The aim of this paper is to present a comparative study on two different methods for the evaluation of the equilibrium point of a ship, core issue for designing an On Board Stability System (OBSS) module that, starting from geometry information of a ship hull, described by a discrete model in a standard format, and the distribution of all weights onboard calculates the ship floating conditions (in draught, heel and trim).

A path following controller for the dynamic model of an underactuated marine vessel is presented. The solution extends previous results relative to the purely kinematics case and is able to cope with constant and known ocean currents. Based on kinematics, a reference linear and angular velocity is computed such that if the vehicle had such velocities the path following problem would be asymptotically solved. Then, applying feedback linearization to the dynamic model of the surface vessel, yaw torque and surge force commands are computed in order to drive the vessels total velocity on the desired reference values. The resulting control laws for the yaw and surge degrees of freedom result in nonlinear terms (from feedback linearization) and PI terms. Numerical simulations are provided to validate the proposed approach.

A guidance control law is designed for an underactuated 3D kinematics model of an underwater vehicle subject to constant, but unknown currents. The model captures the main features of most available Autonomous Underwater Vehicles (AUVs). The paper focuses on the description of the design criteria that allows to explicitly address the difficulties related to the underactuated structure of the model. A convergence and stability proof is sketched and numerical simulations are provided to illustrate the performances of the proposed solution.

Unmanned aerial vehicles (UAVs) are an active research field since several years. They can be applied in a large variety of different scenarios, and supply a test bed to investigate several unsolved problems such as path planning, control and navigation. Furthermore, with the availability of low cost, robust and small video cameras, UAV video has been one of the fastest growing data sources in the last couple of years. In other words, object detection and tracking as well as visual navigation has recently received a lot of attention. This paper proposes an advanced technology framework that, through the use of UAVs, allows to supervise a specific sensible area (i.e. traffic monitoring, dangerous zone and so on). In particular, one of the most cited real-rime visual tracker proposed in the literature, Struck, is applied on video sequences tipically supplied by UAVs equipped with amonocular camera. Furthermore in this paper is investigated on the feasibility to graft different features characterization into the original tracking architecture (replacing the orginal ones). The used feature extraction methods are based on Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). Objects to be tracked could be selected manually or by means of advanced detection technique based, for example, on change detection or template matching strategies. The experimental results on well known benchmark sequences show as these features replacing improve the overall performances of the original considered real-time visual tracker.

This article proposes a novel solution to the Pose Estimation problem for Ego-Motion from stereo camera images. The approach uses a nonlinear function, derived from the concept of Gibbs' Entropy, which is robust by nature to the presence of noise and outliers in the visual features. The SIFT algorithm is used to collect and match the features from stereo images. The 3-vectors quaternion parameterization is used to parameterize the rotation matrix, in order to avoid the unit norm constraint in the minimization computation. Simulations and experimental results are presented and compared with the results obtained via the classical Iterative Closest Point approach.

The process of conversion in Linz-Donawitz converters is a crucial stage in the production of steel: oxygen is blown on the surface of the melted bath in order to reduce the carbon concentration. At the same time, suitable amounts of coolants are added in order to govern the increase of the bath temperature and reduce the impurities (favoring the slag formation). The aim is to direct the bath of melted steel to the desired final condition, in terms of temperature and carbon content. At around 92-93% of the complete process of conversion, the oxygen blowing is suspended and the In-Blow is performed, i.e. a steel sample is collected by means of a lance introduced in the melted bath and its carbon percentage and temperature measured. A dynamic model, through two characteristic equations, describes the evolution of the carbon percentage and temperature of the melted steel during the final phase of the conversion process, i.e. from the In-Blow until the end. Based on this model, the volume of oxygen to be blown during this phase and the amount of coolant to be added in order to reach the required final (End-Point) conditions of carbon percentage and temperature can be calculated. The model is nonlinear and depends on four parameters to be estimated. Based on a dimensional analysis and on a large set of experimental data, the nominal model has been modified introducing the hypothesis that the parameters are not constant, but depend on the temperature. Within this framework the novel model is identified exploiting Least Squares (LS) methods and its output is compared with the existing practice.

Refer to paper.

