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：自主系统最重要的方面之一是安全性。这包括在自主执行复杂任务或协作场景中，确保安全的人机交互和机-环境交互。尽管已有多种方法被提出用于解决这一问题，但大多数方法不适用于实时应用，且需要精心手工构建的障碍物描述。本文提出一种方法，将机械臂的高频实时自身与环境避碰，与低频、多模态、高分辨率的环境感知（累积于数字孪生系统中）相结合。该方法基于几何基元，即所谓的基元骨架。这些基元是机器人身体与环境的压缩信息、实时兼容的数字表示，由数字孪生提供的超真实虚拟副本自动生成。通过为毫秒级实时控制阶段提供当前且准确的世界描述，使控制阶段能够响应环境变化，本方法成为环境感知与机器人控制之间闭环的关键推动力。我们通过五个实验，在9自由度机器人系统上评估了全身避碰性能，验证了所提框架的功能与效率。