AIS-Based Collision Avoidance in MOOS-IvP using a Geodetic Unscented Kalman Filter

This paper describes the design and implementation of a low-cost collision avoidance system, designed primarily for use on small and medium-sized autonomous surface vehicles (ASVs). The proposed methodology leverages real-time information broadcast via the Automatic Information System (AIS) messaging protocol, in order to estimate the position, speed, and heading of nearby vessels. The state of each target vessel is recursively estimated in geodetic coordinates using an Unscented Kalman Filter (UKF). Once identified, each vessel is avoided in accordance with the International Regulations for Preventing Collisions at Sea (COLREGs). This capability is enabled by MOOS-IvP, a behavior-based autonomy middleware that is able to make navigation decisions by weighing the relative importance of multiple competing objectives. For the purposes of collision avoidance, each target vessel produces a two-dimensional objective function which increases the cost of heading and speed combinations that will result in a collision or near-miss event. However, the primary mission behaviors remain active, allowing the IvP solver to choose an optimal combination of vessel speed and heading which drive the vehicle toward a desired state while simultaneously minimizing the risk of collision. It is shown through field testing that the proposed framework is an effective, robust means of collision avoidance.