and authenticated encryption can be applied to transfer sensor data securely and efficiently between computing devices suitable for distributed guidance, navigation, and control systems. We provide an efficient and flexible pipeline of cryptographic operations on image and point cloud data in the Robot Operating System (ROS). We also demonstrate how image data can be compressed to reduce the amount of data to be encrypted, transmitted, and decrypted. Experiments on embedded computers verify that modern software cryptographic algorithms perform very well on large sensor data. Hence, the introduction of such algorithms should enhance security without significantly compromising the overall performance.

The technology of hydrodynamic modeling and marine craft motion control systems has progressed greatly in recent years. This timely survey includes the latest tools for analysis and design of advanced guidance, navigation and control systems and presents new material on underwater vehicles and surface vessels. Each section presents numerous case studies and applications, providing a practical understanding of how model-based motion control systems are designed.

This paper addresses the problem of ship motion estimation using live data from Automatic Identification Systems (AIS). A globally exponentially stable observer for visualization and motion prediction of ships has been designed. Instead of using the extended Kalman filter (EKF) to deal with the kinematic nonlinearities the eXogenous Kalman Filter (XKF) is applied and by this global stability properties are proven. The proposed observer was validated using live AIS data from the Trondheim harbor in Norway and it was demonstrated that the observer tracks ships in real time. It was also demonstrated that the observer can predict the future motion of ships. The motion prediction capabilities are very useful for decision-support systems since this can be used to improve situational awareness e.g. for manned and unmanned autonomous ships that operate in common waters. © 2018 Norwegian Society of Automatic Control. All articles in MIC are published with the Creative Commons Attribution 3.0 Unported (CC BY 3.0) license. See: http://creativecommons.org/licenses/by/3.0/.

This paper presents a passivity based formation control strategy for multiple unmanned aerial vehicles (UAVs) cooperatively carrying a suspended load. The control strategy we propose consists of an internal feedback control law for each UAV and a formation control law that regulates the relative position between each UAV. We show that under this control strategy the interconnected system has a continuum of equilibria and prove stability for all equilibrium points where the cables supporting the suspended load are in tension. This is a submitted manuscript of an article published by Elsevier Ltd in IFAC-PapersOnLine, 18 October 2017.

There exist numerous navigation solutions already implemented into various navigation systems. Depending on the vehicle in which the navigation system is used, it can be distinguished in most cases among; navigation, tactical, and commercial grade categories of such systems. The core of these systems is formed by inertial sensors, i.e. accelerometers and angular rate sensors/gyros. Navigation and tactical grade systems commonly rely on fiber optic/ring laser gyros and servo/quartz accelerometers with high resolution, sensitivity, and stability. In the case of cost-effective navigation systems, for example piloted light and ultralight aircraft, usually use commercial grade sensors, where the situation differs. The sensor outputs are less stable and sensitive, and suffer from manufacturing limits leading to temperature dependency, bias instability, and misalignment which introduces non-negligible disturbances. These conditions commonly limit the applicability of the navigation solution since its stand-alone operation using free integration of accelerations and angular rates is not stable. This paper addresses a cost-effective solution with commercial grade inertial sensors, and studies the performance of different approaches to obtain navigation solution with robustness to GNSS outages. A main goal of this paper is thus comparison of a nonlinear observer and two extended Kalman filter solutions with respect to the accuracy of estimated quantities and their sensitivity to GNSS outages. The performance analyses are carried out on real flight data and evaluated during phases of the flight when the solutions are challenged by different environmental disturbances.

A global nonlinear algebraic transform of nonlinear pseudorange measurement equations enables navigation solutions based on a globally valid linear time-varying measurement model. Using an interconnection of a nonlinear attitude observer and a translational motion observer based on pseudorange and range–range measurements, a tightly coupled integrated aided inertial navigation system is designed. The attitude observer uses a proper acceleration estimate from the translational motion observer as a reference vector for the accelerometer measurement. This leads to a feedback interconnection that is shown to be globally exponentially stable under some conditions on the tuning parameters. The model transformation has eliminated the information about a certain nonlinear relationship that exists among the measurements. While this enables the global solution to be found, it also leads to loss of estimation accuracy when there is measurement noise. In order to recover close-to-optimal (minimum variance) estimates, the observer estimates are only used to generate a locally linearized time-varying model that is subsequently employed by a Kalman filter, without loss of global convergence.

