Human safety has always been the main priority when working near an industrial robot. With the rise of Human-Robot Collaborative environments, physical barriers to avoiding collisions have been disappearing, increasing the risk of accidents and the need for solutions that ensure a safe Human-Robot Collaboration. This paper proposes a safety system that implements Speed and Separation Monitoring (SSM) type of operation. For this, safety zones are defined in the robot's workspace following current standards for industrial collaborative robots. A deep learning-based computer vision system detects, tracks, and estimates the 3D position of operators close to the robot. The robot control system receives the operator's 3D position and generates 3D representations of them in a simulation environment. Depending on the zone where the closest operator was detected, the robot stops or changes its operating speed. Three different operation modes in which the human and robot interact are presented. Results show that the vision-based system can correctly detect and classify in which safety zone an operator is located and that the different proposed operation modes ensure that the robot's reaction and stop time are within the required time limits to guarantee safety.
翻译:在工业机器人附近工作时,人类安全始终是主要优先事项。随着人类机器人合作环境的兴起,避免碰撞的物理屏障已经消失,事故风险增加,需要确保人类机器人合作安全的解决办法。本文件提出一个安全系统,用于执行速度和分离监测(SSM)类型的操作。为此,机器人工作场所的安全区按照工业协作机器人的现行标准加以定义。一个深层次的基于学习的计算机视觉系统探测、跟踪和估计操作者靠近机器人的3D位置。机器人控制系统接收操作者的3D位置,并在模拟环境中生成3D表示器。根据最接近操作者所在的区域,机器人停止或改变操作速度。三种不同的操作模式是人类和机器人互动。结果显示,基于视觉的系统可以正确检测和分类操作者所在的安全区,不同的操作模式确保机器人的反应和停止时间在必要的时限内,以保证安全。