为移动机器人在多人环境中的安全评估提供新框架
FRESHR-GSI: A Generalized Safety Model and Evaluation Framework for Mobile Robots in Multi-Human Environments
- 提出以机器人为中心的多维度安全评估框架
- 实测显示该框架能实时生成精准安全指数
- 适合需高安全性的服务型机器人研发
人类安全在人机近距离交互中至关重要，是人机物理兼容的核心。现有安全度量主要针对工业机械臂场景，对移动机器人与多人共享空间的场景关注不足。本文提出一种以机器人为中心的方向性安全框架，适用于多人环境中的移动机器人评估。该框架融合相对距离、速度和朝向等关键指标，兼具灵活性与视角可转换性。通过集成基于RGB-D视觉与深度学习的人体检测流程，构建了通用安全指数（GSI），可实时评估人类安全状态。在真实场景实验中验证了其生成合理、鲁棒且细粒度安全度量的能力，并与现有模型对比，表现更优。
原文摘要 · Abstract (English)
Human safety is critical in applications involving close human-robot interactions (HRI) and is a key aspect of physical compatibility between humans and robots. While measures of human safety in HRI exist, these mainly target industrial settings involving robotic manipulators. Less attention has been paid to settings where mobile robots and humans share the space. This paper introduces a new robot-centered directional framework of human safety. It is particularly useful for evaluating mobile robots as they operate in environments populated by multiple humans. The framework integrates several key metrics, such as each human's relative distance, speed, and orientation. The core novelty lies in the framework's flexibility to accommodate different application requirements while allowing for both the robot-centered and external observer points of view. We instantiate the framework by using RGB-D based vision integrated with a deep learning-based human detection pipeline to yield a generalized safety index (GSI) that instantaneously assesses human safety. We evaluate GSI's capability of producing appropriate, robust, and fine-grained safety measures in real-world experimental scenarios and compare its performance with extant safety models.
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