Kinematic control and obstacle avoidance for redundant manipulators using a recurrent neural network

Wai Sum Tang, Cherry Miu Ling Lam, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Citations (Scopus)

Abstract

In this paper, a recurrent neural network called the Lagrangian network is applied for obstacle avoidance in kinematically redundant manipulators. Conventional numerical methods implemented in digital computers for obstacle avoidance redundancy resolution calculation could only compute the solution in milliseconds while neural network realized by hardware could complete the computation in microseconds, which is more desirable in real-time control of manipulators. By giving the desired end-effector velocities and the obstacle location, the neural network could generate the joint velocity vector which drives the manipulator to avoid obstacles and tracks the desired end-effector trajectory simultaneously. Simulation results show that the neural network is capable of computing the redundancy resolution for obstacle avoidance.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2001 - International Conference, Proceedings
EditorsKurt Hornik, Georg Dorffner, Horst Bischof
PublisherSpringer Verlag
Pages922-929
Number of pages8
ISBN (Print)3540424865, 9783540446682
DOIs
Publication statusPublished - 2001
Externally publishedYes
EventInternational Conference on Artificial Neural Networks, ICANN 2001 - Vienna, Austria
Duration: 21 Aug 200125 Aug 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2130
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Artificial Neural Networks, ICANN 2001
Country/TerritoryAustria
CityVienna
Period21/08/0125/08/01

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