This paper presents a vision-aided uniformly semiglobally exponentially stable nonlinear observer for estimation of attitude, gyro bias, position, velocity, and specific force of a fixed-wing unmanned aerial vehicle, using measurements from an inertial measurement unit, a global navigation satellite system receiver, and computer vision. The computer vision uses optical flow from consecutive images from a camera together with the continuous epipolar constraint to calculate the scaled body-fixed linear velocity, namely, the direction of travel. Epipolar geometry eliminates dependency on the distance to objects in the images and the structure of the terrain being recorded, meaning there is no restriction on the types of terrain for which the observer is applicable. Experimental data from an unmanned aerial vehicle test flight and simulated data are presented, showing that the proposed nonlinear observer has robust performance. Experimental results are compared with an extended Kalman filter and illustrate th...

The estimation of a vehicle's position and velocity based on measurements of pseudoranges and range-rates is a nonlinear filtering problem. We utilize a linearized Kalman filter with global convergence that avoids divergence because the local model linearization is not based on the linearized Kalman filter's own estimates. Instead, the linearization is made about the estimates of another suboptimal Kalman filter that is based on a globally valid quasi-linear time-varying measurement model.

When a ship experiences a loss of position reference systems, its navigation system typically enters a mode known as dead reckoning (DR) to maintain an estimate of its position. Commercial systems perform this task using a state estimator that includes mathematical model knowledge. Such a model is nontrivial to derive and needs tuning if the vessel's dynamic properties change. To this end, we propose using machine learning to estimate the horizontal velocity of the vessel without the help of position, velocity, or acceleration sensors. A simulation study was conducted to demonstrate the ability to maintain position estimates during a Global Navigation Satellite System (GNSS) outage. Comparable performance is seen relative to the established Kalmanfilter (KF) model-based approach. © 2019 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.

This paper presents an exponentially stable nonlinear wind velocity observer for fixed-wing unmanned aerial vehicles (UAVs). The observer uses a model of the aircraft combined with a GNSS-aided inertial navigation system (INS). The INS uses an attitude observer together with a pitot static probe measuring dynamic pressure in the longitudinal direction as well as the airspeed. The observer is able to estimate the wind velocity and from this compute the relative velocity, which directly contains information about the angle of attack (AOA) and sideslip angle (SSA). The nonlinear observer is also able to estimate the scaling factor of the pitot static probe measurement and there are no requirements on persistence of excitation (PE) of the UAV maneuvers. The computational footprint is smaller than the conventional Kalman filter, which makes the algorithm well suited for embedded systems. The designed observer is proven exponentially stable under stable flight and through simulations it is verified that the estimates converge to the true values of a realistic wind velocity when there are no model errors.

We conduct full-scale experimental validation and comparison of three different nonlinear attitude observers, two of them with inherent heave estimation, in two different operational scenarios encountered by an offshore vessel in the North Sea. Two different micro-electro-mechanical inertial measurement units are the primary sensors for driving the nonlinear observers, which are aided by gyrocompasses and position reference systems. The results are compared with data from well-proven industry standard vertical reference units.

Dead reckoning capabilities are vital in ship navigation if position and heading references are unrealizable or lost. In safety critical marine operations such as dynamic positioning, the International Maritime Organization and classification societies require that the vessel possesses dead reckoning capabilities and position reference redundancy. In this paper, we conduct a full-scale experimental validation and comparison of the dead reckoning capabilities using two different high-rate and low-cost micro-electro-mechanical inertial measurement units. The full-scale experimental validation is achieved with two nonlinear observers, aided by gyrocompasses and position reference systems, in a dynamic positioning operation carried out by an offshore vessel in the North Sea. The dead reckoning performance is evaluated after ten minutes without aiding from position reference system measurements.

