One of the most important aspects of autonomous systems is safety. This includes ensuring safe human-robot and safe robot-environment interaction when autonomously performing complex tasks or in collaborative scenarios. Although several methods have been introduced to tackle this, most are unsuitable for real-time applications and require carefully hand-crafted obstacle descriptions. In this work, we propose a method combining high-frequency and real-time self and environment collision avoidance of a robotic manipulator with low-frequency, multimodal, and high-resolution environmental perceptions accumulated in a digital twin system. Our method is based on geometric primitives, so-called primitive skeletons. These, in turn, are information-compressed and real-time compatible digital representations of the robot's body and environment, automatically generated from ultra-realistic virtual replicas of the real world provided by the digital twin. Our approach is a key enabler for closing the loop between environment perception and robot control by providing the millisecond real-time control stage with a current and accurate world description, empowering it to react to environmental changes. We evaluate our whole-body collision avoidance on a 9-DOFs robot system through five experiments, demonstrating the functionality and efficiency of our framework.

翻译：自主系统安全性的核心要素之一是保障安全，包括在自主执行复杂任务或协作场景中确保人机交互与机器人-环境交互的安全性。尽管已有多种方法被提出用于解决该问题，但大多数方法不适用于实时应用，且需要人工精心构建障碍物描述。本文提出一种结合高频实时机器人本体与环境碰撞规避与低频、多模态、高分辨率环境感知的方法，后者通过数字孪生系统累积。该方法基于几何基元，即所谓的基本骨架。这些骨架是机器人本体与环境的压缩信息、实时兼容的数字表征，可自动从数字孪生提供的超逼真虚拟环境中生成。该方法通过向毫秒级实时控制阶段提供当前且准确的世界描述，使其能够响应环境变化，从而成为连接环境感知与机器人控制闭环的关键技术。我们在一个9自由度机器人系统上通过五项实验评估了全身避碰效果，验证了该框架的功能性与高效性。