Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results

Hang Su, Yingbai Hu, Hamid Reza Karimi, Alois Knoll, Giancarlo Ferrigno, Elena De Momi

Research output: Contribution to journalArticlepeer-review

172 Scopus citations

Abstract

In this paper, an improved recurrent neural network (RNN) scheme is proposed to perform the trajectory control of redundant robot manipulators using remote center of motion (RCM) constraints. Firstly, learning by demonstration is implemented to model the surgical operation skills in the Cartesian space. After that, considering the kinematic constraints associated with the optimization control of redundant manipulators, we propose a novel RNN-based approach to facilitate accurate task tracking based on the general quadratic performance index, which includes managing the constraints on RCM joint angle, and joint velocity, simultaneously. The results of the conducted theoretical analysis confirm that the RCM constraint has been established successfully, and accordingly. The corresponding end-effector tracking errors asymptotically converge to zero. Finally, demonstration experiments are conducted in a laboratory setup environment using KUKA LWR4+ to validate the effectiveness of the proposed control strategy.

Original languageEnglish
Pages (from-to)291-299
Number of pages9
JournalNeural Networks
Volume131
DOIs
StatePublished - Nov 2020

Keywords

  • Recurrent neural network
  • Redundant manipulator
  • Remote center of motion
  • Robot-assisted minimally invasive surgery

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