Launch and recovery phases pose significant challenges to the operation of UAVs, in particular, for fixed-wing UAVs operating from smaller ships or other geographically or operationally constrained sites without a proper runway. Although UAV missions can often be operated autonomously under pilot supervision via telemetry, the launch and recovery phases often require piloting by using manual remote control (RC) and the pilot's visual feedback. Availability of reliable automatic recovery functionality with predictable performance is an important step towards increased autonomy that could potentially lead to reduced operating costs (no need for a skilled RC pilot) and improved safety records and thereby enable increased use of UAVs.

Summary We present a direct and an indirect nonlinear adaptive path-following controller for marine craft based on a line-of-sight guidance principle used by ancient navigators. The control laws are implemented using hydro-acoustic relative velocity measurements as opposed to absolute velocity measurements. For this purpose, a kinematic model for relative velocity in amplitude-phase form is derived. The first contribution is an adaptive indirect controller based on a disturbance observer designed for estimation and compensation of ocean currents. The equilibrium points of the cross-track and parameter estimation errors are proven to be globally κ exponentially stable. This guarantees that the estimated drift term converges to its true value exponentially. The observer is used in conjuncture with a control law to obtain asymptotic tracking and path following in the presence of ocean currents. The second contribution is a direct adaptive integral line-of-sight controller for path following. Global convergence of the cross-track error is proven by using Barbalat's lemma, which ensures that the parameter estimation error is bounded. Both methods can be applied to the horizontal-plane motion of surface vessels and autonomous underwater vehicles. An autonomous underwater vehicle case study is included to verify the results. Copyright © 2015 John Wiley & Sons, Ltd.

A modular nonlinear observer is considered for tightly coupled integration of inertial measurements with global satellite measurements. A real-time-kinematic (RTK) approach is utilized where the rover and base station measure pseudoranges, carrier-phase, and carrier-phase derived Doppler, to be used in a dual receiver configuration. The modular observer design consists of a nonlinear attitude observer and a translational motion observer (TMO). The attitude observer represents the vehicle attitude as unit quaternions and estimates the gyro bias. A TMO based on the double-differenced measurements between the rover and base station receiver is proposed. The ambiguities introduced by the carrier-phase measurements are included in the state vector and are initially considered real valued, later to be resolved to integer values. The advantages of the nonlinear observer compared with a Kalman filter are reduced computational load, no linearization requirement of the model, and proven stability. The proposed observer is verified using experimental data from flights with an unmanned aerial vehicle, where the position estimates are shown to be within 2-4 cm of a global positioning system L 1 -based RTK reference solution. © 2019 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.

A ship's roll dynamics is very sensitive to changes in the loading conditions and a worst-case scenario is the loss of stability. This paper proposes an approach for online estimation of a ship's ...

A ship's roll dynamics is sensitive to the mass and mass distribution. Changes in these physical properties might introduce unpredictable behavior of the ship and a worst-case scenario is that the ship will capsize. In this paper, a recently proposed approach for online estimation of mass and center of mass is validated using experimental data. The experiments were performed using a scale model of a ship in a wave basin. The data were collected in free run experiments where the rudder angle was recorded and the ship's motion was measured using an inertial measurement unit. The motion measurements are used in conjunction with a model of the roll dynamics to estimate the desired properties. The estimator uses the rudder angle measurements together with an instrumental variable method to mitigate the influence of disturbances. The experimental study shows that the properties can be estimated with quite good accuracy but that variance and robustness properties can be improved further.

This paper deals with the construction of nonlinear observers for navigation purposes. We first present a globally exponentially stable observer for attitude and gyro bias, based on gyro measurements and two or more pairs of vector measurements. We avoid the well-known topological obstructions to global stability by not confining the attitude estimate to SO ( 3 ) , but rather estimating a full rotation matrix with nine degrees of freedom. We also show how the attitude can be estimated under a relaxed persistency-of-excitation condition, with a single vector measurement as a special case. Next, we use the attitude observer to construct a globally exponentially stable observer for gnss/ins integration, based on accelerometer, gyro, and magnetometer measurements, as well as gnss measurements of position and (optionally) velocity. We verify the stability properties of the design using experimental data from a light fixed-wing aircraft.

