Human-Robot Collaboration (HRC) requires strict adherence to safety standards, such as ISO 10218, to prevent harmful interactions. Standard Speed and Separation Monitoring (SSM) filters calculate safe robotic speeds based on conservative assumptions, such as constant human velocity, which prevents accurate predictions of minimum separation distances and causes unnecessary operational halts. This paper proposes a Control Barrier Function (CBF) that explicitly incorporates human acceleration data to analytically forward-predict the minimum human-robot separation distance during a worst-case robotic stopping trajectory. To guarantee safety at the control level, this predictive CBF is integrated as an inequality constraint within a Sequential Quadratic Programming (SQP) framework. Specifically, two methods are proposed: Method I, a CBF-constrained PD safety filter; and Method II, a task-scaling SQP controller that enforces a spatial tube constraint. Simulated and real-world experiments on a UR10e robot evaluate the two proposed methods against a standard industrial SSM module baseline. Results demonstrate that Method II dynamically modulates execution speed and confines spatial deviations. Compared to Method I, Method II achieves a 63\% reduction in mean trajectory error and avoids excessive evasive manoeuvres, ensuring high task throughput while complying with ISO 10218 SSM guidelines.

翻译：人机协作（HRC）要求严格遵守安全标准（如ISO 10218），以防止有害交互。标准速度与分离监控（SSM）滤波器基于保守假设（如恒定人体速度）计算安全机器人速度，这阻碍了对最小分离距离的精确预测，并导致不必要的操作中断。本文提出一种控制障碍函数（CBF），显式融入人体加速度数据，以在机器人最坏情况制动轨迹中解析式地前向预测最小人机分离距离。为确保控制层面的安全，该预测性CBF作为不等式约束被集成到序列二次规划（SQP）框架中。具体提出两种方法：方法一为CBF约束的PD安全滤波器；方法二为施加空间管状约束的任务缩放SQP控制器。在UR10e机器人上进行的仿真与真实世界实验，将两种方法与标准工业SSM模块基线进行对比评估。结果表明，方法二动态调节执行速度并限制空间偏差。与方法一相比，方法二使平均轨迹误差降低63%，并避免过度规避机动，在遵守ISO 10218 SSM准则的同时确保高任务吞吐量。