This research addresses the need for advanced perception systems in offshore crane operations, driven by the global shift towards sustainable energy and the increasing complexity of offshore wind farms. Offshore crane operations are essential for the installation and maintenance of these structures, and currently rely heavily on the subjective judgement of operators. This dependence contributes significantly to human error, with approximately 60-80% of accidents linked to poor situational awareness and misperception. Huisman Equipment identified the potential for enhancing safety and operational efficiency by employing advanced sensing technologies, such as 3D-Light Detection and Ranging (LiDAR) systems.

A comprehensive review of existing technologies and methodologies for load detection and tracking highlighted the advantages of LiDAR sensors over cameras, radar, and radio-based sensors. Particularly in offshore environments, where visibility is often compromised, LiDAR’s ability to produce high-resolution, three-dimensional point cloud data, coupled with the stability of a fixed-mounted setup, ensures reliable and consistent monitoring of loads during crane operations.

To achieve accurate load detection using a single LiDAR sensor, the study incorporated techniques such as Statistical Outlier Removal (SOR) and voxel grid downsampling for effective data preprocessing. RANSAC and HDBSCAN were employed for robust background removal and clustering, respectively. These methods were seamlessly integrated into the detection architecture, demonstrating reliable performance against key performance indicators (KPIs) and achieving high accuracy under varying conditions.

The research identified suitable motion models for reliably tracking the movement of detected loads, including a constant turn model for horizontal motion and a constant velocity model for vertical motion. These models were integrated with the Unscented Kalman Filter (UKF) for tracking and the Joint Probabilistic Data Association Filter (JPDAF) for data association. Experimental results confirmed the system’s ability to accurately track load positions, maintain precision, and distinguish loads from clutter in dynamic offshore scenarios.

An experimental setup was designed to replicate real-world conditions and collect data to develop and test the proposed LiDAR-based methods. The setup, simulating the operational environment of a vessel-mounted LiDAR system, provided diverse datasets essential for validating the detection and tracking methods. Testing confirmed that the setup effectively replicated offshore conditions, supporting the refinement and evaluation of the system. The developed methods were rigorously evaluated for their accuracy, reliability, and performance in simulated offshore crane operations. The detection system consistently achieved accuracy exceeding 70% in high-performance scenarios, with effective clustering indices surpassing minimum thresholds. Tracking results demonstrated reliable load identification and positional precision, despite minor systematic errors. The methods proved robust and reliable, addressing key challenges in offshore environments.

In conclusion, this study successfully developed and validated a fixed-mounted LiDAR system for load detection and tracking in offshore crane operations. The research provides a practical, technology driven solution to improve safety and efficiency, while future work should explore enhancing the system’s capabilities under adverse weather conditions and integrating additional sensor technologies to further advance situational awareness.

This thesis presents a thorough study into the development and implementation of a control method for autonomous unmooring, trajectory tracking and mooring on an autonomous model-scale vessel.
Mooring and unmooring are vital processes in the operation of ships, as it is the system that secures and releases a ship to a terminal or multiple terminals. The process has remained relatively identical over the years, whereas autonomous shipping has been researched over time. This study addresses the lack of focus given to autonomous mooring and unmooring by offering a control strategy that leverages the vessel’s thrusters to perform these tasks, along with trajectory tracking. The study begins by reviewing existing research on trajectory tracking and maritime vessel mooring/unmooring, revealing the gaps in the integration of both procedures within autonomous operations.

To overcome these challenges, the study selects an applicable mathematical model for the vessel which will be controlled. This model covers kinematic and kinetic elements, as well as actuation and thruster allocation. This sets the groundwork for the development of a control strategy capable of precisely predicting and tracking the vessel’s position during unmooring, and trajectory tracking and mooring. Model Predictive Control (MPC) was chosen as an ideal control approach due to its ability to predict future states and effectively handle the complexities of marine operations. MPC makes it ideal for the difficulties of mooring and unmooring, where precise control is necessary to ensure safety and efficiency.

The control strategy was then developed and implemented in MATLAB/Simulink, with the approach modified to meet the model-scale vessel’s specific dynamics and operational requirements.
In addition, Key Performance Indicators (KPIs) to assess the effectiveness of the strategy were introduced. The strategy’s performance was assessed in three operational phases: unmooring, trajectory tracking, and mooring. The results show that the MPC controls the vessel’s trajectory well, with errors staying well below acceptable bounds. The largest deviation of the final trajectory during the mooring test was 9.8% of the length of the ship. A 1.0% deviation for the benchmark trajectories could also be observed. These results, in addition to others, validate the suggested strategy’s accuracy and dependability in practical situations.

In summary, this thesis provides a comprehensive control approach for autonomous unmooring, trajectory tracking and mooring for an autonomous model-scale vessel, bridging the gap between theoretical study and implementation in simulation. While the research identifies several limitations that present the potential for additional study, it also lays a solid foundation for future developments in autonomous maritime technology.