COLREGs-Informed RRT* for Collision Avoidance of Marine Crafts

TL;DR

This paper introduces a COLREGs-informed sampling strategy integrated into RRT* for marine collision avoidance, improving path optimality and computational efficiency while ensuring compliance with maritime navigation rules.

Contribution

It presents a novel sampling method that encodes maritime rules and vessel constraints within RRT*, enhancing collision avoidance planning for ships.

Findings

Significant improvement in path optimality and convergence rate.

Enhanced computational speed for real-time navigation.

Effective encoding of maritime rules into sampling strategy.

Abstract

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.