予測モデリングと強化学習による自律航行船のデジタルツイン：安全な海上航行に向けて
Digital Twin of Autonomous Surface Vessels for Safe Maritime Navigation Enabled through Predictive Modeling and Reinforcement Learning
自律航行船のデジタルツインに目標追跡と非線形モデル予測制御による安全フィルタを組み込み、予測・処方・自律機能の基盤をUnity上で実証した研究。
著者: Daniel Menges, Andreas Von Brandis, Adil Rasheed
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Autonomous surface vessels (ASVs) play an increasingly important role in the safety and sustainability of open sea operations. Since most maritime accidents are related to human failure, intelligent algorithms for autonomous collision avoidance and path following can drastically reduce the risk in the maritime sector. A DT is a virtual representative of a real physical system and can enhance the situational awareness (SITAW) of such an ASV to generate optimal decisions. This work builds on an existing DT framework for ASVs and demonstrates foundations for enabling predictive, prescriptive, and autonomous capabilities. In this context, sophisticated target tracking approaches are crucial for estimating and predicting the position and motion of other dynamic objects. The applied tracking method is enabled by real-time automatic identification system (AIS) data and synthetic light detection and ranging (Lidar) measurements. To guarantee safety during autonomous operations, we applied a predictive safety filter, based on the concept of nonlinear model predictive control (NMPC). The approaches are implemented into a DT built with the Unity game engine. As a result, this work demonstrates the potential of a DT capable of making predictions, playing through various what-if scenarios, and providing optimal control decisions according to its enhanced SITAW.
