4.4 Article

Dynamic Visual Servoing of A 6-RSS Parallel Robot Based on Optical CMM

期刊

出版社

SPRINGER
DOI: 10.1007/s10846-021-01402-5

关键词

Visual servoing; Sliding mode control; Parallel robot; Optical CMM; Path tracking; Industrial robot

资金

  1. Natural Sciences and Engineering Research Council of Canada (NSERC)
  2. Creaform
  3. GE Aviation
  4. Consortium de Recherche et D'Innovation en Aerospatiale au Quebec (CRIAQ)

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This paper proposes a dynamic sliding mode control scheme combined with visual servoing method to improve the tracking performance of a 6-RSS parallel robot, achieving complex 6-dimensional trajectory tracking with high accuracy in real-time experiments. The proposed DSMC exhibits superiority in terms of tracking performance and robustness compared to classical kinematic level controllers.
The parallel robots exhibit some outstanding properties on the repeatability, stiffness and force-to-weight ratio compared with serial robots. 6-DOF parallel robots have been utilized in various applications and the research on control design has attracted the attentions of researchers. However, the current Cartesian space path tracking performance of the parallel robots cannot meet the growing requirements from industry. In this paper, a dynamic sliding mode control (DSMC) scheme combined with the position-based visual servoing (PBVS) method is proposed to improve the tracking performance of the 6-Revolute-Spherical-Spherical (6-RSS) parallel robot based on the measurements from the optical coordinate measuring machine (CMM) sensor. By employing the CMM sensor, the pose of the parallel robot in Cartesian space can be estimated and incorporated in a closed-loop visual servoing control scheme in real time. The stability of the proposed DSMC has been proved by using Lyapunov theorem. The real-time experiment tests on a 6-RSS parallel robot demonstrate that the complex 6-dimension trajectory tracking can be achieved with high-accuracy. Compared with the classical kinematic level controllers, the proposed DSMC exhibits the superiority in terms of tracking performance and robustness.

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