Proactive Hierarchical Control Barrier Function-Based Safety Prioritization in Close Human-Robot Interaction Scenarios
For roboticists working on human-robot interaction, this work provides a method to proactively mitigate collision risks by prioritizing safety constraints, though it is an incremental improvement over existing CBF-based approaches.
This paper presents a hierarchical control framework using Control Barrier Functions (CBFs) to ensure safe human-robot interaction by dynamically prioritizing safety constraints based on human body part criticality. Experimental validation on a Franka Research 3 robot with a ZED2i camera shows responsive and safe collaboration in dynamic settings.
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.