  In this paper, we look at the role of autonomous navigation in the maritime
domain. Specifically, we examine how an Autonomous Surface Vessel(ASV) can
achieve obstacle avoidance based on the Convention on the International
Regulations for Preventing Collisions at Sea (1972), or COLREGs, in real-world
environments. Our ASV is equipped with a broadband marine radar, an Inertial
Navigation System (INS), and uses official Electronic Navigational Charts
(ENC). These sensors are used to provide situational awareness and, in series
of well-defined steps, we can exclude land objects from the radar data, extract
tracks associated with moving vessels within range of the radar, and then use a
Kalman Filter to track and predict the motion of other moving vessels in the
vicinity. A Constant Velocity model for the Kalman Filter allows us to solve
the data association to build a consistent model between successive radar
scans. We account for multiple COLREGs situations based on the predicted
relative motion. Finally, an efficient path planning algorithm is presented to
find a path and publish waypoints to perform real-time COLREGs compliant
autonomous navigation within highly constrained environments. We demonstrate
the results of our framework with operational results collected over the course
of a 3.4 nautical mile mission on the Charles River in Boston in which the ASV
encountered and successfully navigated multiple scenarios and encounters with
other moving vessels at close quarters.

Robotics