COLREGs-Informed RRT* for Collision Avoidance of Marine Crafts
This addresses collision avoidance for autonomous marine vessels, though it appears incremental as it builds on existing RRT* algorithms with domain-specific modifications.
The paper tackled the problem of computing optimal collision avoidance paths for marine vessels by developing novel sampling strategies that encode maritime navigation rules and vessel steering constraints, resulting in significant improvements in path optimality, computational speed, and convergence rate.
The paper proposes novel sampling strategies to compute the optimal path alteration of a surface vessel sailing in close quarters. Such strategy directly encodes the rules for safe navigation at sea, by exploiting the concept of minimal ship domain to determine the compliant region where the path deviation is to be generated. The sampling strategy is integrated within the optimal rapidly-exploring random tree algorithm, which minimizes the length of the path deviation. Further, the feasibility of the path with respect to the steering characteristics of own ship is verified by ensuring that the position of the new waypoints respects the minimum turning radius of the vessel. The proposed sampling strategy brings a significant performance improvement both in terms of optimal cost, computational speed and convergence rate.