100Hz实时避障机械臂路径跟踪，精度高且不撞自身
Reactive Model Predictive Contouring Control for Robot Manipulators
- 用路径参数替代时间参数，优化路径跟踪
- 结合控制屏障函数，动态避障避奇异点
- 每秒100次计算，比现有方法快10倍，适合真实场景
本文提出一种基于反应式模型预测轮廓控制（RMPCC）的机器人路径跟踪框架，在100 Hz下可成功避免动态环境中的障碍物、奇异点和自碰撞。许多路径跟踪方法依赖时间参数化，但在满足运动学限制或其他约束的同时难以实现碰撞与奇异点规避，执行避让动作时实际位置与期望路径偏差较大。为此，本文采用路径参数化方法，并通过RMPCC进行优化。特别地，引入控制屏障函数（CBFs）以在动态环境中避免碰撞与奇异点。结合雅可比线性化与高斯-牛顿海森矩阵近似，可在100 Hz下求解非线性RMPCC问题，性能优于当前最先进方法达10倍。实验表明，该框架在真实环境中能有效处理动态障碍物，保持低轮廓误差和低机器人加速度。
原文摘要 · Abstract (English)
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.
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