Auto-docking of marine vessels

A control unit configured to perform the steps of a computer-implemented method for generating control parameters for auto-docking of a marine vessel from an initial state including a start position, to a final state including an end position within a target total time, the method including the steps of:

• in a prior offline session, retrieving datasets including prior control parameters provided by human operators made for docking a marine vessel from a starting point to an end point;
• providing a machine learning model configured to predict an initial guess of a set of control parameters and a collision-free path for a marine vessel, the machine learning model being trained on the datasets including the prior control parameters provided by human operators made for docking the marine vessel from the starting point to the end point;
• training the machine learning model on the datasets to mimic control parameters made by a human operator;
• deploying the trained machine learning model in a marine vessel auto-docking system;
• providing an optimization function that describes the total time and energy consumption as a function of control parameters for docking a marine vessel from an initial state to a final state, provided constraints on starting point, ending point, and marine vessel dynamics during maneuvering,
• retrieving, from a computer interface, a start position and an end position for a present auto-docking process for a marine vessel, and locations of obstacles between start and end positions,
• processing the start position and end position, and locations of obstacles, in the machine learning model to generate an initial guess set of control parameters and collision-free path;
• determining output control parameters by minimizing the optimization function using the initial guess set of control parameters and the collision free path as starting input to minimizing the total time and energy consumption for auto-docking the marine vessel,
• providing output control parameters and an output collision-free path extracted from the minimization of the optimization function to an auto-docking system of the marine vessel to control the maneuvering of the marine vessel to perform an auto-docking process;
• and
• maneuvering the marine vessel from the start position to the end position using the output control parameters to control thrusters of the marine vessel.

A marine vessel comprising a control unit configured to perform the steps of a computer-implemented method for generating control parameters for auto-docking of a marine vessel from an initial state including a start position, to a final state including an end position within a target total time, the method including the steps of:

• in a prior offline session, retrieving datasets including prior control parameters provided by human operators made for docking a marine vessel from a starting point to an end point;
• providing a machine learning model configured to predict an initial guess of a set of control parameters and a collision-free path for a marine vessel, the machine learning model being trained on the datasets including the prior control parameters provided by human operators made for docking the marine vessel from the starting point to the end point;
• training the machine learning model on the datasets to mimic control parameters made by a human operator;
• deploying the trained machine learning model in a marine vessel auto-docking system;
• providing an optimization function that describes the total time and energy consumption as a function of control parameters for docking a marine vessel from an initial state to a final state, provided constraints on starting point, ending point, and marine vessel dynamics during maneuvering,
• retrieving, from a computer interface, a start position and an end position for a present auto-docking process for a marine vessel, and locations of obstacles between start and end positions,
• processing the start position and end position, and locations of obstacles, in the machine learning model to generate an initial guess set of control parameters and collision-free path;
• determining output control parameters by minimizing the optimization function using the initial guess set of control parameters and the collision free path as starting input to minimizing the total time and energy consumption for auto-docking the marine vessel;
• providing output control parameters and an output collision-free path extracted from the minimization of the optimization function to an auto-docking system of the marine vessel to control the maneuvering of the marine vessel to perform an auto-docking process;
• and
• maneuvering the marine vessel from the start position to the end position using the output control parameters to control thrusters of the marine vessel.

A computer program product comprising a non-transitory computer-readable medium including program code for generating control parameters for auto-docking of a marine vessel from an initial state including a start position, to a final state including an end position within a target total time, the program code including:

• code for retrieving, in a prior offline session, datasets including prior control parameters provided by human operators made for docking a marine vessel from a starting point to an end point;
• code for providing a machine learning model configured to predict an initial guess of a set of control parameters and a collision-free path for a marine vessel, the machine learning model being trained on the datasets including the prior control parameters provided by human operators made for docking the marine vessel from the starting point to the end point;
• code for training the machine learning model on the datasets to mimic control parameters made by a human operator;
• code for deploying the trained machine learning model in a marine vessel auto-docking system;
• code for providing an optimization function that describes the total time and energy consumption as a function of control parameters for docking a marine vessel from an initial state to a final state, provided constraints on starting point, ending point, and marine vessel dynamics during maneuvering,
• code for retrieving, from a computer interface, a start position and an end position for a present auto-docking process for a marine vessel, and locations of obstacles between start and end positions,
• code for processing the start position and end position, and locations of obstacles, in the machine learning model to generate an initial guess set of control parameters and collision-free path;
• code for determining output control parameters by minimizing the optimization function using the initial guess set of control parameters and the collision free path as starting input to minimizing the total time and energy consumption for auto-docking the marine vessel;
• code for providing output control parameters and an output collision-free path extracted from the minimization of the optimization function to an auto-docking system of the marine vessel to control the maneuvering of the marine vessel to perform an auto-docking process;
• and
• code for maneuvering the marine vessel from the start position to the end position using the output control parameters to control thrusters of the marine vessel.