用转向圈模型提升无人船避障能力，无需预设路径
Multimodal Trajectory Planning for Surface Vehicles using Turning Circle-based Control Barrier Functions
- 基于转向圈设计安全约束，考虑船只转向极限
- 多模式避障成功率超单模式基线，安全违规更少
- 适合复杂动态水域，兼顾效率与避障多样性
本文提出一种无需引导路径的多模态轨迹规划框架，用于自主水面车辆在动态环境中的运行。该方法将模型预测控制（MPC）与基于转向圈的控制屏障函数（TC-CBF）相结合。与仅依赖欧氏距离的常规控制屏障函数（ED-CBF）不同，TC-CBF 考虑了水面车辆的非完整运动特性和有限转向能力，其几何形式根据车辆转向圈识别可行避让区域，并生成左右转向两种避让模式。这些模式使优化求解器能在不依赖全局规划引导路径的情况下，探索并选择拓扑不同的轨迹。通过将避让方向直接嵌入安全约束，该框架缓解了单模式MPC的局部极小和死锁问题，同时保持计算效率。大量涉及多艘移动船只的仿真表明，所提方法在所有测试交通密度下均实现更高成功率、更少安全违规和更小残余违规。
原文摘要 · Abstract (English)
This paper presents a guide path-free multimodal trajectory planning framework for autonomous surface vehicles operating in dynamic environments. The proposed method integrates model predictive control (MPC) with a turning circle-based control barrier function (TC-CBF). Unlike conventional Euclidean distance-based CBFs (ED-CBFs), which evaluate safety solely based on proximity, the TC-CBF accounts for the nonholonomic motion and finite turning capability of a surface vehicle. Its geometric formulation identifies feasible avoidance regions according to the vehicle's turning circles and generates distinct left- and right-turning avoidance modes. These modes allow the optimization solver to explore and select topologically different trajectories without relying on globally planned guide paths, as required by many conventional multimodal planning approaches. By embedding the avoidance direction directly into the safety constraint, the proposed framework alleviates the local-minimum and deadlock problems of single-mode MPC while maintaining computational efficiency. Extensive simulations involving multiple moving vessels demonstrate that the proposed method achieves higher success rates, fewer safety violations, and smaller residual violations than single-mode baselines across all tested traffic densities.
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