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Improving the autonomy of underwater interventions by remotely operated vehicles (ROVs) can help mitigate the impact of communication delays on operational efficiency. Currently, underwater interventions for ROVs usually rely on real-time teleoperation or preprogramming by operators, which is not only time-consuming and increases the cognitive burden on operators but also requires extensive specialized programming. Instead, this paper uses the intuitive learning from demonstrations (LfD) approach that uses operator demonstrations as inputs and models the trajectory characteristics of the task through the dynamic movement primitive (DMP) approach for task reproduction as well as the generalization of knowledge to new environments. Unlike existing applications of DMP-based robot trajectory learning methods, we propose the underwater DMP (UDMP) method to address the problem that the complexity and stochasticity of underwater operational environments (e.g., current perturbations and floating operations) diminish the representativeness of the demonstrated trajectories. First, the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) are used for feature extraction of multiple demonstration trajectories to obtain typical trajectories as inputs to the DMP method. The UDMP method is more suitable for the LfD of underwater interventions than the method that directly learns the nonlinear terms of the DMP. In addition, we improve the commonly used homomorphic-based teleoperation mode to heteromorphic mode, which allows the operator to focus more on the end-operation task. Finally, the effectiveness of the developed method is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

The need for intervention in underwater environments has increased in recent years but there is still a long way to go before AUVs (Autonomous Underwater Vehicleswill be able to cope with really challenging missions. Nowadays, the solution adopted is mainly based on remote operated vehicle (ROV) technology. These ROVs are controlled from support vessels by using unnecessarily complex human–robot interfaces (HRI). Therefore, it is necessary to reduce the complexity of these systems to make them easier to use and to reduce the stress on the operator. In this paper, and as part of the TWIN roBOTs for the cooperative underwater intervention missions (TWINBOT) project, we present an HRI (Human-Robot Interface) module which includes virtual reality (VR) technology. In fact, this contribution is an improvement on a preliminary study in this field also carried out, by our laboratory. Hence, having made a concerted effort to improve usability, the HRI system designed for robot control tasks presented in this paper is substantially easier to use. In summary, reliability and feasibility of this HRI module have been demonstrated thanks to the usability tests, which include a very complete pilot study, and guarantee much more friendly and intuitive properties in the final HRI-developed module presented here.

Autonomous underwater vehicles are frequently used for survey missions and monitoring tasks, however, manipulation and intervention tasks are still largely performed with a human in the loop. Employing autonomous vehicles for these tasks has received a growing interest in the last ten years, and few pioneering projects have been funded on this topic. Among these projects, the Italian MARIS project had the goal of developing technologies and methodologies for the use of autonomous underwater vehicle manipulator systems in underwater manipulation and transportation tasks. This work presents the developed control framework, the mechatronic integration, and the project's final experimental results on floating underwater intervention. [ABSTRACT FROM AUTHOR]

The aim of this work is to describe the development of an innovative cleaning tool for underwater applications, to be used in particular in the field of underwater archaeology. This work takes place in the framework of the EU FP7–funded ARROWS project. ARROWS adapts and develops low-cost autonomous underwater vehicle technologies to significantly reduce the costs of underwater archaeological operations, covering the full extent of archaeological campaigns. The project deals with underwater mapping, diagnosis and cleaning tasks. During the first half of the project, a cleaning tool prototype, able to be mounted on underwater vehicles, has been worked out: this cleaning tool will be exploited not only during research missions but also for the periodic monitoring, controlling and maintenance activities of well-known underwater archaeological sites (e.g. periodic cleaning operations). [ABSTRACT FROM AUTHOR]

The use of commercially available autonomous underwater vehicles (AUVs) has increased during the last fifteen years. While they are mainly used for routine survey missions, there is a set of applications that nowadays can be only addressed by manned submersibles or work-class remotely operated vehicles (ROVs) equipped with teleoperated arms: the intervention applications. To allow these heavy vehicles controlled by human operators to perform intervention tasks, underwater structures like observatory facilities, subsea panels or oil-well Christmas trees have been adapted, making them more robust and easier to operate. The TRITON Spanish founded project proposes the use of a light-weight intervention AUV (I-AUV) to carry out intervention applications simplifying the adaptation of these underwater structures and drastically reducing the operational cost. To prove this concept, the Girona 500 I-AUV is used to autonomously dock into an adapted subsea panel and once docked perform an intervention composed of turning a valve and plugging in/unplugging a connector. The techniques used for the autonomous docking and manipulation as well as the design of an adapted subsea panel with a funnel-based docking system are presented in this article together with the results achieved in a water tank and at sea. [ABSTRACT FROM AUTHOR]

