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.

Vessel platform automation developed notably categorizable in two behaviors: reconfiguration and collaborative motion control. This work explores integration of both behaviors in a single system. Fundamental system characteristics of both behaviors are analyzed to create understanding of the varying design approaches. A multi-vessel platform model approximation is proposed that expresses all module models in one generalized platform coordinate while retaining directional dependent effects. Automated reconfiguration and collaborative, coordinated dynamic positioning are implemented within a single framework. The multi-robot control stucture consists of guidance, navigation & control layers, rather than single systems, where topology changes with platform configuration.

The inland waterway once enabled an industrial revolution, yet the emergence of coalescent road networks has seen its true worth be all but disregarded. Despite the amenity of unimodal travel being compelling, growing awareness for sustainability has reignited interest in more fuel efficient modalities for transportation. Autonomous shipping has the potential to increase efficiency, reliability and safety and will arguably play a major role in the evolving transport revolution, returning the transport modality to its former glory.

The main objective of this research is to cater a collision avoidance strategy to the inland waterway through the development of tailored Guidance and Navigation Systems. An approach to local path planning is introduced to handle the challenges of collision avoidance on the inland waterway and a better understanding of the primary role that stereovision sensors could assume in enabling inland autonomy is gained. Achieving this objective requires first a reflection upon existing work through a study into the state-of-the-art. Subsequently, the Guidance and Navigation Systems are developed and implemented on a scale test vessel. Finally experimental testing is conducted for the evaluation of the developed system performance.

Formation control of autonomous surface vessels (ASVs) is researched extensively in the last years, since it has promising applications. However very few designed strategies have been validated during real experiments. In this research, two
control methods for distributed leader-follower formation control are proposed: A Nonlinear Model Predictive Control (NMPC) method and an MPC method using Feedback Linearization (FL). These two designed methods are compared with a conventional Proportional-Integral (PI) control method. The performance of the proposed strategies is evaluated through simulations and real experiments using small-scale Roboat units. Both designed control methods outperform the conventional PI control method in simulation and real experiments.

Artificial intelligence is used in this research to predict the shelf life of strawberries. The prediction of shelf life is based on temperature measurements from the moment a package of strawberries is harvested till the moment this same package is bought by a customer in a local PLUS supermarket. The strawberries are harvested in the south of Spain, near Huelva and distributed to local PLUS Supermarkets near Rotterdam. After the packages with strawberries, including temperature loggers, arrive at the supermarket shelf, the packages are moved to a shelf life room for visual inspection. During this daily inspection, the actual shelf life of the strawberries is determined by a classified inspector. The combination of the actual shelf life and the temperature profile through the supply chain is used to train, validate and test different machine learning algorithms. The most reliable shelf life prediction algorithm is the Exponential Gaussian Process Regression Algorithm, with the smallest confidence interval and an average deviation of 14.1 \%. To conclude, the possible improvements in the supply chain based on shelf life prediction, like traceability, food date labeling and quality grading are evaluated.