Myoelectric Teleoperation of a Dual-Arm Manipulator Using Neural Networks

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.