Abstract
This contribution presents a robot path-following framework via Reactive Model Predictive Contouring Control (RMPCC) that successfully avoids obstacles, singularities and self-collisions in dynamic environments at 100 Hz. Many path-following methods rely on the time parametrization, but struggle to handle collision and singularity avoidance while adhering kinematic limits or other constraints. Specifically, the error between the desired path and the actual position can become large when executing evasive maneuvers. Thus, this paper derives a method that parametrizes the reference path by a path parameter and performs the optimization via RMPCC. In particular, Control Barrier Functions (CBFs) are introduced to avoid collisions and singularities in dynamic environments. A Jacobian-based linearization and Gauss-Newton Hessian approximation enable solving the nonlinear RMPCC problem at 100 Hz, outperforming state-of-the-art methods by a factor of 10. Experiments confirm that the framework handles dynamic obstacles in real-world settings with low contouring error and low robot acceleration.
Abstract (translated)
这篇贡献介绍了一种基于反应式模型预测轮廓控制（Reactive Model Predictive Contouring Control，RMPCC）的机器人路径跟踪框架，能够在动态环境中以100 Hz的速度成功地避开障碍物、奇点和自我碰撞。许多路径跟踪方法依赖于时间参数化，但难以在遵守运动学限制或其他约束条件的同时处理碰撞及奇异点避免问题。特别地，在执行避让操作时，期望路径与实际位置之间的误差可能会变得非常大。因此，本文提出了一种通过路径参数对参考路径进行参数化的办法，并采用RMPCC进行优化的方法。具体来说，引入了控制障碍函数（Control Barrier Functions，CBFs）以避免动态环境中的碰撞和奇异点问题。基于雅可比矩阵的线性化以及高斯-牛顿海森矩阵近似方法使得非线性的RMPCC问题能够在100 Hz的速度下解决，并且其性能优于现有技术水平的方法十倍以上。实验结果证实，该框架在现实世界的应用中能够处理动态障碍物，具有低轮廓误差和低机器人加速度的特点。
URL
https://arxiv.org/abs/2508.09502
