UBIQUITOUS ROBOT: THE THIRD GENERATION OF ROBOTICS

Control circuits for operating a two-phase brushless do motor from a dc power source are described. Control of the motor is optimized by detecting induced back EMF of the two phases to sense rotor phase position. It is common knowledge that the voltage developed across the terminals of a permanent magnet motor can be represented by a winding resistance, a winding inductance and a generated back EMF. In two-phase brushless dc and permanent magnet stepping motors, the induced back EMFs of the two phases are displaced from each other by 90 electrical degrees. Thus, by detecting the back EMFs of the two phases, an accurate relative position of rotor-to-stator can be determined at any rotor position. Commutation of the motor phases and closed loop control of the motor and load is achieved by accurately sensing the back EMFs of the two phases. Also, phase position of the permanent magnet rotor poles is detected by integrating the sensed back EMFs of the two phases to produce quadrature sinewaves the magnitudes of which are constant relative to rotor speed.

[1]  Bruce Blumberg,et al.  The Art and Science of Synthetic Character Design , 1999 .

[2]  Jongwoo Lim,et al.  Implementation of Artificial Creature based on Interactive Learning , 2002 .

[3]  R. Lewontin.  ‘The Selfish Gene’ , 1977, Nature.

[4]  Bruce Blumberg,et al.  Motivation driven learning for interactive synthetic characters , 2000, AGENTS '00.

[5]  Jong-Hwan Kim,et al.  Face detection using quantum-inspired evolutionary algorithm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[6]  Jong-Hwan Kim.  Recent Progress and Development of Humanoid Robot HanSaRam Prof. Jong-Hwan Kim Korea Advance Institute of Technology Daejon, Korea The Origin of Artificial Species: Humanoid Robot HanSaRam , 2005 .

[7]  Michael Mateas,et al.  An Oz-Centric Review of Interactive Drama and Believable Agents , 1999, Artificial Intelligence Today.

[8]  A. B. Loyall,et al.  Integrating Reactivity, Goals, and Emotion in a Broad Agent , 1992 .

[9]  Atsuo Takanishi,et al.  Robot personality based on the equations of emotion defined in the 3D mental space , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[10]  Mark Weiser,et al.  Some Computer Science Problems in Ubiquitous Computing , 1993 .

[11]  Jong-Hwan Kim,et al.  Ubiquitous Robot: Recent Progress and Development , 2005, 2006 SICE-ICASE International Joint Conference.

[12]  Jong-Hwan Kim,et al.  Behavior selection and learning for synthetic character , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[13]  Masahiro Fujita,et al.  Ethological modeling and architecture for an entertainment robot , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[14]  Juan David Velásquez,et al.  An emotion-based approach to robotics , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[15]  Cynthia Breazeal,et al.  Function meets style: insights from emotion theory applied to HRI , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Toshio Fukuda,et al.  Evolving pet robot with emotional model , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[17]  Bruce Blumberg,et al.  A Layered Brain Architecture for Synthetic Creatures , 2001, IJCAI.