用动态调整的约束函数，让机器人在未知环境中安全避障。
Corridor-based Adaptive Control Barrier and Lyapunov Functions for Safe Mobile Robot Navigation
- 基于轨迹周围空域走廊设计安全约束
- 通过强化学习实时优化约束参数，提升可行性
- 适用于需要高安全性移动机器人的场景
在未知且杂乱的环境中实现安全导航仍是机器人领域的挑战。模型预测轮廓控制（MPCC）通过精确灵活的轨迹跟踪展现出良好的避障性能，但现有方法缺乏形式化安全保证。为此，我们提出一种基于控制李雅普诺夫函数（CLF）和控制屏障函数（CBF）的MPCC框架，通过规划轨迹周围的自由空间走廊来施加安全约束。为提高可行性，采用软演员-评论家（SAC）策略在运行时动态调整CBF参数。该方法在大量仿真及实际移动机器人导航实验中得到验证。
原文摘要 · Abstract (English)
Safe navigation in unknown and cluttered environments remains a challenging problem in robotics. Model Predictive Contour Control (MPCC) has shown promise for performant obstacle avoidance by enabling precise and agile trajectory tracking, however, existing methods lack formal safety assurances. To address this issue, we propose a general Control Lyapunov Function (CLF) and Control Barrier Function (CBF) enabled MPCC framework that enforces safety constraints derived from a free-space corridor around the planned trajectory. To enhance feasibility, we dynamically adapt the CBF parameters at runtime using a Soft Actor-Critic (SAC) policy. The approach is validated with extensive simulations and an experiment on mobile robot navigation in unknown cluttered environments.
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