This paper provides experimental results for dead reckoning of a fixed-wing UAV using a nonlinear observer (NLO) and a more recent tool called eXogenous Kalman Filter (XKF), which uses the NLO itself as a first-stage filter. The sensors used are an IMU (accelerometers, inclinometers, and rate gyros), a camera, and an altimeter; the observed states are position, velocity, and attitude. A machine vision system provides the body-fixed velocity of the UAV. Although the calculated velocity results affected by a bias, it is necessary both for estimating the attitude and for bounding the rate of divergence of the position during dead reckoning. Gyro, accelerometer, and optical flow (OF) velocity biases are estimated, but only as long as GNSS is available. When dead reckoning begins, they are frozen at their last calculated value. The experimental results show that the position error grows at a bounded rate with the proposed estimators. © 2017 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.

The control algorithm hierarchy of motion control for over-actuated mechanical systems with a redundant set of effectors and actuators commonly includes three levels. First, a high-level motion control algorithm commands a vector of virtual control efforts (i.e. forces and moments) in order to meet the overall motion control objectives. Second, a control allocation algorithm coordinates the different effectors such that they together produce the desired virtual control efforts, if possible. Third, low-level control algorithms may be used to control each individual effector via its actuators. Control allocation offers the advantage of a modular design where the high-level motion control algorithm can be designed without detailed knowledge about the effectors and actuators. Important issues such as input saturation and rate constraints, actuator and effector fault tolerance, and meeting secondary objectives such as power efficiency and tear-and-wear minimization are handled within the control allocation algorithm. The objective of the present paper is to survey control allocation algorithms, motivated by the rapidly growing range of applications that have expanded from the aerospace and maritime industries, where control allocation has its roots, to automotive, mechatronics, and other industries. The survey classifies the different algorithms according to two main classes based on the use of linear or nonlinear models, respectively. The presence of physical constraints (e.g input saturation and rate constraints), operational constraints and secondary objectives makes optimization-based design a powerful approach. The simplest formulations allow explicit solutions to be computed using numerical linear algebra in combination with some logic and engineering solutions, while the more challenging formulations with nonlinear models or complex constraints and objectives call for iterative numerical optimization procedures. Experiences using the different methods in aerospace, maritime, automotive and other application areas are discussed. The paper ends with some perspectives on new applications and theoretical challenges.

When trying to capture the heaving motion of a marine vessel in waves, conventional position references such as global navigation satellite systems fall short in many cases. Because of design and satellite geometry, the vertical position measurement is typically inferior in both accuracy and precision compared to its horizontal counterparts. For aiding an inertial navigation system (INS), using conventional vertical position references may therefore be suboptimal. This article presents an alternative algorithm utilizing a virtual vertical reference (VVR) concept, based on the mean sea level, to aid the INS. The novelty of the algorithm arises where the VVR concept is augmented with an error model, used to improve the heave estimation performance. The motion of the vessel is estimated using an INS, employing a feedback-interconnected observer framework, based on nonlinear theory. The INS, using low-cost micro-electro-mechanical-system-based inertial measurement units, provides position, velocity and attitude in a dead-reckoning fashion, and is aided by a horizontal position reference, the VVR, and a compass to prevent the estimates from drifting. The estimation performance obtained with the observer structure is evaluated using Monte Carlo simulations and compared to preceding results. The algorithm is also validated experimentally and compared to an industry standard vertical reference unit using data collected on board an offshore vessel. © 2017. This is the authors’ accepted and refereed manuscript to the article. LOCKED until 16.10.2018 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

This paper develops a vision-based tracking system in unmanned aerial vehicles based on thermal images . The tracking system are tailored toward objects at sea and consists of three main modules that are independent. The first module is an object detection algorithm that uses image analysis techniques to detect marine vessels in thermal images and extract the center of each object. Moreover, as long as the size of the vessel is known or computed in an image where the whole vessel is visible, the center can be identified in situations where only a part of the object is visible. The pixel position of the center is used in a nonlinear state estimator to estimate the position and velocity in a world-fixed coordinate frame. This is called the filtering part of the tracking system. The state estimator is nonlinear because only two coordinates in the world-frame can be computed with the pixel coordinates. This originates from the fact that cameras are bearing-only sensors that are unable to measure range. The last module in the tracking system is data association , which is used to relate new measurements with existing tracks. The tracking system is evaluated in two different case studies. The first case study investigates three different measures for data association in a Monte Carlo simulation. The second case study concerns tracking of a single object in a field experiment, where the object detection algorithm and the filtering part of the tracking system are evaluated. The results show that the modules in the tracking system are reliable with high precision.

