jcm.hierarch_robotics.unr_ms_thesis03

Jcm.hierarch_robotic - University of Nevada Reno Design and Implementation of a Hierarchical Robotic System A Platform for Artificial Intelligence

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University of Nevada, Reno Design and Implementation of a Hierarchical Robotic System: A Platform for Artificial Intelligence Investigation A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science with a major in Computer Engineering. By Juan C. Macera Dr. Frederick C. Harris, Jr., Thesis Advisor December 2003
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© Juan C. Macera, 2003
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We recommend that the thesis prepared under our supervision by Juan C. Macera entitled Design and Implementation of a Hierarchical Robotic System: A Platform for Artificial Intelligence Investigation be accepted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE ____________________________________________ Dr. Frederick C. Harris, Jr., Ph.D., Advisor ____________________________________________ Dr. Dwight Egbert, Ph.D., Committee Member ____________________________________________ Dr. Monica N. Nicolescu, Ph.D., Committee Member ____________________________________________ Dr. Philip H. Goodman, Ph.D., At-Large Member ____________________________________________ Dr. Marsh H. Read, Ph.D., Associate Dean, Graduate School December 2003
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i Abstract Robots need to act in the real world, but they are constrained by weight, power, and computation capability. Artificial intelligence (AI) techniques try to mimic the clever processing of living creatures, but they lack embodiment and a realistic environment. This thesis introduces a novel robotic architecture that provides slender robots with massive processing and parallel computation potential. This platform allows the investigation and development of AI models (the brain) in interaction with its body and environment. Our robotic system distributes the processing on three biologically correlated layers: the Body, Brainstem, and Cortex; on board the robot, on a local PC, and on a remote parallel supercomputer, respectively. On each layer we have implemented a series of intelligent functions with different computational complexity, including a binaural sound localization technique, a bimodal speech recognition approach using artificial neural networks, and the simulation of biologically realistic spiking neural networks for bimodal speech perception.
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ii To my mother, a virtuous woman.
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Acknowledgments I would like to thank Dr. Frederick Harris Jr., my advisor, for his encouragement and valuable guidance throughout the thesis implementation. I am very grateful to Dr. Phil Goodman, director of the Brain Computation Laboratory, for his support and motivation during the project development. I express my sincere gratitude to Dr. Dwight Egbert, for leading me at the beginning of the project and for being part of my thesis committee. I also thank Dr. Monica Nicolescu for serving on my thesis committee. Finally, I would like to thank my brother Jose Macera, for his support when most needed,
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This note was uploaded on 03/03/2010 for the course SMT 271554 taught by Professor Unknown during the Spring '08 term at SUNY Empire State.

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Jcm.hierarch_robotic - University of Nevada Reno Design and Implementation of a Hierarchical Robotic System A Platform for Artificial Intelligence

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