Dynamic Modeling and Analysis of Omnidirectional Wheeled Robot: Turning Motion Analysis

This paper presents the dynamic modeling of a four-mecanum-wheeled mobile robot (4MWMR) to be assessed for frequent turning motion. Overdriven factor in this kind of vehicle motion is one of the issues that need to be tackled for safety and energy efficiencies reasons especially in its turning region. Therefore, this study has taken initiative to analyzing 4MWMR through a structure of mathematical model starting from the inverse kinematics calculation. Moreover, the dynamic model of 4MWMR was calculated using Euler Lagrange approach as a part of the model for torque and force assessment. The analyses are done by using the data history of the experiment of an actual 4MWMR platform as trajectory input to kinematics and dynamics model that connected with 4MWMR transfer function plant. Finally, the performance of 4MWMR parameters; wheel velocity, torque and vehicle axial forces; are demonstrated. From the sample of turning point input, the results show that 4MWMR performing different speed of wheels at different poles during turning session as well as torques. Vehicle longitude force shows the highest since the vehicle is a holonomic system used more force on longitude and latitude axes instead of rotational force on the body

EEG Different Frequency Sound Response Identification using Neural Network and Fuzzy Techniques

Electroencephalographic (EEG) technology has enabled effective measurement of human brain activity, as functional and physiological changes within the brain may be registered by EEG signals. In this paper, electrical activity of human brain due to sound waves of different frequency, i.e. 40 Hz, 500 Hz, 5000 Hz and 15000 Hz, is studied based on EEG signals. Several signal processing techniques, i.e. Principle Component algorithm, Discrete Wavelet Transform and Fast Fourier Transform, are applied onto the raw EEG signal to extract useful information and specific characteristics from the EEG signals. This research has shown that the characteristics of EEG signals differ with respect to different frequency of sound waves, and hence the EEG signal can be identified with suitable characterization algorithm using artificial intelligent techniques, such as Artificial neural network, fuzzy logic and adaptive neuro-fuzzy system