Artificial intelligence based marine autopilot: Trained using reinforcement learning in the Unity simulation environment
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2022
Författare
Asplund, Martin
Näslund, David
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
When driving a ship, especially long distances, an autopilot is usually deployed.
Since the ship is subjected to disturbances from currents and wind alike, it is not
easy to keep a steady course. Furthermore, the dynamic behaviour of waves striking
the vessel, known as swaying, makes the task of keeping a straight line through
the sea daunting. To assist, there exist commercial autopilots. However, most of
these are subject to less than simple calibration, which also is hard to keep accu rate throughout the lifespan of the boat due to wear, different load conditions, and
other similar things. Also, there is generally no adaptability related to the autopilot, meaning a sudden change in engine performance will stop the autopilot from
functioning. Further, the majority of today’s commercial autopilots are designed
to follow a course or a heading, known as course-hold and heading-hold autopilots.
Hence, there exists a desire to develop a more adaptable path-following autopilot.
One way of solving the adaptability issue is to borrow the solution from the
aircraft industry and use a control allocator. Given a set of global forces (usually
Fx, Fy, and moment Mz) the control allocator tries to distribute these between a
given set of actuators. Since the number of control signals usually is far less than
the number of actuators, these systems are said to be over-actuated, and no unique
solution exists.
This work aims at exploring a new way of constructing computationally efficient
regulation and control allocation for vessels, in the form of a path-following autopilot.
The hypothesis is that, by using a Neural Network as a control allocator, better
performance and adaptability than offered by present solutions can be achieved.
Beskrivning
Ämne/nyckelord
Unity, Reinforcement learning , Simulation, Autopilot , Marine , Artificial Intelligence , Neural Network , Control Allocation