Visual Servoing of Humanoid Dual-Arm Robot with Neural Learning Enhanced Skill Transferring Control

Andy SK Annamalai

Research output: Contribution to journalArticle

Abstract

This paper presents a novel combination of visual servoing (VS) control and neural network (NN) learning on humanoid dual-arm robot. A VS control system is built by using stereo vision to obtain the 3D point cloud of a target object. A least square based method is proposed to reduce the stochastic error in workspace calibration. An NN controller is designed to compensate for the effect of uncertain payload and other internal and external uncertainties during the tracking control. In contrast to the conventional NN controller, a deterministic learning technique is utilised in this work, to enable the learned neural knowledge to be reused before current dynamics changes. A skill transfer mechanism is is also developed to apply the neural learned knowledge from one arm to the other, to increase the neural learning efficiency. Tracked trajectory of object is used to provide target position to the coordinated dual arms of a Baxter robot in the experimental study. Robotic implementations has demonstrated the efficiency of the developed VS control system and has verified the effectiveness of the proposed NN controller with knowledge-reuse and skill transfer features.
Original languageEnglish
JournalInternational Journal of Humanoid Robotics
Volume15
Issue number2
DOIs
Publication statusPublished - 30 Apr 2018

    Fingerprint

Cite this