A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems

Amitava Chatterjee, Koliya Pulasinghe, Keigo Watanabe, Kiyotaka Izumi

Research output: Contribution to journalArticle

164 Citations (Scopus)

Abstract

This paper shows the possible development of particle swarm optimization (PSO)-based fuzzy-neural networks (FNNs) that can be employed as an important building block in real robot systems, controlled by voice-based commands. The PSO is employed to train the FNNs that can accurately output the crisp control signals for the robot systems, based on fuzzy linguistic spoken language commands, issued by a user. The FNN is also trained to capture the user-spoken directive in the context of the present performance of the robot system. Hidden Markov model (HMM)-based automatic speech recognizers (ASRs) are developed, as part of the entire system, so that the system can identify important user directives from the running utterances. The system has been successfully employed in two real-life situations, namely: 1) for navigation of a mobile robot; and 2) for motion control of a redundant manipulator.

Original languageEnglish
Pages (from-to)1478-1489
Number of pages12
JournalIEEE Transactions on Industrial Electronics
Volume52
Issue number6
DOIs
Publication statusPublished - Dec 1 2005
Externally publishedYes

Keywords

  • Combinatorial metaheuristics
  • Fuzzy-neural network
  • Particle swarm optimization
  • Voice-controlled robots

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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