Date of Award

6-3-2011

Document Type

Thesis (Undergraduate)

Department

Department of Computer Science

First Advisor

Jerald Kralik

Second Advisor

Devin Balkcom

Abstract

This paper presents a multilevel, posture-based motor control model intended to plan collision-free movements in a 3D environment while maintaining computationally efficiency and accurately imitating human and primate motor function. Our model is a comprehensive approach that addresses the storage and lookup of postures and movements, path planning and the generation of new movements, and learning with experience. We demonstrate the functionality and computational advantages of the model through preliminary testing on a humanoid robot.

Comments

Originally posted in the Dartmouth College Computer Science Technical Report Series, number

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