The human hand is an essential part of human body, capable of making complex and expressive motions. Its complicated structure makes it a formidable challenge for animators to animate hand motions. Most computer graphics research on hand motion has focused on preshaping, preshaping and gestures with application to areas of human computer interaction and sign language. There are also a number of educational applications such as typing, playing of musical instruments etc. From a computer graphics standpoint, these applications are difficult in animation of hand. This thesis aims to animate 3D hand preshaping activity for a chosen virtual 3D object in real-time. Researches on human hand kinematics, structure and geometric stability analysis on preshaping are the main motivation for the algorithms developed in this thesis for animating 3D preshaping. The algorithm that we developed is made of two main parts. The first part is related with the precision type preshaping requiring the finger-tips positioning for a given object such as the cube, cylinder or sphere. First part is completed by procedural approach which is based on kinematics to generate the motion of the hand for the given virtual object at the determined finger-tip positions. Second part related with the wrap type preshaping aims to have maximum interaction between hand and object. For this purpose, we have developed the collision detection algorithm to find intersection surfaces between hand and object. Even though developed algorithm based on the kinematics was used for the precision type preshaping application, it can also be used for many other applications requiring hand animation given the positions of finger tips.

This dissertation propose a new fluidics based framework to determine a continuum between preshaping and grasping so as to appropriately preshape a multi-fingered robot hand for creating an optimal initialization of grasp. The continuum of a hand preshape closing upon an object that creates an initial object motion tendency of the object based on the impact moment patterns generated from the fingers is presented. These motion tendencies should then be suitable for the proper initiation of the grasping task. The aim is motivated by human like behavior where we preshape and land on an object to initiate a certain grasping behavior without losing the continuum during the &quot / preshaping to grasping&quot / phases. The continuity of momentum transfer phenomena is inspired by fluid dynamics that deals with fluid flow. We have adapted governing equations based on the physical principles of the fluid flow to generate momentum transfer from the robotic fingers, closing upon the object surface to fluid medium particles then from these fluid medium particles to the grasping object. Smoothed Particular Hydrodynamics (SPH) which is a mesh free particle method and finite volume approximation is used to analyze fluid flow equations. The fingers of the robotic hand and object are modeled by solidified fluid elements and also can be compliance. For evaluating the optimal grasp initialization of different hand preshape, we propose a decision support system consisting of artificial feed forward neural network based on the moment distribution on the object determines either : 1) given initial position and orientation of a robot hand, what preshape is suitable for generating a desired moment distribution on the surface of a given object in order to trigger a desired rotation in a desired direction when approaching with this preshaped hand or 2) given a predetermined hand preshape what initial position, orientation and hand aperture are suitable to generate a desired rotation upon approach and without causing the retroceeding of the object.