用自适应障碍物模型实现无人船实时安全导航
Safe Motion Planning and Control Using Predictive and Adaptive Barrier Methods for Autonomous Surface Vessels
- 基于MPC与高阶控制屏障函数的动态安全规划
- 自适应椭圆膨胀半径降低保守性，提升通过率
- 实测验证在狭窄水域可避死锁且实时响应
无人船在复杂环境（如狭窄内河航道）中的安全路径规划至关重要。传统方法往往计算量大或过于保守。本文提出一种结合模型预测控制（MPC）与控制屏障函数（CBFs）的安全规划策略，引入随船-障碍相对位置和姿态动态调整的时变膨胀椭圆障碍物表示。该自适应膨胀机制有效降低了控制器的保守性，优于传统固定椭球模型。MPC提供近似运动规划，高阶CBFs利用变化的膨胀半径保障航行安全。仿真与真实实验表明，该策略使全驱动无人船可在实时条件下穿越狭窄空间，有效解决潜在死锁问题，同时确保安全性。
原文摘要 · Abstract (English)
Safe motion planning is essential for autonomous vessel operations, especially in challenging spaces such as narrow inland waterways. However, conventional motion planning approaches are often computationally intensive or overly conservative. This paper proposes a safe motion planning strategy combining Model Predictive Control (MPC) and Control Barrier Functions (CBFs). We introduce a time-varying inflated ellipse obstacle representation, where the inflation radius is adjusted depending on the relative position and attitude between the vessel and the obstacle. The proposed adaptive inflation reduces the conservativeness of the controller compared to traditional fixed-ellipsoid obstacle formulations. The MPC solution provides an approximate motion plan, and high-order CBFs ensure the vessel's safety using the varying inflation radius. Simulation and real-world experiments demonstrate that the proposed strategy enables the fully-actuated autonomous robot vessel to navigate through narrow spaces in real time and resolve potential deadlocks, all while ensuring safety.
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