Lecture Notes in Control and Information Sciences, Springer Nature
巻, 号, ページ
pp. 407-441
出版年月
2025年
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Springer
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アブストラクト
In this chapter, we investigate autonomous vessel operation in or near ports and propose a novel hierarchical control architecture that combines control barrier function (CBF)-based online optimization, model predictive control (MPC), and a rapidly exploring random tree (RRT)-like spatiotemporal path generator (StPG). Our controller consists of three layers: a vessel-friendly path planner, a safe trajectory generator, and low-level safe control. Since the optimal vessel operation varies depending on the vessel’s location, our controller switches control modes accordingly. Specifically, we divide operations into the approaching phase, breakwater-passing phase, and docking phase. Before entering the port area, we employ the StPG to find safe paths, avoiding dynamically evolving hazardous areas congested with moving obstacles detected by the Automatic Identification System. In the subsequent operational phases, we utilize MPC-CBF-based online optimization with a vessel dynamics model to ensure safety. The safe trajectory generator, designed based on MPC, smooths collision avoidance behavior and adheres to various legal specifications. CBF-based optimization in low-level safe control ensures safety even in the presence of prediction errors in the safe trajectory generator. The present control architecture is demonstrated via various simulations, including the one with real data of vessels in Tokyo Bay.
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