Proactive Hierarchical Control Barrier Function-Based Safety Prioritization in Close Human-Robot Interaction Scenarios

TL;DR

This paper introduces a hierarchical control framework using Control Barrier Functions to enhance safety in close human-robot interactions by dynamically prioritizing safety constraints and managing collision risks in real-time.

Contribution

It proposes a novel CBF-based control approach with a relaxation variable for safety prioritization and a secondary constraint mechanism to handle infeasibility, validated on a real robot system.

Findings

The framework enables real-time safety prioritization during close human-robot interactions.

Experimental validation shows responsive and safe collaboration with dynamic human movements.

The system maintains robust performance and detailed risk analysis in complex scenarios.

Abstract

In collaborative human-robot environments, the unpredictable and dynamic nature of human motion can lead to situations where collisions become unavoidable. In such cases, it is essential for the robotic system to proactively mitigate potential harm through intelligent control strategies. This paper presents a hierarchical control framework based on Control Barrier Functions (CBFs) designed to ensure safe and adaptive operation of autonomous robotic manipulators during close-proximity human-robot interaction. The proposed method introduces a relaxation variable that enables real-time prioritization of safety constraints, allowing the robot to dynamically manage collision risks based on the criticality of different parts of the human body. A secondary constraint mechanism is incorporated to resolve infeasibility by increasing the priority of imminent threats. The framework is experimentally validated on a Franka Research 3 robot equipped with a ZED2i AI camera for real-time human pose and body detection. Experimental results confirm that the CBF-based controller, integrated with depth sensing, facilitates responsive and safe human-robot collaboration, while providing detailed risk analysis and maintaining robust performance in highly dynamic settings.