• autonomous underwater vehicles
• autonomous marine vehicles
• acoustic localisation
• marine robotics
• robot cooperation

Oceans exploration and conservation are topics that have gained great attention in recent years, driven by the commercial interests of industries and the growing desire of nations and environmental agencies to protect the health of the seas. In this context, the scientific community has a responsibility to identify new sustainable solutions that can lead to economic growth in the sector while preserving the marine environment. Achievements over the past two decades in the fields of marine technologies and mobile robotics have enabled the development of Autonomous Marine Vehicles (AMVs) and their use in a variety of application fields. Both Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs) fall into this category, both of which are characterised by the key element of autonomy. Although the term traditionally denotes the ability to make decisions without being controlled by others, for a mobile robot it can best be described as a set of strategies and solutions it has to implement in order to operate without human intervention in an unknown and changing environment, optimising its behaviour to adapt to the situation while trying to achieve a predefined goal. These marine robots are in fact powerful tools through which data, essential for gaining a better understanding of oceanographic processes, can be collected efficiently and at a lower cost than traditionally employed methods such as large oceanographic ships or Remotely Operated Vehicles (ROVs). In addition, AMVs have the potential to be able to perform inspection, repair and maintenance (IRM) operations, on offshore plants and submerged structures, safely and without any intervention by a human operator.
An ambitious goal of researchers from academia and industry is to make these vehicles capable of persistent operation in the field, so that they can constantly monitor sea conditions or carry out routine IRM tasks. To this end, it is necessary for marine vehicles to achieve the so-called long-term autonomy, through progress in the respective areas of navigation, perception, planning, communication and endurance.

This thesis aims to lay the foundation for the realisation of a Marine System of Systems (MSoS) in which heterogeneous, low-cost AMVs cooperate to complete missions in an efficient and intelligent manner. In this regard, the thesis proposes algorithms and methods to increase the autonomy of marine robots by approaching the problem from two perspectives: (i) by improving the navigation, perception and planning capabilities of individual underwater vehicles; (ii) through a cooperative system designed to enable accurate and low-cost underwater navigation of a team of AUVs through the support of an ASV. These two approaches also define the structure of the elaborate, which is in fact divided into two parts.

Regarding the first approach, a low-cost and data-based procedure for identifying the parameters of a simplified dynamic model for an AUV is initially presented. The process was tested and validated in the field and allowed the definition of a state transition model to be used within a Kalman-based filter to improve the navigation performance of an AUV. Next, in order to increase the perception capabilities of an underwater robot, solutions are presented for the online and offline processing of data sensed by a Side-Scan Sonar (SSS). The first method described makes it possible to obtain an accurate acoustic map of the inspected area by means of vehicle motion compensation, georeferencing of the data and their mosaicing. The second proposed solution is an algorithm for Automatic Target Reconition (ATR) based on saliency filters applied to SSS images for the Mine CounterMeasures (MCM) scenario. Finally, the last contribution provided in this first part concerns the adaptive planning capabilities with which a marine vehicle must be endowed in order to carry out operations efficiently and adapt to the surrounding environment. In particular, a planning algorithm inspired by evolutionary processes is proposed and analysed, the aim of which is to generate an optimal route in terms of information gathered and coverage of the area to be inspected. The method exploits a priori knowledge of the environment and a Gaussian Process (GP) to model the spatial distribution of the variable of interest, which is then used by the method to plan the optimal trajectory.

Concerning the second approach, the cooperative system based on two heterogeneous vehicles, an ASV and an AUV, is initially presented in detail. The system is designed so that the surface vehicle can provide support to the AUV with regard to navigation and communication. In fact, the ASV is able to locate the AUV through the use of an Ultra-Short BaseLine (USBL) device and the definition of a communication and positioning protocol specifically designed for the scenario in question. The distinctive features of the developed protocol are that it prioritises the positioning frequency, does not require any a priori synchronisation between vehicles and allows the exchange of information between robots and a command and control station for monitoring purposes, without affecting the localisation procedure. The cooperative system was conceived as a low-cost alternative solution for accurate underwater navigation, and was therefore designed to take advantage of minimal and typically standard equipment for AMVs: GPS sensor, Attitude and Heading Reference System (AHRS) and acoustic modem. It should be noted that although the AUV installs a simple acoustic modem, the ASV is equipped with a USBL device to perform localisation, which also has communication capabilities.
Strategies were implemented to allow tracking and following of the AUV by the surface robot, so that it could move to keep the distance between vehicles limited. An excessive increase in distance is in fact problematic for the performance of the positioning system, due to the unreliability of acoustic communication and the inherent delay of transmissions through this channel. The cooperative platform was extensively tested during experiments at sea, which allowed a characterisation of its performance in terms of navigation accuracy, tracking and reliability of the positioning procedure. A particular testing campaign allowed the evaluation of its performance also for a scenario involving the presence of natural gas seeps. Indeed, it is believed that such gas leaks may have an impact on the readings of the acoustic sensors typically employed by an AUV to navigate. An experimental analysis of the impact of these gas seeps on DVL- and USBL-based navigation strategies was therefore carried out, and the main issues an AUV may encounter when performing operations in similar areas were identified.
A further simulative study was conducted to define a motion planning algorithm designed for the ASV. The purpose of the planner is to guide the surface vehicle to positions from which it can take measurements with the USBL sensor that are as informative as possible for the submerged vehicle. Therefore, a cost functional was defined, composed of the determinant of the estimated covariance matrix related to the AUV position error and some penalising factors regarding the minimum and maximum distance between vehicles. By minimising this functional, the method aims to reduce the uncertainty in the position estimate made by the AUV, thereby improving its navigation accuracy.
Finally, the communication and positioning protocol was extended to the multi-AUVs scenario, defining a protocol inspired by the Time Division Multiple Access (TDMA) method but retaining the distinctive features of the two-vehicle approach: priority to positioning and no synchronisation between vehicles. The strategy was then tested and validated in the field through sea trials with one surface vehicle and two nodes to be located.

All the solutions proposed in this thesis represent an important first step towards the realisation of the MSoS, and progress towards the achievement of the long-term autonomy. The results also confirm how cooperation can be a game changer in defining low-cost strategies that pave the way for the use of AMVs in various application fields.