用鲁棒控制提升内河船舶自主导航安全性
Safe Robust Predictive Control-based Motion Planning of Automated Surface Vessels in Inland Waterways
- 结合鲁棒模型预测控制与安全屏障函数设计
- 仿真验证在复杂水道中实现安全避障
- 适合需高安全性的内河智能船舶应用
在内河航道部署自动驾驶水面船可有效缓解道路拥堵与碳排放。然而，狭窄航道、高密度通航及水动力干扰带来独特挑战，现有自主导航方法往往缺乏足够鲁棒性或精度。本文提出一种基于鲁棒模型预测控制（RMPC）与控制屏障函数（CBFs）的运动规划新方法，将航道边界和障碍物作为控制设计中的安全约束，确保复杂水道环境下既不碰撞又稳定航行。仿真结果表明，该方法在真实场景下能安全引导自动化水面船（ASVs），相比现有最优方法显著提升安全性和适应性。
原文摘要 · Abstract (English)
Deploying self-navigating surface vessels in inland waterways offers a sustainable alternative to reduce road traffic congestion and emissions. However, navigating confined waterways presents unique challenges, including narrow channels, higher traffic density, and hydrodynamic disturbances. Existing methods for autonomous vessel navigation often lack the robustness or precision required for such environments. This paper presents a new motion planning approach for Automated Surface Vessels (ASVs) using Robust Model Predictive Control (RMPC) combined with Control Barrier Functions (CBFs). By incorporating channel borders and obstacles as safety constraints within the control design framework, the proposed method ensures both collision avoidance and robust navigation on complex waterways. Simulation results demonstrate the efficacy of the proposed method in safely guiding ASVs under realistic conditions, highlighting its improved safety and adaptability compared to the state-of-the-art.
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