In this paper, we consider the problem of trajectory tracking of a multirotor Unmanned Aerial Vehicle carrying a suspended payload. The movement of the suspended payload influences the dynamics of the multirotor, which must be appropriately handled by the controller to achieve satisfactory tracking results. We derive a mathematical model of the interconnected multi-body system using Kane’s equations, and develop a non-linear tracking controller based on the backstepping technique. In addition to suppressing the effects of the swinging payload, the controller also compensates for an unknown constant wind disturbance. The origin of the tracking error is proven UGAS (Uniformly Globally Asymptotically Stable) and ULES (Uniformly Locally Exponentially Stable) through Lyapunov analysis. To reduce the swing motion of the suspended load, a nominal swing-free path is generated through open loop shaping filters, then further perturbed through a delayed feedback approach from measured load deflection angles to achieve robustness. The proposed controller structure is verified by simulations and experiments. © Springer Science+Business Media Dordrecht 2017. This is a post-peer-review, pre-copyedit version of the article, locked until 2 March 2018 due to copyright restrictions. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10846-017-0509-6

This paper presents four vision-based tracking system architectures for marine surface objects using a fixed-wing unmanned aerial vehicle (UAV) with a thermal camera mounted in a pan/tilt gimbal. The tracking systems estimate the position and velocity of an object in the North-East (NE) plane, and differ in how the measurement models are defined. The first tracking system measures the position and velocity of the target with georeferencing and optical flow. The states are estimated in a Kalman filter. A Kalman filter is also utilized in the second architecture, but only the georeferenced position is used as a measurement. A bearing-only measurement model is the basis for the third tracking system, and because the measurement model is nonlinear, an extended Kalman filter is used for state estimation. The fourth tracking system extends the bearing-only tracking system to let navigation uncertainty in the UAV position affect the target estimates in a Schmidt-Kalman filter. All tracking architectures are evaluated on data gathered at a flight experiment near the Azores islands outside of Portugal. The results show that various marine vessels can be tracked quite accurately. © Springer Verlag. The final publication is available at https://link.springer.com/article/10.1007%2Fs10846-017-0722-3. This is the authors' submitted manuscript to the article.

Navigation is a term with broad usage in everyday life and can mean the act of moving, moving through an unknown environment, ascertaining a craft's position, or even browsing on the Internet. Originally, the term meant the skill of sailing a ship, in particular steering and maneuvering, and is derived from the Latin words navis and agere, meaning "ship" and "to drive." The modern field of study of navigation in a scientific context usually relates to the ascertainment or determination of position, velocity, and orientation of some object relative to a reference point and is used for all type of crafts and vehicles, such as aircraft, ships, submarines, and cars. © 2017 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.

This paper describes an unmanned aerial vehicle (UAV) ice tracking framework for use in sea ice management applications. The framework is intended to be used in an ice management scenario where the UAV should detect and track the movement of icebergs and ice floes in an Arctic environment, and seeks to enable the UAV to do so autonomously. This is achieved by using an occupancy grid map algorithm and a locations of interest generator coupled with a Model Predictive Control (MPC) UAV path planner. The main contribution of this paper is interfacing the occupancy grid map algorithm with a machine vision object detection module in order to enable the UAV to generate an occupancy grid map of a pre-defined search area in real-time using on-board processing of UAV sensor data. Further, the paper presents a locations of interest generator module which generates locations that the UAV should investigate based on the generated occupancy grid map. These locations of interest are then used by an MPC path planner in order to make the UAV autonomously investigate and track ice features at said locations. Furthermore, the paper verifies the use of the developed ice tracking framework for autonomously detecting and tracking ice features based on thermal images captured with a UAV, as well as verifying the usefulness and role of UAVs in ice management scenarios by conducting two flight experiments. © 2017 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.

In this paper, we study the path-following problem for marine craft which are unactuated in the sway dynamics. The proposed control law is derived using the method of least squares to find an approximate solution of overdetermined systems. The controller adjusts the speed according to the distance to the path and the rate of convergence while it stabilizes the unactuated dynamics. We show the circumstances under which the proposed controller achieves path following. The origin of the error dynamics is proven to be globally asymptotically and locally exponentially stable.

