This paper outlines the specifications and basic design approach taken on the development of the Girona 500, an autonomous underwater vehicle whose most remarkable characteristic is its capacity to reconfigure for different tasks. The capabilities of this new vehicle range from different forms of seafloor survey to inspection and intervention tasks.

To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study was to investigate the use of reinforcement learning as a tool for range-only underwater target-tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.

This paper presents a control architecture for an autonomous underwater vehicle (AUV) named the Component Oriented Layer-based Architecture for Autonomy (COLA2). The proposal implements a component-oriented layer-based control architecture structured in three layers: the reactive layer, the execution layer, and the mission layer. Concerning the reactive layer, to improve the vehicle primitives' adaptability to unknown changing environments, reinforcement learning (RL) techniques have been programmed. Starting from a learned-in-simulation policy, the RL-based primitive cableTracking has been trained to follow an underwater cable in a real experiment inside a water tank using the Ictineu AUV. The execution layer implements a discrete event system (DES) based on Petri nets (PNs). PNs have been used to safely model the primitives' execution flow by means of Petri net building block (PNBBs) that have been designed according to some reachability properties showing that it is possible to compose them preserving these qualities. The mission layer describes the mission phases using a high-level mission control language (MCL), which is automatically compiled into a PN. The MCL presents agreeable properties of simplicity and structured programming. MCL can be used to describe offline imperative missions or to describe planning operators, in charge of solving a particular phase of a mission. If planning operators are defined, an onboard planner will be able to sequence them to achieve the proposed goals. The whole architecture has been validated in a cable tracking mission divided in two main phases. First, the cableTracking primitive of the reactive layer has been trained to follow a cable in a water tank with the Ictineu AUV, one of the research platforms available in the Computer Vision and Robotics Group (VICOROB), University of Girona, Girona, Spain. Second, the whole architecture has been proved in a realistic simulation of a whole cable tracking mission.

We present an approach for navigating in unknown environments while, simultaneously, gathering information for inspecting underwater structures using an autonomous underwater vehicle (AUV). To accomplish this, we first use our pipeline for mapping and planning collision-free paths online, which endows an AUV with the capability to autonomously acquire optical data in close proximity. With that information, we then propose a reconstruction pipeline to create a photo-realistic textured 3D model of the inspected area. These 3D models are also of particular interest to other fields of study in marine sciences, since they can serve as base maps for environmental monitoring, thus allowing change detection of biological communities and their environment over time. Finally, we evaluate our approach using the Sparus II, a torpedo-shaped AUV, conducting inspection missions in a challenging, real-world and natural scenario.