Complementary filtering is a frequency based method used to design data processing algorithms exploiting signals with complementary spectra. The technique is mostly used in sensor fusion architectures, but it may also be effective in the design of state estimators. In spite of its potential in several areas of robotics, the complementary filtering paradigm is poorly used as compared to alternative time domain methods. The first part of the paper aims at reviewing the basics of complementary filtering in sensor data processing and linear systems state estimation. The second part of the paper describes how to exploit the main ideas of complementary filtering to design a depth controller for an actuator redundant autonomous underwater vehicle (AUV). Unlike with alternative state space methods commonly used to address the design of control solutions for actuator redundant systems, the proposed approach allows to fully exploit the knowledge of frequency characteristics of actuators. Simulation results are reported to demonstrate the effectiveness of the proposed solution.

The paper describes the "Cooperative Cognitive Control for Autonomous Underwater Vehicles (Co3AUVs)" EU-project. This is a 7th Framework Program STREP project under the theme: Information and Communication Technologies Cognitive Systems, Interaction, Robotics. The project duration is February 2009 to January 2012. The aim of Co3AUVs is to develop, implement and test advanced cognitive systems for coordination and cooperative control of multiple AUVs. Several aspects are investigated including 3D perception and mapping, cooperative situation awareness, deliberation and navigation as well as behavioral control strictly linked with the underwater communication challenges. This paper presents an overview of the project with a focus on results from the final 3rd project year.

This paper addresses the issue of planning smooth (C^infty) 2D paths with bounded curvature and curvature derivative connecting two assigned poses (positions and orientations). The solution builds on an approximation of Dubins shortest paths and it can be used also to link an ordered sequence of via-points. The effectiveness of the proposed planning method is validated by simulations.

This investigation describes the dynamic modeling of a PEM (Polymer Electrolyte Membrane) fuel cell applied to a commercial 1kW dead end anode configuration. The system is tested and validated through some initial experiments. The model allows the characterization of the polarization curve, the evaluation of cell performance in terms of efficiency and consumption and the estimation of water production. To this purpose, an experimental set-up has been created using an electronic DC load (connected to a computer by RS232 serial communication) and an NI DAQ CompactRio evaluation board. The target is studying and testing solutions to improve performance, in particular with reference to hydrogen recovery solution from the purge valve. The fuel cell model has been interfaced with a 3D race simulator that is able to reproduce the environment of the competition and the specification of the vehicle. This allows the analysis of the driver’s single lap results in terms of performance and fuel consumption according to the goals of the competition. In the present investigation the rules of the Shell Eco Marathon 2012 competition have been taken into account. Thanks to the developed tool, the driver is able to choose the best race strategy both interactively or with the help of a external optimizer.

The concept and the mathematical properties of entropy play an im- portant role in statistics, cybernetics and information sciences. Indeed many al- gorithms and statistical data processing tools, with a wide range of targets and scopes, have been designed based on entropy. The paper describes two estima- tors inspired by the concept of entropy that allow to robustly cope with multi- collinearity, in one case, and outliers, in the other. The Generalized Maximum Entropy (GME) estimator optimizes the Shannon’s entropy function subject to consistency and normality constraints. In regression applications GME allows, for example, to estimate model coefficients in the presence of multicollinearity. The Least Entropy-Like (LEL) estimator is a novel prediction error model co- efficient identification algorithm that minimizes a nonlinear cost function of the fitting residuals. As the cost function that is minimized shares the same mathe- matical properties of entropy, it allows to compute an estimate of the model co- efficients corresponding to a positively skewed distribution of the residuals. The resulting estimator exhibits higher robustness to outliers with respect to standard, as ordinary least squares (OLS) model coefficient approaches. Both the GME and LEL estimation methods are applied to a common case study to illustrate their respective properties.

The problem of identifying the position of a fixed target by a vehicle moving in 3D space may occur in several applications as underwater or aerospace monitoring. Assuming that only range measurements to the target are available and that the vehicle is equipped with a navigation system providing self-localization, the problem of localizing the target is analyzed. A recursive least squares fading memory filter solution is proposed. The dependence of the covariance of the estimated target position on the velocity profile of the vehicle is discussed.

The WiMUST proposal (Widely scalable Mobile Underwater Sonar Technology) has been favorably evaluated by the European Commission and the project is expected to kick off in the near future. It aims at expanding and improving the functionalities of current cooperative marine robotic systems, effectively enabling distributed acoustic array technologies for geophysical surveying. This paper describes the main features of the envisaged developments, with a focus on communication and networking issues.