Intervention autonomous underwater vehicles (I-AUVs) have the potential to open new avenues for the maintenance and monitoring of offshore subsea facilities in a cost-effective way. However, this requires challenging intervention operations to be carried out persistently, thus minimizing human supervision and ensuring a reliable vehicle behaviour under unexpected perturbances and failures. This paper describes a system to perform autonomous intervention-in particular valve-turning-using the concept of persistent autonomy. To achieve this goal, we build a framework that integrates different disciplines, involving mechatronics, localization, control, machine learning and planning techniques, bearing in mind robustness in the implementation of all of them. We present experiments in a water tank, conducted with Girona 500 I-AUV in the context of a multiple intervention mission. Results show how the vehicle sets several valve panel configurations throughout the experiment while handling different errors, either spontaneous or induced. Finally, we report the insights gained from our experience and we discuss the main aspects that must be matured and refined in order to promote the future development of intervention autonomous vehicles that can operate, persistently, in subsea facilities. [ABSTRACT FROM AUTHOR]

The implementation of autonomous intervention tasks with underwater vehicles is a non-trivial issue due to the challenging and dynamic conditions of the underwater medium (e.g., water current perturbations, water visibility). Likewise, it requires a significant programming effort each time that the vehicle must perform a different manipulation operation. In this paper we propose, instead, to use a cognitive system that learns the intervention task from an expert operator through an intuitive learning by demonstration (LbD) algorithm. Taking as an input few operator demonstrations, the algorithm generalizes the task knowledge into a model and is able to control the vehicle and the manipulator simultaneously to reproduce the task, thus conferring a more adaptive behavior in front of the environment changes and allowing to easily transfer the knowledge of new tasks. A cognitive architecture has been implemented in order to integrate the LbD algorithm with the onboard sensors and actuators and to allow its interplay with the vehicle perception, control and navigation modules. To validate the full framework we present real experiments in a water tank using an AUV equipped with a four DoF manipulator. A human operator teaches the system to perform a valve turning intervention and we analyze the results of multiple task reproductions, including cases under the effect of water current perturbations, showing the success of the system in autonomously reproducing the task. [ABSTRACT FROM AUTHOR]

The long term of this ongoing research has to do with increasing degree of autonomy for robots involved in underwater intervention missions. Bearing in mind that the specific mission to face has been the intervention on a panel, in this paper some results in different development stages are presented by using the real mechatronics and the panel mockup. Furthermore, some details are highlighted describing two methodologies implemented for the required visually-guided manipulation algorithms. These algorithms are used to correct problems caused by a bad initialization or miscalibration of the arm. They consist of detecting a marker placed in a known position of the arm and updating the value of each joint after any detection. A roadmap explaining the different testbeds used for experimental validation, in increasing complexity order, is also presented. It is worth mentioning that the aforementioned results would be impossible without previously generated know-how for both the complete developed mechatronics for the autonomous underwater vehicle for intervention, and the required 3D simulation tool. In summary, thanks to the implemented approach, the intervention system is able to control the way in which the gripper approximates and manipulates the two panel devices (i.e. a valve and a connector) in an autonomous manner. Results in different scenarios demonstrate the reliability and feasibility of this autonomous intervention system in water tank and pool conditions. [ABSTRACT FROM AUTHOR]

The use of commercially available autonomous underwater vehicles (AUVs) has increased during the last fifteen years. While they are mainly used for routine survey missions, there is a set of applications that nowadays can be only addressed by manned submersibles or work-class remotely operated vehicles (ROVs) equipped with teleoperated arms: the intervention applications. To allow these heavy vehicles controlled by human operators to perform intervention tasks, underwater structures like observatory facilities, subsea panels or oil-well Christmas trees have been adapted, making them more robust and easier to operate. The TRITON Spanish founded project proposes the use of a light-weight intervention AUV (I-AUV) to carry out intervention applications simplifying the adaptation of these underwater structures and drastically reducing the operational cost. To prove this concept, the Girona 500 I-AUV is used to autonomously dock into an adapted subsea panel and once docked perform an intervention composed of turning a valve and plugging in/unplugging a connector. The techniques used for the autonomous docking and manipulation as well as the design of an adapted subsea panel with a funnel-based docking system are presented in this article together with the results achieved in a water tank and at sea.