In this work we have designed a signal based K-exponentially stable frequency estimator for the estimation of the wave encounter frequency at sea. The frequency estimator under consideration is a second-order filter designed for the estimation of the frequency of a sinusoid with unknown amplitude, frequency, and phase. The wave encounter frequency is estimated using measurements of the roll angle of the ship. The frequency estimator is able to estimate the frequency correctly when the ship is experiencing parametric roll resonance and when the resonance has damped out. The frequency estimator is therefore fitted with a switching gain in the frequency adaptation law. A stability proof is provided for the estimator with switching gain. The effectiveness of the designed estimator is demonstrated in a simulation study. In this simulation study we use a roll angle measurement generated by a second-order model of ship's roll dynamics. The frequency estimator is validated using experimental data.

Marine craft employing a dynamic positioning system rely on measurements from numerous position reference systems in order to maintain the craft's desired position during operation. Faults in such systems pose a serious risk. In particular, slowly emerging faults are difficult to detect and can result in vessel drive-offs, that may in turn have dire consequences. An inertial measurement unit may provide independent position and orientation information, provided that the attitude estimates of the observer are sufficiently accurate. In this paper a three-stage nonlinear observer, based on strapdown inertial measurement units aided by a position reference system with uniform semiglobal exponential stability properties, is posed. The design includes lever arm compensation of position references. Fault detection is achieved with established methodology. Simulations show that this design has the potential to detect slowly emerging position reference faults.

Ship-based Unmanned Aerial Vehicle (UAV) operations represent an important field of research which enables a large variety of mission types. Most of these operations demand a high level of endurance which normally requires the use of a fixed-wing UAV. Traditionally, a net located on the ship deck is used for recovering the fixed-wing UAV. However, there are numerous challenges when attempting autonomous landings in such environments. Waves will induce heave motion, and turbulence near the ship will make approaches challenging. In this paper, we present a concept using multirotor UAVs to move the recovery operation off the ship deck. To recover the fixed-wing UAV, a net is suspended below two coordinated multirotor UAVs which can synchronize the movement with the fixed-wing UAV. The approach trajectory can be optimized with respect to the wind direction, and turbulence caused by the ship can be avoided. In addition, the multirotor UAVs can transport the net at a certain speed along the trajectory of the fixed-wing UAV, thus decreasing the relative velocity between the net and fixed-wing UAV to reduce the forces of impact. This paper proves the proposed concept through a simulation study and a preliminary control system architecture.

This paper presents a vision-based tracking system for marine surface objects utilizing images captured in a fixed-wing unmanned aerial vehicle (UAV) with a retractable pan/tilt gimbal. A formula for calculating the North-East velocities of objects detected in the images is derived. The formula utilizes optical flow (OF) and compensates for the UAV and gimbal motions. It can be used together with georeferencing to obtain position and velocity measurements of objects detected in the images. The tracking system is suitable with both infrared and visual spectrum images. A flight experiment has been conducted near the Azores, Portugal in 2015 to gather thermal images with marine surface objects. The tracking system has been evaluated off-line and the results show that a marine vessel can be tracked in the North-East-Down (NED) plane.

In this paper, using the idea of integrator backstepping, a dynamic controller is designed for two-dimensional path maneuvering of underactuated marine craft. The geometric errors are explicitly considered in the design procedure and convergence to the path is guaranteed. The marine craft converges to the path at the expense of sacrificing the speed assignment task when the craft moves off the path. Moreover, the unactuated dynamics is demonstrated to be globally bounded. The effectiveness of the method is verified by performing simulations.

This paper demystifies how ocean currents together with wind and wave loads influence the marine craft equations of motion. In the literature there exists great confusion of the use of absolute and relative velocity terms when modeling rigid-body and hydrodynamic forces. The article is useful for engineers who want to simulate and predict the motions of marine craft exposed to wind, wave and ocean currents as well as control engineers evaluating the performance of marine craft control systems. The results are also very useful for testing and tuning of integral action time constants for compensation of ocean current and 2nd-order wave-induced drift forces.

