面向自主水面船舶的基于预测与自适应屏障方法的安全运动规划与控制
Safe Motion Planning and Control Using Predictive and Adaptive Barrier Methods for Autonomous Surface Vessels
AI总结：
针对自主水面船舶传统运动规划方法计算量大或过于保守的问题，本文结合MPC与自适应膨胀椭圆障碍物表示的高阶CBFs，提出安全运动规划策略，经仿真与实船实验验证可在保障安全的同时实现狭窄空间实时通行与死锁解决。
摘要：
安全运动规划对自主船舶运行至关重要，尤其是在狭窄内河航道等复杂空间中。然而，传统运动规划方法通常计算量大或过于保守。本文提出一种结合模型预测控制（Model Predictive Control, MPC）与控制屏障函数（Control Barrier Functions, CBFs）的安全运动规划策略。我们引入一种时变膨胀椭圆障碍物表示方法，其膨胀半径根据船舶与障碍物之间的相对位置和姿态进行调整。与传统的固定椭圆障碍物公式相比，所提出的自适应膨胀方法降低了控制器的保守性。MPC的求解结果提供近似运动规划，高阶CBFs则利用变化的膨胀半径确保船舶安全。仿真与真实世界实验表明，该策略能使全驱动自主机器人船舶在确保安全的前提下，实时通过狭窄空间并解决潜在死锁问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
