船舶の衝突回避とミッション計画における最近の機械学習ソリューションの調査
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
船舶の衝突回避とミッション計画に用いられる最近の機械学習手法を調査し、比較・課題・将来展望をまとめたサーベイ論文。
著者: Pouria Sarhadi, Wasif Naeem, Nikolaos Athanasopoulos
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.
