This thesis report proposes a framework to implement Navigation, Guidance and Control (GNC) systems, that enable point-to-point autonomy for displacement vessels. A model-based control approach is chosen as the basis of the GNC systems. The resulting algorithms are implemented fo
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This thesis report proposes a framework to implement Navigation, Guidance and Control (GNC) systems, that enable point-to-point autonomy for displacement vessels. A model-based control approach is chosen as the basis of the GNC systems. The resulting algorithms are implemented for verification in a 1:25 scale model of a Azimuth Stern Drive (ASD) 3111 Damen tug named "Damen Autonomous Ship", aka DASh.

First, a compact maneuvering model that captures relevant dynamics of displacement vessel is formulated and identified using system identification.

Secondly,the guidance system is automated such that it connects an initial state to a goal state with a collision

free path that satisfies all input and differential constraints of the vessel model. To this end, the kinodynamic

Rapidly-exploring Random Tree (RRT) algorithm is extended to use a maneuver automaton and optimal motion primitives in its steering function. A learned cost-to-go distance metric for the state space is formulatedto efficiently calculate distance between states, which is used to search for nearest neighbors in the kino-

dynamic RRT algorithm. The performance of the planner using the learned cost-to-go distance metric is compared to a minimal curve length distance metric based on

Dubins Curves and the commonly used straight-line Euclidean distance metric. It is shown that the learned cost-to-go and the minimal curve length distance metric result in paths of similar performance while the Euclidean metric performs severely worse.

Lastly, the navigation and control systems are implemented on DASh. Due to disturbances present in real

world environments, the paths must be tracked using feedback control. State estimation for navigation,

based on position and heading measurements is performed by implementing an observer using a Extended

Kalman Filter (EKF). Non-linear model predictive control (NMPC) in combination with thrust allocation is used to control the vessel during path execution. Due to real time requirements of DASh, the EKF, NMPC and the thrust allocation algorithm are directly implemented inefficient C ++, on the on-board computer of DASh.

It is shown that NMPC converges to static reference positions faster and with less control inputs compared to

traditional non-linear PD control. It is also shown that time-varying trajectories created by the kinodynamic RRT can be executed successfully. This shows that the identified model is suitable for use in model-based

control and that the planned paths indeed satisfy the input and differential constraints of the vessel.