Oil & gas operators spend tens of million USD every year on removing marine growth, using manually operated remotely operated vehicles (ROV) with water jets. This study investigates the benefits of automating the ROV used for cleaning by demonstrating a sliding mode control (SMC) algorithm on a reconfigured BlueROV2 with an attached water jet. A nonlinear SMC was designed for the cleaning task. SMC was able to stabilize the orientation of the ROV while following a trajectory in depth. Regular SMC could not stabilize the ROV in front of the member, with the water jet activated. To accommodate for the delay, integral action was added to the SMC (IxSMC) in the surge direction, which stabilized the ROV. From the research presented in this paper, it can be concluded that automation of a marine growth removing ROV can be achieved by applying IxSMC.

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Marine growth affects offshore structures, causing additional weight and roughened surfaces, increasing wave load. In order to reduce these issues, regular inspection and cleaning can be carried out using various methods, of which one is Remotely Operated Vehicle-based (ROV) operations. In the work presented here, the design of a task-specific ROV for marinegrowth cleaning is described, which is differentiated from the normal general-purpose ROVs currently used for this purpose by specialized construction and the use of a simple yet flexible framework. Compared to existing solutions, the proposed framework requires limited low-level programming, which heavily simplifies the implementation and thus reduces the associated practical overhead. The presented ROV prototype design has been demonstrated in a test tank facility and will be validated in an offshore scenario in a future offshore campaign.

Offshore pipelines and structures require regular marine growth removal and inspection to ensure structural integrity. These operations are typically carried out by Remotely Operated Vehicles (ROVs) and demand reliable and accurate feedback signals for operating the ROVs efficiently under harsh offshore conditions. This study investigates and quantifies how sensor delays impact the expected control performance without the need for defining the control parameters. Input-output (IO) controllability analysis of the open-loop system is applied to find the lower bound of the H-infinity peaks of the unspecified optimal closed-loop systems. The performance analyses have shown that near-structure operations, such as pipeline inspection or cleaning, in which small error tolerances are required, have a small threshold for the time delays. The IO controllability analysis indicates that off-structure navigation allow substantial larger time delays. Especially heading is vulnerable to time delay; however, fast-responding sensors usually measure this motion. Lastly, a sensor comparison is presented where available sensors are evaluated for each ROV motion’s respective sensor-induced time delays. It is concluded that even though off-structure navigation have larger time delay tolerance the corresponding sensors also introduce substantially larger time delays.

This paper presents a simulation model environment for the popular and low-cost remotely operated vehicle (ROV) BlueROV2 implemented in Simulink™ which has been designed and experimentally validated for benchmark control algorithms for underwater vehicles. The BlueROV2 model is based on Fossen’s equations and includes a kinematic model of the vehicle, the hydrodynamics of vehicle and water interaction, a dynamic model of the thrusters, and, lastly, the gravitational/buoyant forces. The hydrodynamic parameters and thruster model have been validated in a test facility. The benchmark model also includes the ocean current, modeled as constant velocity. The tether connecting the ROV to the top-site facility has been modeled using the lumped mass method and is implemented as a force input to the ROV model. At last, to show the usefulness of the benchmark model, a case study is presented where a BlueROV2 is deployed to inspect an offshore monopile structure. The case study uses a sliding mode controller designed for the BlueROV2. The controller fulfills the design criteria defined for the case study by following the provided trajectory with a low error. It is concluded that the simulator establishes a benchmark for future control schemes for position control and trajectory tracking under the influence of environmental disturbances.