# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 3 Implementing Collaborative UVs for the OWT IMR Operations
### Collaborative Localization and Navigation Strategies
Using one of the blades as an axis, any point of interest on the OTW blades can be localized accurately [107]. However, this approach is only suitable to be carried out from a distance, purposely for visual inspection. For IMR with close contact operations, as soon as the origin is located, the flight path towards any point of interest can be planned. However, without the GNSS fused with the IMU navigation system, the vision-guided UAV will lose its positioning while approaching a large structure, as the view will be zoomed in. To overcome this matter, the visual-inertial odometry technique using point and line features can be implemented [84]. This approach has enabled a UAV to work robustly in strong electromagnetic interference, low-textured and illumination-change windfarm. Since the visual-inertial odometry technique is based on dead-reckoning localization, it is still susceptible to drift from the true localization due to the accumulation of errors in relative motion estimation [111]. Using a collaborative approach may solve this problem by constantly updating the position of the UAV with respect to the WT origin. This can be implemented by deploying another UAV or a swarm of UAVs that has the capability of monitoring the UAV working near the OWT with full visualization [112]. The monitoring UAVs, having a direct view of the OWT origin with respect to the working UAV, might transmit the correct position through radio frequency communication [113; 114], if possible, or using a visible light communication [115], which is immune to interference from electromagnetic sources. Another possible method is by using ultraviolet direction and ranging [116] to steer another UAV in a three-dimensional position and orientation relative to the monitoring UAV. Nevertheless, prior knowledge of a WT structure, for example, the blade length or the tower height, is needed to determine the absolute localization of the WT, which is one of the drawbacks when using visual-based measurement. Nonetheless, with recent advancements in the twin digital technology of wind farms [117], prior knowledge on the OWT structures can easily be retrieved. On the other hand, the visual SLAM methods do not require prior knowledge of the WT structures. Using a specific algorithm with a generated synthetic depth map to localize a UAV within an environment [85], a WTB can be detected at its centre (hub). However, since the UAV uses a flight plan guided by the IMU to approach the WTB, it cannot prevent drifting errors from occurring. Again, this can be solved using the collaborative approach for visual-based navigation as previously discussed. Another method, a LiDAR-based SLAM approach, has been proposed for WTB localization [108]. Within this approach, the proposed algorithm manages to map a WTB with greater performance when the proximity is around 0.5 m distance to the blade. Localization is achieved by marking the origin to the centre of the scanned blade. However, the centre of the origin can be set to the hub as the WT centre when full scanning is completed. For OWT blade inspection, the LiDAR-based SLAM can be initiated by initially guiding a UAV close to a WTB, followed by efficient LiDAR scanning [118]. With this approach, post-processing of the WTB inspection data can be stored, where analysis of the recorded inspection data can later be made and future revisiting for maintenance and repair operations is also made possible. For future revisiting, a LiDAR-based SLAM UAV should be initially guided to the recorded origin location (hub) where a recorded map from the previous visit can be restored and matched using a specific algorithm [119]. The UAV can then approach any point of interest on the map, starting from the origin point using the improved Reference LiDAR-based Odometry and Mapping (R-LOAM) approach [120]. Using the LiDAR-based SLAM technique, 1 to 2 centimetre-level mapping accuracy can be achieved [121]; hence, future revisiting becomes easier.