机器人机械臂的反应式模型预测轮廓控制
Reactive Model Predictive Contouring Control for Robot Manipulators
AI总结：
本文提出反应式模型预测轮廓控制（RMPCC）框架，通过路径参数化和控制障碍函数在100 Hz下实现机器人避障、避奇异点与自碰撞，求解速度较现有方法快10倍。
摘要：
本文提出了一种通过反应式模型预测轮廓控制（RMPCC）的机器人路径跟踪框架，能够在100 Hz的动态环境中成功避开障碍物、奇异点和自碰撞。许多路径跟踪方法依赖时间参数化，但在遵守运动学限制或其他约束条件时，难以处理碰撞和奇异点避让。具体而言，在执行规避动作时，期望路径与实际位置之间的误差可能会变大。因此，本文推导出一种方法，通过路径参数对参考路径进行参数化，并通过RMPCC执行优化。特别地，引入了控制障碍函数（CBFs）以在动态环境中避免碰撞和奇异点。基于雅可比的线性化和Gauss-Newton Hessian近似使得能够以100 Hz求解非线性RMPCC问题，比现有最优方法快10倍。实验证实，该框架在真实环境设置中能够处理动态障碍物，且具有低轮廓误差和低机器人加速度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
