Multi-domain inspection of offshore wind farms using an autonomous surface vehicle

Abstract

The offshore wind power industry is an emerging and exponentially growing sector, which calls to a necessity for a cyclical monitoring and inspection to ensure the safety and efficiency of the wind farm facilities. Thus, the emersed (aerial) and immersed (underwater) scenarios must be reconstructed to create a more complete and reliable map that maximizes the observability of all the offshore structures from the wind turbines to the cable arrays, presenting a multi domain scenario.This work proposes the use of an Autonomous Surface Vehicle (ASV) to map both domains simultaneously. As such, it will produce a multi-domain map through the fusion of navigational sensors, GPS and IMU, to localize the vehicle and aid the registration process for the perception sensors, 3D Lidar and Multibeam echosounder sonar. The performed experiments demonstrate the ability of the multi-domain mapping architecture to provide an accurate reconstruction of both scenarios into a single representation using the odometry system as the initial seed to further improve the map with data filtering and registration processes. An error of 0.049 m for the odometry estimation is observed with the GPS/IMU fusion for simulated data and 0.07 m for real field tests. The multi-domain map methodology requires an average of 300 ms per iteration to reconstruct the environment, with an error of at most 0.042 m in simulation.

Nautilus: An autonomous surface vehicle with a multilayer software architecture for offshore inspection

“…Figure 20a illustrates the multidomain map of Durius' neighborhood, where the red to purple color scale represents the Z ‐axis variation from minimum to maximum, respectively. For such, a 3D LiDAR odometry, the Continuous‐Time Iterative Closest Point (CT‐ICP) (Dellenbach et al, 2021), was used to estimate the ASV position given that it was operating in proximity to structures, which allows for a more accurate localization and, then, using the method proposed by Campos, Matos et al (2021) to reconstruct the multidomain map. As it can be seen, the seafloor representation is sparse (red and orange areas).…”

Applications of robotics in floating offshore wind farm operations and maintenance: Literature review and trends

“…Nevertheless, soft robotic systems provide promising potential for conducting O&M in the FOWF environment. Based on 63 Máthé et al, 43 Campos et al 73 Inspection of array/ export cables ROV, AUV camera, grippers High -increased usage of ROVs for fault detection and fatigue inspection in sub-components Albiez et al 74 Fahrni et al 20 Burial of export cables ROV camera, grippers High -burial of cables is done using work-class ROVs, inspection of defects and fatigue in sub-components Cho et al 75 Marine growth on subsea structures…”

Modular Multi-Domain Aware Autonomous Surface Vehicle for Inspection

“…This sensor publishes high accuracy point clouds, where the higher the distances the sparser data becomes. Moreover, the sensor wave length is 905 nm, as such it can only penetrate 1 cm of the water surface providing no reflections, thus the wave impact will translate into missing data instead of spurious data [54].…”

“…As such, by applying the method proposed by Campos et al [54] to a partial sequence of the ROAM@CRAS it was possible to create a multi-domain map of the harbor. This approach uses the SENSE localization estimation to provide the initial seed of the system state.…”