Adaptive visual servoing for the robot manipulator with extreme learning machine and reinforcement learning

Jiashuai Li, Xiuyan Peng, Bing Li, Victor Sreeram, Jiawei Wu, Wansheng Mi

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, a novel image-based visual servo (IBVS) controller for robot manipulators is investigated using an optimized extreme learning machine (ELM) algorithm and an offline reinforcement learning (RL) algorithm. First of all, the classical IBVS method and its difficulties in accurately estimating the image interaction matrix and avoiding the singularity of pseudo-inverse are introduced. Subsequently, an IBVS method based on ELM and RL is proposed to solve the problem of the singularity of the pseudo-inverse solution and tune adaptive servo gain, improving the servo efficiency and stability. Specifically, the ELM algorithm optimized by particle swarm optimization (PSO) was used to approximate the pseudo-inverse of the image interaction matrix to reduce the influence of camera calibration errors. Then, the RL algorithm was adopted to tune the adaptive visual servo gain in continuous space and improve the convergence speed. Finally, comparative simulation experiments on a 6-DOF robot manipulator were conducted to verify the effectiveness of the proposed IBVS controller.

Original languageEnglish
Pages (from-to)280-296
Number of pages17
JournalAsian Journal of Control
Volume26
Issue number1
Early online date28 Aug 2023
DOIs
Publication statusPublished - Jan 2024

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