The ocean and human activities related to the sea are under increasing pressure due to climate change, widespread pollution, and growth of the offshore energy sector. Data, in under-sampled regions of the ocean and in the offshore patches where the industrial expansion is taking place, are fundamental to manage successfully a sustainable development and to mitigate climate change. Existing technology cannot cope with the vast and harsh environments that need monitoring and sampling the most. The limiting factors are, among others, the spatial scales of the physical domain, the high pressure, and the strong hydrodynamic perturbations, which require vehicles with a combination of persistent autonomy, augmented efficiency, extreme robustness, and advanced control. In light of the most recent developments in soft robotics technologies, we propose that the use of soft robots may aid in addressing the challenges posed by abyssal and wave-dominated environments. Nevertheless, soft robots also allow for fast and low-cost manufacturing, presenting a new potential problem: marine pollution from ubiquitous soft sampling devices. In this study, the technological and scientific gaps are widely discussed, as they represent the driving factors for the development of soft robotics. Offshore industry supports increasing energy demand and the employment of robots on marine assets is growing. Such expansion needs to be sustained by the knowledge of the oceanic environment, where large remote areas are yet to be explored and adequately sampled. We offer our perspective on the development of sustainable soft systems, indicating the characteristics of the existing soft robots that promote underwater maneuverability, locomotion, and sampling. This perspective encourages an interdisciplinary approach to the design of aquatic soft robots and invites a discussion about the industrial and oceanographic needs that call for their application.

Predictive control methods can substantially improve the performance of Unmanned Underwater Vehicles (UUVs), particularly in shallow water environments or near the free surface where wave induced disturbance are of magnitude comparable to the vehicle characteristic inertia. To facilitate the adoption of these methods, a fast estimation of the time evolution of hydrodynamic forces acting on a vehicle is required. To this end, we perform experiments in a wave tank with an ROV to validate the use of Linear Wave Theory (LWT) to capture the time history of surge, heave and pitch wave induced forces and moments. Validation is performed for various sea states, reconstructed with a mean correlation of 0.9138 in comparison to experimental measurements, displaying a maximum normalised mean error deviation between simulation and experimental data of 0.16 and 0.27 respectively for surge and heave forces, and 0.34 for pitch moment. The effectiveness of employing real-time wave disturbance forecasting for the purpose of anticipatory control is then assessed by incorporating the predicted loads within a Model Predictive Controller. Results display a mean RMS positional error reduction of 47.32% in comparison to a standard PD controller. This presents evidence that accurate, near real-time predictions of the wave-generated forces and moments on an ROV can be produced, laying the foundation for developing model-based predictive control strategies that better suit operation in harsh environments.

The ability to navigate complex unstructured environments and carry out inspection tasks requires robots to be capable of climbing inclined surfaces and to be equipped with a sensor payload. These features are desirable for robots that are used to inspect and monitor offshore energy platforms. Existing climbing robots mostly use rigid actuators, and robots that use soft actuators are not fully untethered yet. Another major problem with current climbing robots is that they are not built in a modular fashion, which makes it harder to adapt the system to new tasks, to repair the system, and to replace and reconfigure modules. This work presents a 450 g and a 250 × 250 × 140 mm modular, untethered hybrid hard/soft robot—Limpet II. The Limpet II uses a hybrid electromagnetic module as its core module to allow adhesion and locomotion capabilities. The adhesion capability is based on negative pressure adhesion utilizing suction cups. The locomotion capability is based on slip-stick locomotion. The Limpet II also has a sensor payload with nine different sensing modalities, which can be used to inspect and monitor offshore structures and the conditions surrounding them. Since the Limpet II is designed as a modular system, the modules can be reconfigured to achieve multiple tasks. To demonstrate its potential for inspection of offshore platforms, we show that the Limpet II is capable of responding to different sensory inputs, repositioning itself within its environment, adhering to structures made of different materials, and climbing inclined surfaces.

The Italian Fixed-Point Observatory Network (IFON) integrates well-established coastal and ocean infrastructures (buoys, platforms, moorings, mast platforms, etc.), most of them providing real-time multidisciplinary monitoring for a number of marine and atmospheric variables. Here, we describe the network characteristics and then discuss an example of its operation during the cold spell of winter 2012. One of the goals of the Italian Flagship Project Ricerca Italiana per il mare (RITMARE) is to create a common, validated IFON database able to fulfil both public and private demands, including validation of remotely sensed data and numerical models, environmental planning and management, and time-series analysis of climate and oceanographic data.

Hydrodynamic forces are an important input value for the design, navigation and station keeping of underwater Remotely Operated Vehicles (ROVs). The experiment investigated the forces imparted by currents (with representative real world turbulence) and waves on a commercially available ROV, namely the BlueROV2 (Blue Robotics, Torrance, USA). Three different distances of a simplified cylindrical obstacle (shading effects) were investigated in addition to the free stream cases. Eight tethers held the ROV in the middle of the 2 m water depth to minimise the influence of the support structure without completely restricting the degrees of freedom (DoF). Each tether was equipped with a load cell and small motions and rotations were documented with an underwater video motion capture system. The paper describes the experimental set-up, input values (current speed and wave definitions) and initial processing of the data. In addition to the raw data, a processed dataset is provided, which includes forces in all three main coordinate directions for each mounting point synchronised with the 6DoF results and the free surface elevations. The provided dataset can be used as a validation experiment as well as for testing and development of an algorithm for position control of comparable ROVs.