This work addresses the problem of optimal positioning for an intervention AUV, minimizing the energy consumption and improving the stability in orientation. During a generic intervention task, the vehicle is generally maintained in a hovering configuration, thus requiring a 6 DOF control of the vehicle positioning. The choice of roll and pitch, if done arbitrarily, can severely impact the power efficiency of the vehicle, especially in heavy systems, since the center of buoyancy (COB) may not be necessarily aligned over the center of mass (COM). This approach uses an Extended Kalman Filter (EKF) to identify the location of the center of buoyancy relative to the center of mass, thus allowing to compute the working orientation that maintains the COB vertically aligned above the COM. The EKF is implemented online and hence is able to detect movements of the COB due for example to ballast operations. This algorithm has been firstly implemented in simulation and then successfully validated with the SAUVIM (Semi-Autonomous Underwater Vehicle for Intervention Missions) autonomous underwater vehicle. With its weight of about 4 tons, this testbed is an optimal platform for validating the precision of the filter, since a very small variation of the target pitch and roll results in a large restoring torque. [ABSTRACT FROM AUTHOR]

Abstract: Many underwater intervention tasks are today performed using manned submersibles or remotely operated vehicles in teleoperation mode. Autonomous underwater vehicles are mostly employed in survey applications. In fact, the low bandwidth and significant time delay inherent in acoustic subsea communications represent a considerable obstacle to remotely operate a manipulation system, making it impossible for remote controllers to react to problems in a timely manner. Nevertheless, vehicles with no physical link and with no human occupants permit intervention in dangerous areas, such as in deep ocean, under ice, in missions to retrieve hazardous objects, or in classified areas. The key element in underwater intervention performed with autonomous vehicles is autonomous manipulation. This is a challenging technology milestone, which refers to the capability of a robot system that performs intervention tasks requiring physical contacts with unstructured environments without continuous human supervision. Today, only few AUVs are equipped with manipulators. SAUVIM (Semi Autonomous Underwater Vehicle for Intervention Mission, University of Hawaii) is one of the first underwater vehicle capable of autonomous manipulation. This paper presents the solutions chosen within the development of the system in order to address the problems intrinsic to autonomous underwater manipulation. In the proposed approach, the most noticeable aspect is the increase in the level of information transferred between the system and the human supervisor. We describe one of the first trials of autonomous intervention performed by SAUVIM in the oceanic environment. To the best knowledge of the authors, no sea trials in underwater autonomous manipulation have been presented in the literature. The presented operation is an underwater recovery mission, which consists in a sequence of autonomous tasks finalized to search for the target and to securely hook a cable to it in order to bring the target to the surface. [Copyright &y& Elsevier]

The need for intervention in underwater environments has increased in recent years but there is still a long way to go before AUVs (Autonomous Underwater Vehicles) will be able to cope with really challenging missions. Nowadays, the solution adopted is mainly based on remote operated vehicle (ROV) technology. These ROVs are controlled from support vessels by using unnecessarily complex human–robot interfaces (HRI). Therefore, it is necessary to reduce the complexity of these systems to make them easier to use and to reduce the stress on the operator. In this paper, and as part of the TWIN roBOTs for the cooperative underwater intervention missions (TWINBOT) project, we present an HRI (Human-Robot Interface) module which includes virtual reality (VR) technology. In fact, this contribution is an improvement on a preliminary study in this field also carried out, by our laboratory. Hence, having made a concerted effort to improve usability, the HRI system designed for robot control tasks presented in this paper is substantially easier to use. In summary, reliability and feasibility of this HRI module have been demonstrated thanks to the usability tests, which include a very complete pilot study, and guarantee much more friendly and intuitive properties in the final HRI-developed module presented here. [ABSTRACT FROM AUTHOR]