測距センサとモデルベース強化学習による港湾内自律航行
Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning
測距センサのみで環境を観測し、モデルベース強化学習を用いて動的障害物を含む複雑な港湾シナリオを自律航行する手法を提案し、既存手法を上回る性能を示した。
著者: Siemen Herremans, Ali Anwar, Arne Troch, Ian Ravijts, Maarten Vangeneugden, Siegfried Mercelis, Peter Hellinckx
分類: cs.RO, cs.AI
原文アブストラクト
Autonomous shipping has recently gained much interest in the research community. However, little research focuses on inland - and port navigation, even though this is identified by countries such as Belgium and the Netherlands as an essential step towards a sustainable future. These environments pose unique challenges, since they can contain dynamic obstacles that do not broadcast their location, such as small vessels, kayaks or buoys. Therefore, this research proposes a navigational algorithm which can navigate an inland vessel in a wide variety of complex port scenarios using ranging sensors to observe the environment. The proposed methodology is based on a machine learning approach that has recently set benchmark results in various domains: model-based reinforcement learning. By randomizing the port environments during training, the trained model can navigate in scenarios that it never encountered during training. Furthermore, results show that our approach outperforms the commonly used dynamic window approach and a benchmark model-free reinforcement learning algorithm. This work is therefore a significant step towards vessels that can navigate autonomously in complex port scenarios.
