Local Route Planning for Collision Avoidance of Maritime Autonomous Surface Ships in Compliance with COLREGs Rules

Ho Namgung

doi:10.3390/su14010198

Abstract

A maritime autonomous surface ship (MASS) ensures safety and effectiveness during navigation using its ability to prevent collisions with a nearby target ship (TS). This avoids the loss of human life and property. Therefore, collision avoidance of MASSs has been actively researched recently. However, previous studies did not consider all factors crucial to collision avoidance in compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) Rules 5, 7, 8, and 13–17. In this study, a local route-planning algorithm that takes collision-avoidance actions in compliance with COLREGs Rules using a fuzzy inference system based on near-collision (FIS-NC), ship domain (SD), and velocity obstacle (VO) is proposed. FIS-NC is used to infer the collision risk index (CRI) and determine the time point for collision avoidance. Following this, I extended the VO using the SD to secure the minimum safe distance between the MASS and the TS when they pass each other. Unlike previous methods, the proposed algorithm can be used to perform safe and efficient navigation in terms of near-collision accidents, inferred CRI, and deviation from the course angle route by taking collision-avoidance actions in compliance with COLREGs Rules 5, 7, 8, and 13–17.

Highlights

The Korea Maritime Safety Tribunal (KMST) has found that approximately 85% of marine accidents are caused by human error [1]
Two existing algorithms were selected for this purpose: the A* exploration-based local route-planning algorithm using the FIS [23] and the velocity obstacle (VO)-based local route-planning algorithm using fuzzy comprehension evaluation (VO + FCE) [6] for taking collision-avoidance actions at a suitable time point based on fuzzy logic
A local route-planning algorithm based on fuzzy inference system based on near-collision (FIS-NC), ship domain (SD), and VO was developed based on the situational awareness (SA) model

Summary

Introduction

The Korea Maritime Safety Tribunal (KMST) has found that approximately 85% of marine accidents are caused by human error [1]. This finding indicates that removing human involvement may be beneficial for maritime safety. One of the most critical features of a MASS is its ability to prevent collisions with nearby obstacles during navigation. This is because collisions can cause structural hazards, loss of human life and property, and ocean pollution due to oil and cargo spills.

Results

Discussion

Conclusion