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An optimal visual servo trajectory planning method for manipulators based on system nondeterministic model

Published online by Cambridge University Press:  04 February 2022

Ruolong Qi Open the ORCID record for Ruolong Qi [Opens in a new window] ,
Yuangui Tang  and
Ke Zhang
Ruolong Qi
Affiliation:
School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, China
Yuangui Tang*
Affiliation:
The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China
Ke Zhang
Affiliation:
School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, China
*
*Corresponding author. E-mail: 53157088@qq.com
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Abstract

When a manipulator captures its target by a visual servo system, uncertainties can arise because of mechanical system and visual sensors exist error. This paper proposes an intelligent method to predict the successful rate for a manipulator to capture its target with motion and sensor errors. Because the mapping between the joint space of the manipulator and the Cartesian space at the end of the manipulator is nonlinear, when there is a bounded error of the manipulator’s joint, the error range of the end motion is constantly changing with the different joint positions. And at the same time, the visual servo camera will also measure the target from different positions and postures, so as to produce measurement results with different error ranges. The unknown time-varying error property not only greatly affects the stability of the closed-loop control but also causes the capture failure. The purpose of this paper is to estimate the success probability of different capture trajectories by establishing the nondeterministic model of manipulator control system. First, a system model including motion subsystem and feedback subsystem was established with system error described by Gaussian probability. And then Bayesian estimation was introduced into the system model to estimate the executing state of the predefined trajectory. Linear least quadratic regulators (LQR) control is used to simulate the input correction in the closed-loop control between motion subsystem and feedback subsystem. At last, the successful probability of capturing the target is established by the Gaussian distribution at the end point of the trajectory with geometric relationship calculation between tolerance range and error distribution. The effectiveness and practicability of the proposed method are proved by simulation and experiment.

Keywords

visual servotrajectory evaluationmotion and sensor uncertaintymanipulator
Type
Research Article
Information
Robotica , Volume 40 , Issue 6 , June 2022 , pp. 1665 - 1681
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

†

Ruolong Qi, born in 1983, is currently an associate professor at School of Mechanical Engineering, Shenyang Jianzhu University, China. He received his doctor degree from Shenyang Institute of Automation, Chinese Academy of Sciences in 2017. He received his bachelor and master degree from Dalian University of Technology, China, in 2005 and 2008. His research interests include robot system and intelligent robotics. Tel: +86-15904026145; E-mail: qiruolong@126.com

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