# COLREGs-Informed RRT* for Collision Avoidance of Marine Crafts

arXiv: https://arxiv.org/abs/2103.14426 · HTML: https://mlsanity.com/paper/2103.14426/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [Spatial-temporal recurrent reinforcement learning for autonomous ships](https://mlsanity.com/paper/2211.01004/) (2022)
- [Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea](https://mlsanity.com/paper/2402.08502/) (2024)

## Abstract (arXiv)

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.