Most cooperative motion tasks of multi vehicle systems require the agents to share relative localization information. Assuming the relative velocity of the vehicles to be known, under suitable observability conditions, relative localization among a pair of agents can be performed based on single range measurements. The problem addressed consists in designing a relative localization solution for a networked group of vehicles measuring mutual ranges: in particular, the objective is to exploit the presence of intra-vehicle communications to enhance the range-based relative position estimation. Geometrical constraints associated to the agents' (unknown) positions are explicitly accounted for in the estimation schema. The approach brings together a recent single range localization solution with a projection based Kalman filter estimation technique in the presence of state space constraints. Simulation examples are provided showing the effectiveness of the proposed solution.

This paper addresses the issue of plane detection in 3 dimensional (3D) range images. The identification of planar structures is a crucial task in many visual-aided autonomous robotic applications. The proposed method consists in implementing, in cascade, two algorithms: Random Sample and Consensus (RANSAC) and the more recent Least Entropy-like Estimator (LEL), a nonlinear prediction error estimator that minimizes a cost function inspired by the definition of Gibbs entropy. LEL estimators allow to improve RANSAC performances while maintaining its robustness; kernel density estimation is used to classify data into inliers and outliers. The method has been experimentally applied to 3D images acquired by a Time-Of- Flight camera and compared with a stand alone RANSAC solution. The proposed solution does not require an accurate estimation of the noise variance or outlier scale. This is of fundamental practical importance as the outlier scale, while severely influencing standard RANSAC, is usually unknown a priori and hard to estimate.

Underwater sensor networks represent a rapidly growing technology to monitor the ocean environment. This paper describes a solution to the problem of retrieving underwater dispersed sensors on behalf of an unmanned vehicle: assuming that the vehicle can only measure euclidean distances from itself to the dispersed sensors, an algorithm is designed allowing the vehicle to automatically find the sensors. The solution takes explicitly into account the presence of constant ocean currents and the availability of limited on-board navigation sensors.

This work addresses state estimation in presence of outliers in observed data. Outlying data and measure- ments have a most relevant impact in many control and signal processing applications including marine systems related ones: underwater navigation systems exploiting acoustic data, for example, are frequently affected by outlying measurements. Other on-board sensors and devices are likely to produce measurements contaminated by outlier because of the harsh operating conditions of marine systems. Given the general interest for dealing with measurement outliers in a number of applications, this paper describes a state esti- mation solution exhibiting robustness to output outliers. The system model is assumed to be linear (either time varying or time invariant) discrete time. The proposed observer is designed by extending an outlier robust static parameter identification algorithm to the case of a linear dynamic plant. The designed estimator has a predictor/corrector structure like the Kalman filter and the Luenberger observer. Simulation and exper- imental results are provided illustrating the robustness of the derived solution to measurement outliers as compared with the Kalman filter. The proposed solution is also compared with alternative outlier robust state estimation filters showing its effectiveness, in particular, in the presence of measurements outliers occurring in a consecutive sequence. Because of its deterministic execution time and limited numerical complexity, the proposed state estimator can be readily applied in real-time applications.

The paper presents a novel path-following solution for the dynamic model of an underactuated marine surface vessel. Assuming the lack of closed loop control over the linear velocities of the vessel, a velocity for a virtual target on the path and a reference angular velocity for the vessel are computed such that if the set target-vehicle had such velocities the path-following problem would be asymptotically solved at kinematic level. Then, applying feedback linearization to the dynamic model of the surface vessel, yaw torque command is computed in order to drive the vessels total velocity on the desired reference values. The resulting control law for the yaw degree of freedom results in nonlinear terms (from feedback linearization) and PI terms. Numerical simulations are provided to validate the proposed approach.

Recent studies relative to range-only localization in underwater robotics applications have focused primarily on (nonlinear) observability analysis. In particular, local weak observ- ability is guaranteed as long as the motion of the system to be localized is sufficiently rich, i.e. persistency of excitation conditions need to be satisfied. These conditions typically depend on the initial state of the system and on its inputs. Once that the challenging problem of identifying the necessary initial state and input constraints for guaranteeing local weak observability has been solved, state estimation can be performed resorting to linear (Kalman-like) filters. Yet the convergence of such state estimation approaches is local. Building on recent results in this area, this paper addresses the problem of designing input signals for a class of underactuated underwater vehicles allowing global range-only pose estimation.

The paper proposes a robust estimation method which implements, in cascade, two algorithms: (i) a Random Sample and Consensus (RANSAC) algorithm and (ii) a novel nonlinear prediction error estimator minimizing a cost function inspired by the mathematical definition of Gibbs entropy. The minimization of the nonlinear cost function allows to refine the Consensus Set found with standard RANSAC in order to reach optimal estimates of geometric transformation parameters under image stitching context. The method has been experimentally tested and compared with a standard RANSAC-MSAC algorithm where noticeable improvements are recorded in terms of computational complexity and quality of the stitching process, namely of the mean squared symmetric re-projection error.

