Embedding ISO 10218 Safety Compliance in Robots via Control Barrier Functions for Human-Robot Collaboration

2026-06-11

AI Summary

This work addresses the limitations of traditional Safety Speed Models (SSMs), which assume constant human velocity and often yield inaccurate predictions of minimum human–robot separation, leading to unnecessary stops. To overcome this, the authors propose a novel safety controller that, for the first time, incorporates human acceleration into Control Barrier Functions (CBFs). By analytically computing the minimum separation distance under worst-case braking scenarios, the method embeds this distance as an inequality constraint within a Sequential Quadratic Programming (SQP) framework, enabling task-scaling safety guarantees. Validated on a UR10e platform with a PD safety filter and spatial tube constraints, the approach reduces average trajectory error by 63% compared to baseline methods while significantly mitigating over-conservative avoidance behaviors—all within compliance with ISO 10218 standards—thereby enhancing collaborative efficiency.

Abstract

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.

Problem

• Human-Robot Collaboration
• ISO 10218
• Speed and Separation Monitoring
• Safety Compliance
• Minimum Separation Distance

Innovation

• Control Barrier Function
• ISO 10218
• Human-Robot Collaboration
• Sequential Quadratic Programming
• Speed and Separation Monitoring