| dc.description.abstract | The development of Autonomous Surface Vessels (ASVs) has during the recent years seen a great
progress. When being completely developed, these vessels may be used in a variety of scientific
and commercial operations, eventually leading human based operations redundant.
An ASV needs a well-functioning Collision Avoidance (COLAV) system in order to operate
at sea where other obstacles such as vessels and land are present. In addition of being
able to handle collision situations, the COLAV system needs to comply with the rules
for avoiding collision at sea (COLREGS). To retrieve the necessary information of the
surroundings, the COLAV system utilizes a number of information sources. This may include
exteroceptive sensors such as radar and cameras, or communication-based solutions
such as the automatic identification system (AIS). In order to enable COLAV, the sensor
information needs to be included into the state estimation of the surrounding obstacles.
This is done by the use of a tracking system.
In this thesis, a COLAV system including a multi-target tracking system based on the Joint
Integrated Probabilistic Data Association Filter (JIPDAF) and two COLAV algorithms,
one based on the Velocity Obstacle (VO) and another method called Scenario-Based Model
Predictive Control (SBMPC), has been tested in a wide variety of ASV scenarios. The scenarios
are generated with a new method which challenges the COLAV system to a high
number of succeeding vessel interactions. To evaluate the COLAV system s scenario performance,
a number of evaluation metrics used to determine COLREGS compliance are
applied.
The COLAV system have been implemented on a Platform Supply Vessel (PSV) and the
testing has taken place in a simulated environment. The results from the testing show that
when exposed to a small amount of noise, the tracking system delivers accurate obstacle
estimates to the COLAV algorithm, resulting in good evaluation scores. In more challenging
scenarios which includes considerably more noise, the tracking system delivers more
fluctuating obstacle estimates and a high number of false tracks, resulting in poor performance
of the SBMPC algorithm, while the VO algorithm performs remarkably well. | |
