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Observer-Extended Direct Method for Collision Monitoring in Robot Manipulators Using Proprioception and IMU Sensing

机译:使用Braprioception和IMU感测机器人操纵器中的观察者扩展直接方法

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摘要

In this letter a novel method for accurate and high-bandwidth real-time monitoring of robot collisions is presented. To the authors' knowledge this is the first time the so called direct method, which is mathematically the simplest and theoretically the ideal one, has been realized at practically relevant levels. For this, joint velocity and acceleration of serial chain robots are initially estimated using observer techniques that fuse joint position, Cartesian acceleration and angular velocity measurements. Consequently, this algorithm, which also extends our previous work in velocity and acceleration estimation, together with the available robot dynamics model are utilized to algebraically monitor external forces applied to the robot. Specifically, the proposed sensor fusion setup increases estimation bandwidth and decreases detection uncertainties compared to existing methods. Moreover, since neither inversion of large matrices nor their derivatives are required, our approach also shows increased numerical stability. Finally, the developed algorithm is evaluated based on a realistic simulation with the consideration of all parasitic effects and experimentally with a 7-DoF flexible joint robot.
机译:在这封信中,提出了一种用于机器人冲突的准确和高带宽实时监测的新方法。对于作者的知识,这是第一次被称为直接方法,这是在实际相关的水平的最简单和理论上是最简单和理论上的方法。为此,最初使用熔断器的观察者技术,笛卡尔加速度和角速度测量来估计串联链式机器人的联合速度和加速度。因此,该算法还扩展了我们以前的速度和加速度估计的工作,以及可用的机器人动力学模型用于代数监测应用于机器人的外力。具体地,所提出的传感器融合设置增加了估计带宽,并降低了与现有方法相比的检测不确定性。此外,由于大矩阵的反转也不需要它们的衍生物,因此我们的方法也显示出增加的数值稳定性。最后,通过考虑所有寄生效应并用7-DOF柔性接头机器人进行实验来评估发达的算法。

著录项

  • 来源
    《IEEE Robotics and Automation Letters》 |2020年第2期|954-961|共8页
  • 作者单位

    Syst Univ Munich Munich Sch Robot Machine Intelligence Chair Robot Sci Syst Intelligence TUM Munich Germany|Tech Univ Munich TUM Munich Sch Robot & Machine Intelligence Chair Robot Sci & Syst Intelligence D-80797 Munich Germany;

    Syst Univ Munich Munich Sch Robot Machine Intelligence Chair Robot Sci Syst Intelligence TUM Munich Germany|Tech Univ Munich TUM Munich Sch Robot & Machine Intelligence Chair Robot Sci & Syst Intelligence D-80797 Munich Germany;

    Syst Univ Munich Munich Sch Robot Machine Intelligence Chair Robot Sci Syst Intelligence TUM Munich Germany|Tech Univ Munich TUM Munich Sch Robot & Machine Intelligence Chair Robot Sci & Syst Intelligence D-80797 Munich Germany;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Collision avoidance; sensor fusion; robot safety;

    机译:碰撞;传感器融合;机器人安全;

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