A simple nonlinear model for variable pitch wind turbines is described. The model is aimed specifically at nonlinear control design and analysis. We propose a parametric model and subsequently describe a method to determine the parameters for any turbine that falls into the scope of the model. The result is a model that describes a turbine rotor, which is free to move in all six degrees of freedom. This freedom is required to model the new breed of floating offshore turbines. The resulting model is compared to experimental data with good results. Cyclic forces at large yaw offsets are not modeled well, however. Limitations of the model are discussed.

This paper presents a modified version of a well known line-of-sight (LOS) guidance algorithm for path-following. The proposed method employs a time-varying equation for the lookahead distance which depends on the cross-track error. A sliding mode controller is designed in order to stabilize the vehicle heading angle to the desired value specified by the guidance system. The guidance system along with the controller form a cascaded structure which is shown to be globally κ-exponentially stable when the control task is to converge to a straight line. The effectiveness of the proposed strategy is demonstrated by simulations indicating that the variable lookahead distance algorithm can contribute to obtaining a diminished oscillatory behavior around the desired path.

An efficient potential flow solver is applied to marine vessel path-planning for cluttered environments. The approach is computationally light. This is achieved by employing a simplified potential flow solution as well as generating the path online. The method relies on a simple guidance law that; (A)Regulates a target point to maintain a constant stream-function value, (B)Employ rudder control to steer a vessel towards this target. The utility of potential flow for path-planning is discussed. Simulation results are presented, showing the promise of the above approach.

Parametric roll is a dangerous resonance phenomenon for ships caused by time-varying restoring forces. The resulting large roll oscillations may endanger the ship's safety and possibly lead to capsizing. The susceptibility of container ships to parametric roll resonance has caused considerable research activities in the development of control and operational strategies to avoid large roll motions. In this paper, a novel approach to the active control of ships experiencing parametric roll resonance is presented. The methodology of extremum seeking control is used to iteratively determine the set-point for the wave encounter frequency in order to avoid the conditions for the onset and build-up of parametric roll. The desired trajectory for the wave encounter frequency is tracked by mapping it to the ship speed and heading angle by a control allocation, formulated as a constrained optimization problem, and consequently by implementing speed and heading controllers. The combined variation of the speed and heading angle is shown to efficiently drive the ship out of the parametric roll resonance region.

In this article, we have described the main components of a ship motion-control system and two particular motion-control problems that require wave filtering, namely, dynamic positioning and heading autopilot. Then, we discussed the models commonly used for vessel response and showed how these models are used for Kalman filter design. We also briefly discussed parameter and noise covariance estimation, which are used for filter tuning. To illustrate the performance, a case study based on numerical simulations for a ship autopilot was considered. The material discussed in this article conforms to modern commercially available ship motion-control systems. Most of the vessels operating in the offshore industry worldwide use Kalman filters for velocity estimation and wave filtering. Thus, the article provides an up-to-date tutorial and overview of Kalman-filter-based wave filtering.

Ship-to-ship operations, such as underway replenishment (UNREP), lightering, etc., entail potentially hazardous situations due to the possibility of collisions between two ships operating in close proximity. In order to ensure safe joint operations with collision avoidance, knowledge of the hydrodynamic interaction loads between the two vessels is highly advantageous. This paper thus considers the hydrodynamic interaction effects between two advancing ships and their maneuvering behaviors in calm water during typical replenishment operations. For this purpose, a unified seakeeping and maneuvering model of two interacting ships based on a two-time scales and modular concept relevant for calm water is employed. The maneuvering module is based on generalized slender-body theory, while calm-water interaction forces and moments between the two ships are estimated using Newman-Tuck theory. Automatic steering and speed control algorithms for both ships are also employed to achieve high-precision and collision-free UNREP maneuvers, which is illustrated through a numerical simulation involving the well-known “ESSO OSAKA’ and “MARINER’ models from the ship literature.

This article describes a Matlab toolbox for parametric identification of fluid-memory models associated with the radiation forces ships and offshore structures. Radiation forces are a key component of force-to- motion models used in simulators, motion control designs, and also for initial performance evaluation of wave-energy converters. The software described provides tools for preparing non-parmatric data and for identification with automatic model-order detection. The identification problem is considered in the frequency domain.

