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In this chapter, we investigate autonomous vesselVessel operation in or near ports and propose a novel hierarchical control architectureArchitecture that combines control barrierControl barrier function (CBFControl Barrier Function (CBF))-based online optimization, model predictive control (MPC)Model Predictive Control (MPC), and a rapidly exploring random tree (RRT)-like spatiotemporal pathPath generator (StPG). Our controllerController consists of three layers: a vessel-friendly pathPath planner, a safeSafe trajectoryTrajectory generator, and low-level safeSafe control. Since the optimalOptimal vesselVessel operation varies depending on the vessel’s location, our controller switches control modes accordingly. Specifically, we divide operationsOperations into the approaching phase, breakwater-passing phase, and docking phase. Before entering the port area, we employ the StPG to find safeSafe paths, avoiding dynamically evolving hazardous areas congested with moving obstacles detected by the Automatic IdentificationIdentifcation System. In the subsequent operational phases, we utilize MPC-CBF-based online optimizationOptimization with a vesselVessel dynamics model to ensure safetySafety. The safeSafe trajectoryTrajectory generator, designed based on MPC, smooths collisionCollision avoidanceAvoidance behaviorBehavior and adheres to various legal specifications. CBFControl Barrier Function (CBF)-based optimizationOptimization in low-level safeSafe control ensures safetySafety even in the presence of prediction errors in the safeSafe trajectoryTrajectory generator. The present control architectureArchitecture is demonstrated via various simulations, including the one with real data of vessels in Tokyo Bay.

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Hierarchical Vessel   Safe  Operation in a Port Through CBF , MPC and RRT-like Spatiotemporal Path  Planning

  • 章节

  • 发表日期：

    2025年

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  • Satoshi Otsuki,
  • Naoki Hatta,
  • Muhammad Hanif,
  • Riku Funada,
  • Kenichi Nakashima,
  • Takeshi Hatanaka

摘要

In this chapter, we investigate autonomous vesselVessel operation in or near ports and propose a novel hierarchical control architectureArchitecture that combines control barrierControl barrier function (CBFControl Barrier Function (CBF))-based online optimization, model predictive control (MPC)Model Predictive Control (MPC), and a rapidly exploring random tree (RRT)-like spatiotemporal pathPath generator (StPG). Our controllerController consists of three layers: a vessel-friendly pathPath planner, a safeSafe trajectoryTrajectory generator, and low-level safeSafe control. Since the optimalOptimal vesselVessel operation varies depending on the vessel’s location, our controller switches control modes accordingly. Specifically, we divide operationsOperations into the approaching phase, breakwater-passing phase, and docking phase. Before entering the port area, we employ the StPG to find safeSafe paths, avoiding dynamically evolving hazardous areas congested with moving obstacles detected by the Automatic IdentificationIdentifcation System. In the subsequent operational phases, we utilize MPC-CBF-based online optimizationOptimization with a vesselVessel dynamics model to ensure safetySafety. The safeSafe trajectoryTrajectory generator, designed based on MPC, smooths collisionCollision avoidanceAvoidance behaviorBehavior and adheres to various legal specifications. CBFControl Barrier Function (CBF)-based optimizationOptimization in low-level safeSafe control ensures safetySafety even in the presence of prediction errors in the safeSafe trajectoryTrajectory generator. The present control architectureArchitecture is demonstrated via various simulations, including the one with real data of vessels in Tokyo Bay.