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虚拟传感器在并联机器人柔性环节运动学估计中的应用。

Virtual Sensor for Kinematic Estimation of Flexible Links in Parallel Robots.

机构信息

Department of Automatic Control and System Engineering, Faculty of Engineering in Bilbao, University of the Basque Country (UPV/EHU), Plaza Ingeniero Torres Quevedo 1, 48013 Bilbao, Spain.

Department of Mechanical Engineering, Faculty of Engineering in Bilbao, University of the Basque Country (UPV/EHU), Plaza Ingeniero Torres Quevedo 1, 48013 Bilbao, Spain.

出版信息

Sensors (Basel). 2017 Aug 23;17(9):1934. doi: 10.3390/s17091934.

Abstract

The control of flexible link parallel manipulators is still an open area of research, endpoint trajectory tracking being one of the main challenges in this type of robot. The flexibility and deformations of the limbs make the estimation of the Tool Centre Point (TCP) position a challenging one. Authors have proposed different approaches to estimate this deformation and deduce the location of the TCP. However, most of these approaches require expensive measurement systems or the use of high computational cost integration methods. This work presents a novel approach based on a virtual sensor which can not only precisely estimate the deformation of the flexible links in control applications (less than 2% error), but also its derivatives (less than 6% error in velocity and 13% error in acceleration) according to simulation results. The validity of the proposed Virtual Sensor is tested in a Delta Robot, where the position of the TCP is estimated based on the Virtual Sensor measurements with less than a 0.03% of error in comparison with the flexible approach developed in ADAMS Multibody Software.

摘要

柔性连杆并联机器人的控制仍然是一个研究领域,端点轨迹跟踪是这种机器人的主要挑战之一。肢体的灵活性和变形使得工具中心点 (TCP) 位置的估计变得具有挑战性。作者已经提出了不同的方法来估计这种变形并推导出 TCP 的位置。然而,这些方法中的大多数都需要昂贵的测量系统或使用高计算成本的积分方法。本工作提出了一种基于虚拟传感器的新方法,该方法不仅可以在控制应用中精确估计柔性连杆的变形(误差小于 2%),而且根据仿真结果还可以估计其导数(速度误差小于 6%,加速度误差小于 13%)。根据与 ADAMS 多体软件中开发的柔性方法相比,在 Delta 机器人中测试了所提出的虚拟传感器的有效性,根据虚拟传感器测量值估计 TCP 的位置,误差小于 0.03%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8312/5621090/b77f8a3a0e64/sensors-17-01934-g001.jpg

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