Myoelectric Teleoperation of a Dual-Arm Manipulator Using Neural Networks

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Author(s): Toshio Tsuji (Hiroshima University, Japan), Kouji Tsujimura (OMRON Corporation, Japan)and Yoshiyuki Tanaka (Hiroshima University, Japan)
Copyright: 2006
Pages: 23
Source title: Neural Networks in Healthcare: Potential and Challenges
Source Author(s)/Editor(s): Rezaul Begg (Victoria University, Australia), Joarder Kamruzzaman (Monash University, Australia)and Ruhul Sarker (University of New South Wales, Australia)
DOI: 10.4018/978-1-59140-848-2.ch007

Keywords: Computer Science & IT / Health Information Systems / Medical Information Science Reference / Neural Networks

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Abstract

In this chapter, an advanced intelligent dual-arm manipulator system teleoperated by EMG signals and hand positions is described. This myoelectric teleoperation system employs a probabilistic neural network, so called log-linearized Gaussian mixture network (LLGMN), to gauge the operator’s intended hand motion from EMG patterns measured during tasks. In addition, an event-driven task model using Petri net and a non-contact impedance control method are introduced to allow a human operator to maneuver a couple of robotic manipulators intuitively. A set of experimental results demonstrates the effectiveness of the developed prototype system.

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Myoelectric Teleoperation of a Dual-Arm Manipulator Using Neural Networks

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