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We present an approach to endow an autonomous underwater vehicle with the capabilities to move through unexplored environments. To do so, we propose a computational framework for planning feasible and safe paths. The framework allows the vehicle to incrementally build a map of the surroundings, while simultaneously (re)planning a feasible path to a specified goal. To accomplish this, the framework considers motion constraints to plan feasible 3D paths, that is, those that meet the vehicle’s motion capabilities. It also incorporates a risk function to avoid navigating close to nearby obstacles. Furthermore, the framework makes use of two strategies to ensure meeting online computation limitations. The first one is to reuse the last best known solution to eliminate time-consuming pruning routines. The second one is to opportunistically check the states’ risk of collision. To evaluate the proposed approach, we use the Sparus II performing autonomous missions in different real-world scenarios. These experiments consist of simulated and in-water trials for different tasks. The conducted tasks include the exploration of challenging scenarios such as artificial marine structures, natural marine structures, and confined natural environments. All these applications allow us to extensively prove the efficacy of the presented approach, not only for constant-depth missions (2D), but, more important, for situations in which the vehicle must vary its depth (3D) J.D. Hern´andez, E. Vidal, N. Palomeras, and M. Carreras have been supported by the EXCELLABUST and ARCHROV Projects under the Grant agreements H2020-283TWINN-2015, CSA, ID: 691980 and DPI2014- 57746-C3-3-R respectively

Natural CO2 releases from shallow marine hydrothermal vents are assumed to mix into the water column and not accumulate into stratified seafloor pools. We present newly discovered shallow subsea pools located within the Santorini volcanic caldera of the Southern Aegean Sea, Greece, that accumulate CO2 emissions from geologic reservoirs. This type of hydrothermal seafloor pool, containing highly concentrated CO2, provides direct evidence of shallow benthic CO2 accumulations originating from sub-seafloor releases. Samples taken from within these acidic pools are devoid of calcifying organisms and channel structures among the pools indicate gravity driven flow, suggesting that seafloor release of CO2 at this site may preferentially impact benthic ecosystems. These naturally occurring seafloor pools may provide a diagnostic indicator of incipient volcanic activity and can serve as an analog for studying CO2 leakage and benthic accumulations from subsea carbon capture and storage sites.

We present a novel method for planning coverage paths for inspecting complex structures on the ocean floor using an autonomous underwater vehicle (AUV). Our method initially uses a 2.5-dimensional (2.5D) prior bathymetric map to plan a nominal coverage path that allows the AUV to pass its sensors over all points on the target area. The nominal path uses a standard mowing-the-lawn pattern in effectively planar regions, while in regions with substantial 3D relief it follows horizontal contours of the terrain at a given offset distance. We then go beyond previous approaches in the literature by considering the vehicle's state uncertainty rather than relying on the unrealistic assumption of an idealized path execution. Toward that end, we present a replanning algorithm based on a stochastic trajectory optimization that reshapes the nominal path to cope with the actual target structure perceived in situ. The replanning algorithm runs onboard the AUV in real time during the inspection mission, adapting the path according to the measurements provided by the vehicle's range-sensing sonars. Furthermore, we propose a pipeline of state-of-the-art surface reconstruction techniques we apply to the data acquired by the AUV to obtain 3D models of the inspected structures that show the benefits of our planning method for 3D mapping. We demonstrate the efficacy of our method in experiments at sea using the GIRONA 500 AUV, where we cover part of a breakwater structure in a harbor and an underwater boulder rising from 40 m up to 27 m depth This research has been sponsored by the Government of Spain (COMAROB Project, DPI2011-27977-C03-02), the MORPH EU FP7-Project (grant agreement FP7-ICT-2011-7-288704), and the Eurofleets2 EU FP7-Project (grant agreement FP7-INF-2012-312762). The authors are grateful to Lluis Magi, Carles Candela, and Arnau Carrera for helping with the GIRONA 500 operations

Starting in January 2009, the RAUVI (Reconfigurable Autonomous Underwater Vehicle for Intervention Missions) project is a 3-year coordinated research action funded by the Spanish Ministry of Research and Innovation. In this paper, the state of progress after 2 years of continuous research is reported. As a first experimental validation of the complete system, a search and recovery problem is addressed, consisting of finding and recovering a flight data recorder placed at an unknown position at the bottom of a water tank. An overview of the techniques used to successfully solve the problem in an autonomous way is provided. The obtained results are very promising and are the first step toward the final test in shallow water at the end of 2011 This research was partly supported by Spanish Ministry of Research and Innovation DPI2008-06548-C01-02-03 (RAUVI Project), by the European Commissions Seventh Framework Programme FP7/2007-2013 underGrant agreement 248497 (TRIDENT Project), by Fundació Caixa Castelló-Bancaixa P1-1B2009-50, and by the Spanish Ministry of Education and Science (MCINN Grant CTM2010-15216), Ramon Y Cajal Program (N. Gracias) and the FPI program (R. Campos)

