Login | Register

Visual Calibration, Identification and Control of 6-RSS Parallel Robots


Visual Calibration, Identification and Control of 6-RSS Parallel Robots

Li, Pengcheng (2020) Visual Calibration, Identification and Control of 6-RSS Parallel Robots. PhD thesis, Concordia University.

[thumbnail of Li_PhD_F2020.pdf]
Text (application/pdf)
Li_PhD_F2020.pdf - Accepted Version


Parallel robots present some outstanding advantages in high force-to-weight ratio, better stiffness and theoretical higher accuracy compared with serial manipulators. Hence parallel robots have been utilized increasingly in various applications. However, due to the manufacturing tolerances and defections in the robot structure, the positioning accuracy of parallel robots is basically equivalent with that of serial manipulators according to previous researches on the accuracy analysis of the Stewart Platform [1], which is difficult to meet the precision requirement of many potential applications. In addition, the existence of closed-chain mechanism yields difficulties in designing control system for practical applications, due to its highly coupled dynamics.

Visual sensor is a good choice for providing non-contact measurement of the end-effector pose (position and orientation) with simplicity in operation and low cost compared to other measurement methods such as the coordinate measurement machine (CMM) [2] and the laser tracker [3]. In this research, a series of solutions including kinematic calibration, dynamic identification and visual servoing are proposed to improve the positioning and tracking performance of the parallel robot based on the visual sensor.

The main contributions of this research include three parts. In the first part, a relative pose-based algorithm (RPBA) is proposed to solve the kinematic calibration problem of a six-revolute-spherical-spherical (6-RSS) parallel robot by using the optical CMM sensor. Based on the relative poses between the candidate and the initial configurations, a calibration algorithm is proposed to determine the optimal error parameters of the robot kinematic model and external parameters introduced by the optical sensor. The experimental results demonstrate that the proposal RPBA using optical CMM is an implementable and effective method for the parallel robot calibration.

The second part focuses on the dynamic model identification of the 6-RSS parallel robots. A visual closed-loop output-error identification method based on an optical CMM sensor is proposed for the purpose of the advanced model-based visual servoing control design of parallel robots. By using an outer loop visual servoing controller to stabilize both the parallel robot and the simulated model, the visual closed-loop output-error identification method is developed and the model parameters are identified by using a nonlinear optimization technique. The effectiveness of the proposed identification algorithm is validated by experimental tests.

In the last part, a dynamic sliding mode control (DSMC) scheme combined with the visual servoing method is proposed to improve the tracking performance of the 6-RSS parallel robot based on the optical CMM sensor. By employing a position-to-torque converter, the torque command generated by DSMC can be applied to the position controlled industrial robot. 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 developed DSMC scheme is robust to the modeling errors and uncertainties. Compared with the classical kinematic level controllers, the proposed DSMC exhibits the superiority in terms of tracking performance and robustness.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Thesis (PhD)
Authors:Li, Pengcheng
Institution:Concordia University
Degree Name:Ph. D.
Program:Mechanical Engineering
Date:27 April 2020
Thesis Supervisor(s):Xie, Wen-Fang
ID Code:986930
Deposited By: PENGCHENG LI
Deposited On:25 Nov 2020 16:05
Last Modified:25 Nov 2020 16:05
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top