用分层控制屏障函数实现人机近距离互动中的主动安全优先级管理
Proactive Hierarchical Control Barrier Function-Based Safety Prioritization in Close Human-Robot Interaction Scenarios
- 基于控制屏障函数设计分层框架，动态调整安全约束优先级
- 引入松弛变量实现实时风险响应，关键部位碰撞威胁优先处理
- 适用于高动态人机协作场景，适合工业机器人安全控制研发
在人机协同环境中，人类运动的不可预测性和动态性可能导致碰撞不可避免。为此，机器人系统需通过智能控制策略主动减轻潜在伤害。本文提出一种基于控制屏障函数（CBFs）的分层控制框架，确保自主机器人操作臂在近距离人机交互中安全、自适应运行。该方法引入松弛变量，实现安全约束的实时优先级调度，使机器人可根据人体不同部位的危险程度动态管理碰撞风险。同时引入二级约束机制，在约束不可行时提升紧迫威胁的优先级。该框架在配备ZED2i AI摄像头的Franka Research 3机器人上进行实验验证，利用深度感知实现人体姿态与位置的实时检测。实验结果表明，集成深度传感的CBF控制器能有效支持响应迅速且安全的人机协作，并在高度动态环境下提供详细的風險分析与鲁棒性能。
原文摘要 · Abstract (English)
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.
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