基于转向圆型控制障碍函数的水面航行器多模态轨迹规划
Multimodal Trajectory Planning for Surface Vehicles using Turning Circle-based Control Barrier Functions
- Kongju National University（公州国立大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文针对动态环境下自主水面航行器，提出结合MPC与TC-CBF的无引导路径多模态轨迹规划框架，可提升避碰成功率、减少安全违规并缓解局部极小问题。
中文摘要 AI 辅助
本文提出了一种适用于动态环境中自主水面航行器的无引导路径多模态轨迹规划框架。该方法将模型预测控制（MPC）与基于转向圆的控制障碍函数（TC-CBF）相结合。与仅基于邻近度评估安全性的传统欧氏距离型控制障碍函数（ED-CBF）不同，TC-CBF考虑了水面航行器的非完整运动特性和有限转向能力，其几何公式根据航行器的转向圆确定可行避碰区域，并生成不同的左转和右转避碰模式。这些模式使优化求解器能够探索和选择拓扑结构不同的轨迹，而无需依赖许多传统多模态规划方法所需的全局规划引导路径。通过将避碰方向直接嵌入安全约束中，所提框架缓解了单模态MPC的局部极小值和死锁问题，同时保持了计算效率。涉及多艘移动船舶的大量仿真实验表明，与所有测试交通密度下的单模态基准方法相比，所提方法实现了更高的成功率、更少的安全违规和更小的残余违规。
英文摘要
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.