The presented work focuses on the theoretical and practical aspects concerning the design and development of a formal method to build a mission control system for autonomous underwater vehicles bringing systematic design principles for the formal description of missions using Petri nets. The proposed methodology compounds Petri net building blocks within it to de_ne a mission plan for which it is proved that formal properties, such as reachability and reusability, hold as long as these same properties are also guaranteed by each Petri net building block. To simplify the de_nition of these Petri net blocks as well as their composition, a high level language called Mission Control Language has been developed. Moreover, a methodology to ensure coordination constraints for teams of multiple robots as well as the de_nition of an interface between the proposed system and an on-board planner able to plan/replan sequences of prede_ned mission plans is included as well. Results of experiments with several real underwater vehicles and simulations involving an autonomous surface craft and an autonomous underwater vehicles are presented to show the system's capabilities., El treball presentat en aquesta tesi està centrat en el disseny i desenvolupament d'un mètode formal per a construir un sistema de control de missió per a vehicles submarins autònoms, que aporta principis sistemàtics de disseny per a la descripció formal de missions. La metodologia proposada parteix d'uns blocs elementals de construcció, descrits mitjançant xarxes de Petri. La composició d'aquests blocs entre si genera un pla de missió per el qual diverses propietats, com ara accessibilitat o reutilització, són garantides sempre i quan aquestes mateixes propietats siguin també garantides per a cada un dels blocs elementals de construcció. Per simplificar la definició d'aquests blocs, així com per simplificar-ne la seva composició, s'ha desenvolupat un llenguatge d'alt nivell anomenat Mission Control Language. A més, s'ha inclòs una metodologia per assegurar la coordinació de restriccions entre equips de múltiples robots. També s'ha establert una interfície entre el sistema proposat i un planificador a bord del vehicle capaç de planificar/replanificar seqü_encies de plans de missió prèviament definits. Per tal de demostrar les capacitats del sistema, s'han presentat resultats d'experiments amb diversos vehicles submarins reals, així com simulacions amb vehicles autònoms submarins i en superfície.

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Autonomous underwater vehicles are used nowadays in many applications. One of the applications is mapping of a particular area of the ocean. Quen the robot needs to cover the area of interest and no prior knowledge of the area to be covered is avaialbe, a robotic exploration algorithm is used. This thesis develops a robotic exploration algorithm for autonomous underwater vehicles. The proposed algorithm allows the full exploration of a 2D or 3D environment, using acoustic sensors such as a scanning profiling sonar or a multibeam, and optical cameras. The validity of the proposed algorithm is demonstrated through numerous experiments carried out in different locations of the catalan coast, using different vehicles and sensor suites Els robots autònoms submarins s'utilitzen actualment en moltes aplicacions. Una d'elles és el mapeig d'una zona concreta de l'oceà. Quan el robot ha de cobrir la zona d'interès sense utilitzar informaciò prèvia sobre l'entorn a mapejar, s'utilitzen algorismes d'exploració robòtica. Aquesta tesi desenvolupa un algorisme d'exploració robòtica per a robots autònoms submarins. L'algorisme proposat permet explorar un entorn 2D o 3D en la seva totalitat, utilitzant sensors acústics, com per exemple un sonar perfilador o multifeix, i càmeres òptiques. La validesa de l'algorisme es demostra en experiments portats a terme a diferents entorns de la costa catalana, utilitzant múltiples vehicles i múltiples sensors

The interest in performing underwater tasks using Autonomous Underwater Vehicles (AUVs) has been growing over the past few years. In this thesis, a flexible framework for underwater interventions using a Learning by Demonstration algorithm as a core has been developed. This algorithm allows to the robot's user to transfer a skill or knowledge to the I-AUV using a natural and intuitive form. The developed framework for interventions has been tailored to the GIRONA 500 AUV in order to enable it to perform an underwater valve turning task under different conditions. The GIRONA 500 has been equipped with a 4 DOF Manipulator and a custom end-effector. Throughout this thesis, the experiments developed have been carried out in a mock-up scenario of a sub-sea installation with a valve panel. The difficulty of the task has been increased gradually in order to test the new improvements and the robustness in the proposed framework, Durant les últimes dècades ha augmentat l’interès en la utilització de Vehicles Autònoms Submarins (AUVs) per realitzar tasques submarines. En aquesta tesis s’ha desenvolupat un marc de treball (framework) per a realitzar intervencions submarines amb AUVs basat en un algorisme d’Aprenentatge per Demostració (LbD). Aquest algorisme permet a l’usuari del robot transferir el seu coneixement al vehicle d’intervenció d’una forma natural. El framework desenvolupat s’ha ajustat a les característiques del GIRONA 500 AUV, amb l’objectiu de que pugui girar vàlvules submarines en diverses condicions. El GIRONA 500 s’ha equipat amb un braç robòtic i un element terminal personalitzat. Al llarg de tota la tesis s’ha utilitzat com entorn de desenvolupament un tanc d’aigua amb una recreació d’un escenari d’intervenció subaquàtic on s’han de girar determinades vàlvules d’un panell. El grau de dificultat de la tasca s’ha incrementat de forma gradual, per tal de poder provar les noves millores

El grup de Visió per Computador i Robòtica (VICOROB) disposa de varis robots submarins per a la recerca i inspecció subaquàtica. Recentment s’ha adquirit un sensor sonar d’escombrat lateral el qual s’utilitza per realitzar imatges acústiques del fons marí quan aquest es mou principalment a velocitat constant i mantenint el rumb. Els robots del grup VICOROB estan equipats amb diferents tipus de sensors i càmeres per analitzar el fons marí. Aquest sensors són de gran qualitat i permeten conèixer de manera bastant satisfactòria l’entorn a les proximitats del robot. Freqüentment però, aquest sensors estant sotmesos a diferents restriccions depenent de la seva naturalesa de funcionament, de tal manera que es necessària la seva combinació per resoldre determinats problemes en diferents situacions. Amb aquest projecte, es pretén integrar un nou sistema de captura d’imatges sonores del fons marí, en un dels robots. Amb la integració d’aquest nou sensor, s’espera obtenir una opció alternativa els sistemes actuals que pugui aportar informació addicional sobre el fons. Aquest sistema podrà ser utilitzat per realitzar tasques per les quals els altres sensors no estant preparats o bé per complementar informació d’altres sensor