Abstract
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.
Abstract (translated)
在未知且复杂的环境中实现安全导航依然是机器人技术面临的一大挑战。模型预测轮廓控制（Model Predictive Contour Control，MPCC）因其能够使轨迹跟踪更加精确和灵活而显示出良好的障碍物避让性能，但现有的方法缺乏正式的安全保证措施。为解决这一问题，我们提出了一种通用的基于控制Lyapunov函数（CLF）和控制屏障函数（CBF）增强型的MPCC框架，该框架通过围绕规划轨迹形成的无障碍空间走廊来执行安全约束条件。为了提高可行性，我们在运行时动态调整CBF参数，并采用软演员评论家（SAC）策略进行优化。我们通过广泛的模拟测试以及在未知复杂环境中的移动机器人导航实验验证了这一方法的有效性。
URL
https://arxiv.org/abs/2507.14700
