让轮式机器人沿路径走不偏航，还避障且全局稳定。
A Safe Hybrid Control Framework for Car-like Robot with Guaranteed Global Path-Invariance using a Control Barrier Function
- 分两阶段控制：局部保证路径不偏离，全局实现任意起始点到达路径。
- 用控制屏障函数避免奇点问题，确保路径不变性。
- 适合需要高安全性的自主导航场景，如自动驾驶或服务机器人。
本文提出一种针对轮式机器人的混合控制框架，实现避障、全局收敛与安全，其中安全定义为路径不变性——一旦机器人到达路径，便不再离开。给定一条预先规划的无障碍可行路径（周围可能存在障碍物），目标是避障并抵达路径后持续沿路径运行。方法分为两步：首先定义路径附近的“紧致”无障碍邻域，设计局部控制器确保路径收敛与路径不变性，并引入控制屏障函数避免奇点处控制失效；其次构建混合控制框架，将该局部路径不变控制器与任意现有全局跟踪控制器融合，无需路径不变性保证，仍能确保从任意初始位置收敛至目标路径，实现全局收敛。该框架保证路径不变性与对传感器噪声的鲁棒性。详细仿真验证了方案有效性。
原文摘要 · Abstract (English)
This work proposes a hybrid framework for car-like robots with obstacle avoidance, global convergence, and safety, where safety is interpreted as path invariance, namely, once the robot converges to the path, it never leaves the path. Given a priori obstacle-free feasible path where obstacles can be around the path, the task is to avoid obstacles while reaching the path and then staying on the path without leaving it. The problem is solved in two stages. Firstly, we define a ``tight'' obstacle-free neighborhood along the path and design a local controller to ensure convergence to the path and path invariance. The control barrier function technology is involved in the control design to steer the system away from its singularity points, where the local path invariant controller is not defined. Secondly, we design a hybrid control framework that integrates this local path-invariant controller with any global tracking controller from the existing literature without path invariance guarantee, ensuring convergence from any position to the desired path, namely, global convergence. This framework guarantees path invariance and robustness to sensor noise. Detailed simulation results affirm the effectiveness of the proposed scheme.
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