Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines

Abstract

Operations and maintenance of Offshore Wind Turbines (OWTs) are challenging, with manual operators constantly exposed to hazardous environments. Due to the high task complexity associated with the OWT, the transition to unmanned solutions remains stagnant. Efforts toward unmanned operations have been observed using Unmanned Aerial Vehicles (UAVs) and Unmanned Underwater Vehicles (UUVs) but are limited mostly to visual inspections only. Collaboration strategies between unmanned vehicles have introduced several opportunities that would enable unmanned operations for the OWT maintenance and repair activities. There have been many papers and reviews on collaborative UVs. However, most of the past papers reviewed collaborative UVs for surveillance purposes, search and rescue missions, and agricultural activities. This review aims to present the current capabilities of Unmanned Vehicles (UVs) used in OWT for Inspection, Maintenance, and Repair (IMR) operations. Strategies to implement collaborative UVs for complex tasks and their associated challenges are discussed together with the strategies to solve localization and navigation issues, prolong operation time, and establish effective communication within the OWT IMR operations. This paper also briefly discusses the potential failure modes for collaborative approaches and possible redundancy strategies to manage them. The collaborative strategies discussed herein will be of use to researchers and technology providers in identifying significant gaps that have hindered the implementation of full unmanned systems which have significant impacts towards the net zero strategy.

A multirobot system for autonomous deployment and recovery of a blade crawler for operations and maintenance of offshore wind turbine blades

“…A second method is a challenging approach in the offshore environment as fixed anchor points are required. Additionally, the current UWB systems have an accuracy of less than 5 cm if the distance from anchor points is in the range of 50 to 75 m (Nordin et al, 2022) As deduced from the literature, rather than relying solely on GPS/GNSS for accurate navigation, researchers have adapted other methods among which onboard LiDAR and vision systems are popular. In this paper, an approach merging GNSS localization with LiDAR data has been adopted.…”

“…For instance, a GNSS receiver, rather than reading direct signals, might read the reflected signals from the wind turbine structure (multipath errors). Nordin et al (2022) have proposed two solutions: (1) visual localization and navigation, and (2) use of ultra‐wide band (UWB) technology and anchor points. A second method is a challenging approach in the offshore environment as fixed anchor points are required.…”

“…A second method is a challenging approach in the offshore environment as fixed anchor points are required. Additionally, the current UWB systems have an accuracy of less than 5 cm if the distance from anchor points is in the range of 50 to 75 m (Nordin et al, 2022). UWB signals can reach 200 m distances at the expense of lower accuracy.…”

An Overview to Unmanned Aerial Vehicles from Perspectives of Quality and Safety Management in Aviation

“…• Delivering medicine, food and water to the injured Schweizer et al, (2018) • Taking images of areas or infrastructures for damage assessment after disasters Yanchao Liu, (2019) • Food delivery Delivery of Goods Bhatt et al, (2018) • Medicine and blood samples delivery Sumanth Reddy et al, (2019) • Lightweight commercial products delivery Ghazali et al, (2021) • Network gateways Providing Wireless Coverage Wu et al, (2021) • Omnipresent coverage Tazibt et al, (2022) • Data collection S. K. Khan et al, (2021) • Relay nodes between wireless devices Fu et al, (2020) • Crop maturity and yield mapping Precision Agriculture Christiansen et al, (2017) • Field tile mapping Romero et al, (2018) • Irrigation scheduling Faiçal et al, (2017) • Pesticide spraying Kerkech et al, (2020) • Disease detection Stroppiana et al, (2018) • Weed detection Huuskonen & Oksanen, (2018) • Soil texture mapping Kavoosi et al, (2020) • Residue cover and tillage mapping Kas & Johnson, (2020) • Infrastructure internal inspection Infrastructure Inspection Gopalakrishnan et al, (2018) • Surface degradation assessment and crack detection Tan et al, (2021) • Vertical inspection for towers and high rise Jalil et al, (2019) • Power transmission lines inspection Gu et al, (2020) • Wind and oil/gas turbine inspection Nordin et al, (2022) • Onshore and offshore asset inspection Other than their operational purposes, the types of UAVs also differ according to their design features which affect their flying principles. In Figure 5, UAV types are classified first according to their vehicle mass, then the heavy types are further classified to rotor and wing types (Liew et al, 2017).…”

Cost‐benefit assessment framework for robotics‐driven inspection of floating offshore wind farms

“…Timely inspection using UAVs can lead to early detection and maintenance that can increase the assets' lifecycle. 14 UAV-assisted videography can lead to identification and classification of damages and can determine the structural integrity of the asset. 15 The coordinated control of multiple UAVs and route optimization 16 can lead to a reduction in the inspection time of turbine blades.…”