In this paper a robotic approach is taken to develop a dynamic nonlinear model of an offshore pipeline installation. A compact dynamic representation of the pipe and its kinematics, for effective implementation in a computer code is developed. The pipe model is used in simulations to compute the dynamic tension from the pipe on the vessel. The pipe tension is commonly computed by quasi-static methods based on the catenary equation, which lack the dynamic behavior and flexural effects of the pipe. The model can also replace computationally slower finite element models in applications with real-time requirements. The model is suitable for many applications, such as real-time decision support, training simulators, motion prediction systems, Hardware-In-the-Loop (HIL) testing, operability analysis, control applications and pre-design of ships.

In this paper, the implementation of genetic programming (GP) to design a controller structure is assessed. GP is used to evolve control strategies that, given the current and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the commanded forces required to maneuver the ship. The controllers created using GP are evaluated through computer simulations and real maneuverability tests in a laboratory water basin facility. The robustness of each controller is analyzed through the simulation of environmental disturbances. In addition, GP runs in the presence of disturbances are carried out so that the different controllers obtained can be compared. The particular vessel used in this paper is a scale model of a supply ship called CyberShip II. The results obtained illustrate the benefits of using GP for the automatic design of propulsion and navigation controllers for surface ships.

A propeller with a rudder can produce a thrust vector within a range of directions and magnitudes in the horizontal plane for low-speed maneuvering and dynamic positioning. The set of attainable thrust vectors is non-convex because significant lift can be produced by the rudder only with forward thrust. It is suggested to decompose the attainable thrust region into a finite union of convex polyhedral sets and a mixed-integer-like convex quadratic programming formulation of the optimal control allocation problem for marine vessels with any number of rudders as well as thrusters and other propulsion devices is derived. Actuator rate and position constraints are explicitly taken into account. Using multi-parametric quadratic programming software, an explicit piecewise linear representation of the least-squares optimal control allocation law is pre-computed. It can therefore be implemented with low computational complexity and high software reliability without the use of real-time optimization. The method is illustrated using a scale model ship in a basin.

A nonlinear observer for estimation of the longitudinal velocity, lateral velocity, and yaw rate of a vehicle, designed for the purpose of vehicle side-slip estimation, is modified and extended in order to work for different road surface conditions. The observer relies on a road-tire friction model and is therefore sensitive to changes in the adhesion characteristics of the road surface. The friction model is parametrized with a single friction parameter, and an update law is designed. The adaptive observer is proven to be uniformly globally asymptotically stable and uniformly locally exponentially stable under a persistency-of-excitation condition and a set of technical assumptions, using results related to Matrosov's theorem. The observer is tested on recorded data from two test vehicles and shows good results on a range of road surfaces.

This paper addresses the problem of joint identification of infinite-frequency added mass and fluid memory models of marine structures from finite frequency data. This problem is relevant for cases where the code used to compute the hydrodynamic coefficients of the marine structure does not give the infinite-frequency added mass. This case is typical of codes based on 2D-potential theory since most 3D-potential-theory codes solve the boundary value associated with the infinite frequency. The method proposed in this paper presents a simpler alternative approach to other methods previously presented in the literature. The advantage of the proposed method is that the same identification procedure can be used to identify the fluid-memory models with or without having access to the infinite-frequency added mass coefficient. Therefore, it provides an extension that puts the two identification problems into the same framework. The method also exploits the constraints related to relative degree and low-frequency asymptotic values of the hydrodynamic coefficients derived from the physics of the problem, which are used as prior information to refine the obtained models.

This paper considers the subject of straight-line target tracking for unmanned surface vehicles (USVs). Target-tracking represents motion control scenarios where no information about the target behavior is known in advance, i.e., the path that the target traverses is not defined apriori. Specifically, this work presents the design of a motion control system which enables an underactuated USV to track a target that moves in a straight line at high speed. The motion control system employs a guidance principle originally developed for interceptor missiles, as well as a novel velocity controller inspired by maneuverability and agility concepts found in fighter aircraft literature. The performance of the suggested design is illustrated through full-scale USV experiments in the Trondheimsfjord.