Motion Attribute-based Clustering and Collision Avoidance of Multiple In-water Obstacles by ASV
Channel: Dartmouth Reality and Robotics
Date: 2022-10-05

Navigation and obstacle avoidance in aquatic environments for autonomous surface vehicles (ASVs) in high-traffic maritime scenarios is still an open challenge, as the Convention  on  the  International  Regulations  for Preventing  Collisions  at  Sea (COLREGs) is not defined for multi-encounter situations. Current state-of-the-art methods resolve single-to-single encounters with sequential actions and assume that other obstacles follow COLREGs. 
Our work proposes a novel real-time non-myopic obstacle avoidance method, allowing an ASV that has only partial knowledge of the surroundings within the sensor radius to navigate in high-traffic maritime scenarios. Specifically, we achieve a holistic view of the feasible ASV action space able to avoid deadlock scenarios, by proposing (1) a clustering method based on motion attributes of other obstacles, (2) a geometric framework for identifying the feasible action space, and (3) a multi-objective optimization to determine the best action. 
Theoretical analysis and extensive realistic experiments in simulation considering real-world traffic scenarios demonstrate that our proposed real-time obstacle avoidance method is able to achieve safer trajectories than other state-of-the-art methods and that is robust to uncertainty present in the current information available to the ASV.