A Velocity-Level Bi-Criteria Optimization Scheme for Coordinated Path Tracking of Dual Robot Manipulators Using Recurrent Neural Network.

Lin Xiao,Long Jin,Bolin Liao,Zhijun Zhang,Yongsheng Zhang,Lei Ding

doi:10.3389/fnbot.2017.00047

Abstract

A dual-robot system is a robotic device composed of two robot arms. To eliminate the joint-angle drift and prevent the occurrence of high joint velocity, a velocity-level bi-criteria optimization scheme, which includes two criteria (i.e., the minimum velocity norm and the repetitive motion), is proposed and investigated for coordinated path tracking of dual robot manipulators. Specifically, to realize the coordinated path tracking of dual robot manipulators, two subschemes are first presented for the left and right robot manipulators. After that, such two subschemes are reformulated as two general quadratic programs (QPs), which can be formulated as one unified QP. A recurrent neural network (RNN) is thus presented to solve effectively the unified QP problem. At last, computer simulation results based on a dual three-link planar manipulator further validate the feasibility and the efficacy of the velocity-level optimization scheme for coordinated path tracking using the recurrent neural network.

Highlights

To satisfy the above requirements, in this article, a novel bi-criteria optimization scheme is presented and investigated for coordinated path tracking of dual robot manipulators at the joint velocity level, of which the bi-criteria consist of the minimum velocity motion (MVN) and the repetitive motion (RM)
It is worth pointing out that, if the final joint velocities is not equal to zero, the manipulator’ joints will not stop immediately at the end of the task duration; and the non-repetitive problem would happen. These results demonstrate and verify the effectiveness of such a bi-criteria optimization scheme synthesized by gradient-based neural network (GNN) model (equation (23))
Two subschemes have been presented for the left and right robot manipulators, which are reformulated as two general quadratic programs (QPs)

Summary

INTRODUCTION

Robot manipulators were widely investigated and applied to many fields (Jin et al, 2017; Zhang and Zhang, 2012; Xiao and Zhang, 2013, 2014a; Jin and Zhang, 2015; Zhang et al, 2015; Yamada et al, 2016), such as human–robot interaction, path tracking, industrial manufacturing, military, repetitive motion, and so on. The traditional pseudoinverse method needs to compute the inverse/pseudoinverse of matrices, which usually costs a lot of time This method would lead to the joint angle drift when the end-effector completes a repetitive motion (Klein and Ahmed, 1995). Considering the importance of the repetitive motion control for dual robot manipulators, it requires an effective criterion for solving the joint-angle drift problem of dual robot manipulators in practical applications (Xiao and Zhang, 2013, 2014a; Zhang et al, 2013). To satisfy the above requirements, in this article, a novel bi-criteria optimization scheme is presented and investigated for coordinated path tracking of dual robot manipulators at the joint velocity level, of which the bi-criteria consist of the minimum velocity motion (MVN) and the repetitive motion (RM). The computer simulation results further verify the feasibility and effectiveness of the proposed scheme for coordinated path tracking of dual robot manipulators using the recurrent neural network

PRELIMINARIES

SCHEME FORMULATION

QP REFORMULATION AND UNIFICATION

RECURRENT NEURAL NETWORK SOLVER

SIMULATIVE VERIFICATIONS

CONCLUSION