Computer Science ›› 2018, Vol. 45 ›› Issue (12): 201-205.doi: 10.11896/j.issn.1002-137X.2018.12.033

• Artificial Intelligence • Previous Articles     Next Articles

Repeatable Motion Planning of Redundant Manipulators Based on Terminal Neural Networks

KONG Ying1,2, SUN Ming-xuan1   

  1. (College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)1
    (School of Information and Electronic Engineering,Zhejiang University of Science and Technology,Hangzhou 310023,China)2
  • Received:2017-09-21 Online:2018-12-15 Published:2019-02-25

Abstract: To solve the joint-angle drift problems in cyclic motion of redundant robot manipulators,a kind of quadratic optimization models for redundant manipulators’ trajectory planning based on terminal optimality criterion was proposed and analyzed.The terminal neural network models with limited value activation functions are applied to redundant manipulators to demonstrate the effectiveness of the proposed computing models in performing the repeatable motion planning tasks under the condition that the initial position deviates from the target position.New types of terminal neural network (TNN) and its accelerated form (ATNN) were proposed,which are of terminal attractor characteristics and can get effective solution for time-varying matrix in finite time.Compared with the asymptotic neural network (ANN),terminal neural network method not only accelerate the convergent rate,but also improve convergent precision.The si-mulation results on the model of PUMA560 show that the proposed method is effective and real-time.

Key words: Finite-time convergence, Redundant manipulators, Repeatable motion planning, Terminal neural networks

CLC Number: 

  • TP309.7
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