# The Branching-Course MPC Algorithm for Maritime Collision Avoidance

## TL;DR

This paper introduces the BC-MPC algorithm for maritime collision avoidance, which is robust to sensor noise, does not require vessel communication, and complies with COLREGs, validated through full-scale experiments.

## Contribution

The paper presents a novel branching-course MPC algorithm that enhances maritime collision avoidance by being sensor-noise robust and COLREGs-compliant, validated in real-world experiments.

## Key findings

- Good collision avoidance performance in experiments
- Robustness to sensor noise demonstrated
- Compliance with COLREGs achieved

## Abstract

This article presents a new algorithm for short-term maritime collision avoidance (COLAV) named the branching-course MPC (BC-MPC) algorithm. The algorithm is designed to be robust with respect to noise on obstacle estimates, which is a significant source of disturbance when using exteroceptive sensors such as e.g. radars for obstacle detection and tracking. Exteroceptive sensors do not require vessel-to-vessel communication, which enables COLAV toward vessels not equipped with e.g. automatic identification system (AIS) transponders, in addition to increasing the robustness with respect to faulty information which may be provided by other vessels. The BC-MPC algorithm is compliant with rules 8 and 17 of the International Regulations for Preventing Collisions at Sea (COLREGs), and favors maneuvers following rules 13-15. This results in a COLREGs-aware algorithm which can ignore rules 13-15 when necessary. The algorithm is experimentally validated in several full-scale experiments in the Trondheimsfjord in 2017 using a radar-based system for obstacle detection and tracking. The COLAV experiments show good performance in compliance with the desired algorithm behavior.