Mamedov Shamil, Mikhel Stanislav
Laboratory of Mechatronics, Control and Prototyping, Center for Technologies in Robotics & Mechatronics Components, Innopolis University, Innopolis, Russia.
Industrial Robotics Lab, Center for Technologies in Robotics & Mechatronics Components, Innopolis University, Innopolis, Russia.
Front Robot AI. 2020 Nov 23;7:571574. doi: 10.3389/frobt.2020.571574. eCollection 2020.
Recently, with the increased number of robots entering numerous manufacturing fields, a considerable wealth of literature has appeared on the theme of physical human-robot interaction using data from proprioceptive sensors (motor or/and load side encoders). Most of the studies have then the accurate dynamic model of a robot for granted. In practice, however, model identification and observer design proceeds collision detection. To the best of our knowledge, no previous study has systematically investigated each aspect underlying physical human-robot interaction and the relationship between those aspects. In this paper, we bridge this gap by first reviewing the literature on model identification, disturbance estimation and collision detection, and discussing the relationship between the three, then by examining the practical sides of model-based collision detection on a case study conducted on UR10e. We show that the model identification step is critical for accurate collision detection, while the choice of the observer should be mostly based on computation time and the simplicity and flexibility of tuning. It is hoped that this study can serve as a roadmap to equip industrial robots with basic physical human-robot interaction capabilities.
近年来，随着越来越多的机器人进入众多制造领域，出现了大量以利用来自本体感觉传感器（电机或/和负载侧编码器）的数据进行人机物理交互为主题的文献。大多数研究都将机器人的精确动态模型视为理所当然。然而，在实际中，模型识别和观测器设计先于碰撞检测。据我们所知，之前没有研究系统地研究过人机物理交互背后的各个方面以及这些方面之间的关系。在本文中，我们通过首先回顾关于模型识别、干扰估计和碰撞检测的文献，并讨论这三者之间的关系，然后通过在UR10e上进行的案例研究来考察基于模型的碰撞检测的实际情况，弥补了这一差距。我们表明，模型识别步骤对于准确的碰撞检测至关重要，而观测器的选择应主要基于计算时间以及调谐的简单性和灵活性。希望这项研究能够成为为工业机器人配备基本人机物理交互能力的路线图。
