# Short-term ASV Collision Avoidance with Static and Moving Obstacles

## TL;DR

This paper presents an enhanced BC-MPC algorithm for autonomous surface vehicles that effectively avoids static and moving obstacles using an occupancy grid and radar tracking, validated through real-world experiments.

## Contribution

The authors extend the BC-MPC algorithm to include static obstacle avoidance and improve trajectory stability, verified by full-scale maritime experiments.

## Key findings

- Successfully avoided static and moving obstacles in real-world tests
- Generated clear, observable maneuvers complying with COLREGs
- Enhanced trajectory stability with reduced wobbling

## Abstract

This article considers collision avoidance (COLAV) for both static and moving obstacles using the branching-course model predictive control (BC-MPC) algorithm, which is designed for use by autonomous surface vehicles (ASVs). The BC-MPC algorithm originally only considered COLAV of moving obstacles, so in order to make the algorithm also be able to avoid static obstacles, we introduce an extra term in the objective function based on an occupancy grid. In addition, other improvements are made to the algorithm resulting in trajectories with less wobbling. The modified algorithm is verified through full-scale experiments in the Trondheimsfjord in Norway with both virtual static obstacles and a physical moving obstacle. A radar-based tracking system is used to detect and track the moving obstacle, which enables the algorithm to avoid obstacles without depending on vessel-to-vessel communication. The experiments show that the algorithm is able to simultaneously avoid both static and moving obstacles, while providing clear and readily observable maneuvers. The BC-MPC algorithm is compliant with rules 8, 13 and 17 of the the International Regulations for Preventing Collisions at Sea (COLREGs), and favors maneuvers following rules 14 and 15.