Underwater image mosaicking is an important tool for visual surveys, object detection, and as a means to control the underwater robot if done online. Such application areas can benefit significantly from a recent focus on robust methods for graph-based Simultaneous Localization and Mapping (SLAM). This paper focuses on two contributions: An approach to combine registration results from multiple methods in multimodal constraints and, up to the authors' knowledge, the first method to generate hyperedge constraints from state-of-the-art place recognition techniques. Both contributions are implemented within the Generalized Graph SLAM framework. Experimental results show that the methods generate informative constraints and that the authors' Prefilter method outperforms related methods on a large underwater image dataset processed with these methods.

This article discusses the possibility of building online a mosaic of the seafloor relying on a SLAM framework. The goal is to provide the ROV operator with an approximated seafloor's visual map relatively rough. In order to have that map, it is important to get an accurate estimate of the location of the visual landmarks and, in particular, a correct data association when a visual landmark is re-visited by the vehicle. The proposed approach uses the combination of a set of local mosaics constructed in the proximity of the SLAM visual landmarks instead of using a global mosaic. The algorithm was tested using a batch of experimental data in typical operating conditions and the results show the effectiveness of the approach.

A line following guidance solution for underactuated marine systems is presented. The approach differs from other ones known in the literature in the definition of the error variables to be stabilized to zero. The proposed guidance technique has been applied to the Charlie USV (Unmanned Surface Vehicle), developed by CNR-ISSIA Autonomous robotic systems and control group, and experimental results are presented.

Determining a model of marine vehicles for the control purposes can be a very time-consuming process. Having this in mind, in [3] a much faster identification method has been proposed based on the use of self-oscillation identification method. Here we demonstrate the use of the proposed method for heading controller tuning of the autonomous unmanned surface vehicle (USV) Charlie.

The design, development, sea trials and exploitation of the SESAMO (SEa Surface Autonomous MOdular unit) platform, an autonomous surface vessel for the study and characterization of the air-sea interface, are presented. The SESAMO prototype robot, developed by the robotics group of CNR-ISSIA, Genova branch, in strict cooperation with the scientific end-users in the framework of a project of the Italian National Program of Research in Antarctica (PNRA), was able to sample the sea surface microlayer and immediate sub-surface. After satisfactory basic at field trials of the navigation, guidance and control (NGC) and sampling systems, the robotised catamaran was exploited by the scientific end-users for water sample collection in the area of Terra Nova Bay, Ross Sea, Antarctica, in the framework of the PNRA project Chemical contamination.

This paper deals with a method to design the control architecture for mobile robots. A Petri net-based approach is extended in this work to deal with uncontrollable events. In particular, the case where an uncontrollable transition is present in the model of the task is tackled. This situation may arise, for example, when taking into account the initialization phase of the estimators.

The problem of designing an interface between the continuous-state and the discrete-state domains of intelligent control architectures is addressed, focusing on the case of a hierarchical navigation, guidance and control (NGC) architecture for unmanned underwater vehicles. The proposed interface represents the underlying continuous-state execution level as a discrete event system using a Petri net formalism. The correct behavior of the execution level is ensured, checking that no forbidden state is reached and that the proper task activation and deactivation order is respected. Methodologies for the off-line generation of the Petri net representation of the execution level and automatic system reconfiguration are presented.

This paper addresses the problem of guidance of unmanned underwater vehicles (UUVs). In the framework of a two layered hierarchical architecture decoupling the system dynamics and kinematics, two guidance laws for approaching a target with the desired orientation and following an environmental feature have been designed with Lyapunov-based techniques. Suitable acoustic-based estimators of the corresponding operational variables have been designed and integrated with the guidance and control system. Experimental results of pool trials of a prototype UUV executing free-space maneuvering and wall-following tasks are reported and discussed.

A two-layered hierarchical guidance and control architecture for UUVs has been integrated with a set of model-based motion estimators in order to enable the execution of high precision motion tasks in proximity of the seabed for scientific benthic applications. Preliminary tests, carried out in a high-diving pool, proved the system functionality, showing the high performances in terms of precision guaranteed by the use of PI-type guidance algorithms and combined estimation of the vehicle depth and altitude in hovering conditions.

AMADEUS is a dexterous subsea robot hand incorporating force and slip contact sensing, using fluid-filled tentacles for fingers. Hydraulic pressure variations in each of three flexible tubes (bellows) in each finger create a bending moment, and consequent motion or increase in contact force during grasping. Such fingers have inherent passive compliance, no moving parts, and are naturally depth pressure-compensated, making them ideal for reliable use in the deep ocean. In addition to the mechanical design, development of the hand has also been considered for closed loop finger position and force control, coordinated finger motion for grasping, force and slip sensor development/signal processing, and reactive world modelling/planning for supervisory "blind grasping". Initially, the application focus is for marine science tasks, but broader roles in offshore oil and gas, salvage, and military use are foreseen. Phase I of the project has been completed, with the construction of a first prototype. This paper summarizes the developments in Phase I. Further details for each area of investigation are referenced.