A novel plane estimation algorithm from 3D range data is presented. The proposed solution is based on the minimization of a nonlinear prediction error cost function inspired by the mathematical definition of Gibbs' entropy. The method has been experimentally tested and compared with a standard implementation of the RANSAC algorithm. Results suggest that the proposed approach has the potential of performing better in terms of precision and reliability while requiring a lower computational effort.

"Cooperative Cognitive Control for Autonomous Underwater Vehicles" (Co3AUVs) is Collaborative Project (STREP) funded by the European Commission (EC) under the Seventh Framework Programme (FP7), "Information and Communication technologies" (ICT) Challenge 2: "Cognitive Systems, Interaction, Robotics". The aim of Co 3AUVs is to develop, implement and test advanced cognitive systems for coordination and cooperative control of multiple AUVs. Several aspects are investigated including 3D perception and mapping, cooperative situation awareness, deliberation and navigation as well as behavioral control strictly linked with the underwater communication challenges. This paper presents results from the first two years of the project, including the simulator, the development of vehicles and components, 2D as well as 3D underwater mapping, cooperative navigation and motion control, and cooperative skills.

The paper describes the CO3AUVs - Cooperative Cognitive Control for Autonomous Underwater Vehicles - European Commission Project. This is seventh Framework Programme STREP project under the theme: Information and Communication Technologies Cognitive Systems, Interaction, Robotics. The project started in December 2008. After an overview of the project objectives and consortium composition, the paper describes the project activities.

In this paper we present the application of the Null-Space-based Be- havioral (NSB) approach to the motion control of mobile robots with velocity saturated actuators. The NSB is a behavior-based robot con- trol approach that uses a hierarchical organization of the tasks to guarantee that they are executed according to a desired priority: it uses a projection technique to avoid that, in the absence of actuator saturations, low-priority tasks could influence higher-priority tasks. The main contribution of this paper is the extension of the NSB ap- proach to the case where actuator velocity saturation bounds are ex- plicitly taken into account. The proposed solution dynamically scales task velocity commands so that the hierarchy of task priorities is pre- served in spite of actuator velocity saturations. The approach has been validated on two specific case studies. In the first case, the NSB elaborates the motion directives for a single mobile robot that has to reach a target while avoiding a point obstacle; in this case, the mission is composed of two tasks. In the second case, the NSB elab- orates the motion directives for a team of six mobile robots that has to entrap and escort a target; in this case the mission is composed of four tasks. The approach is validated by numerical simulations and by experiments with real mobile robots.

The Italian national project MARIS (Marine Robotics for Interventions) pursues the strategic objective of studying, developing, and integrating technologies and methodologies to enable the development of autonomous underwater robotic systems employable for intervention activities. These activities are becoming progressively more typical for the underwater offshore industry, for search-and-rescue operations, and for underwater scientific missions. Within such an ambitious objective, the project consortium also intends to demonstrate the achievable operational capabilities at a proof-of-concept level by integrating the results with prototype experimental systems.

Communication performance of two different acoustic communication network infrastructures as designed in the projects UAN and CO^3AUV are reported. Qualitative explanation of communication performance variations can be accounted, at least in the UAN experiment, by standard Transmission Loss computation. The implications on multi- robots cooperation in AUV distributed autonomous tasks are also discussed by using the AUVNetSim simulator initialized with the parameters as measured in the field data.

This paper proposes an advanced technology framework that, through the use of UAVs, allows to monitor archaeological sites. In particular this paper focuses on the development of computer vision techniques such as super-resolution and mosaicking aiming at extracting detailed and panoramic views of the sites. In particular, super-resolution aims at providing imagery solutions (from aerial and remote sensing platforms) that create higher resolution views that make visible some details that are not perceivable in the acquired images. Mosaicking aims, instead, at creating a unique large still image from the sequence of video frames contained in a motion imagery clip. In this way large areas can be observed and a global analysis of their temporal changes can be performed. In general super-resolution and mosaicking can be exploited both for touristic or surveillance purposes. In particular they can be efficiently used to allow the enjoyment of the cultural heritage through a fascinating visual experience also eventually containing augmented information but also for surveillance tasks that can help to detect or prevent illegal activities.