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Research and development leading to the realisation of a fully autonomous and robust multi-fingered hand has been going on for three decades. Yet none can be found in an industrial application. This is largely because we do not fully understand the fundamental mechanics of multi-finger grasping. / This thesis is a study of the mechanics of multi-finger grasping, with particular attention being paid to applying the analysis to experimental co-operative motion tasks between a hand-arm system and grasped object. / Fine manipulation with multi-fingered robot hands is critically influenced by the capacity to achieve stable grasps. By exploring the fundamental mechanics involved, a method for establishing the stability of spatial four finger-contact grasps is obtained. This work examines both frictionless and frictional grasps in two and three dimensions and develops the stability requirements for grasping. The conditions for a stable grasp are expressed as simple equations relating the line coordinates of (i) transitory sliding actuator and (ii) the normal to the tangent plane at every contact location. This is achieved by using the principle of virtual work and a branch of statics known as astatics. / After specifying a grasp in terms of its contact locations and forces the object can be grasped. However, in general the configuration of the hand-arm combination will not be unique, as such a manipulator system has more than six degrees of freedom and is said to be super-abundant. The choice of appropriate shares taken by the arm and hand in delivering the manipulation task needs to be resolved. This can be done making use of a kinematic performance measure based on aligning the grip triangle with the hand line of symmetry and maximising the available manipulation range. The hand-arm combination can then be driven to this desired grasp enabling the manipulator to carry out the specified task effectively. A Salisbury hand and PUMA 760 robot arm are used to demonstrate these co-operative motion tasks. / All the experimental results are presented along with a detailed description of the implementation of a hierarchical robot controller system which incorporates force control of the PUMA 760.

During dexterous manipulation with the fingertips, forces are applied to objects' surfaces. To achieve grasp stability, these forces must be appropriate given the properties of the objects and the skin of the fingertips, and the nature of the task. It has been demonstrated that tactile sensors in the fingertips provide crucial information about both object properties and mechanical events critical for the control of fingertip forces, while in certain tasks vision may also contribute to predictions of required fingertip actions. This thesis focuses on two specific aspects of the sensory control of manipulation: (i) how individual fingers are controlled for grasp stability when people restrain objects subjected to unpredictable forces tangential to the grasped surfaces, and (ii) how tactile sensors in the fingertips encode direction of fingertip forces and shape of surfaces contacted by the fingertips. When restraining objects with two fingers, subjects adjust the fingertip forces to the local friction at each digit-object interface for grasp stability. This is accomplished primarily by partitioning the tangential force between the digits in a way that reflects the local friction whereas the normal forces at the involved digits are scaled by the average friction and the total load. The neural control mechanisms in this task rely on tactile information pertaining to both the friction at each digit-object interface and the development of tangential load. Moreover, these mechanisms controlled the force application at individual digits while at the same time integrating sensory inputs from all digits involved in the task. Microneurographical recordings in awake humans shows that most SA-I, SA-II and FA-I sensors in the distal phalanx are excited when forces similar to those observed during actual manipulation are applied to the fingertip. Moreover, the direction of the fingertip force influences the impulse rates in most afferents and their responses are broadly tuned to a preferred direction. The preferred direction varies among the afferents and, accordingly, ensembles of afferents can encode the direction of fingertip forces. The local curvature of the object in contact with the fingertip also influenced the impulse rates in most afferents, providing a curvature contrast signals within the afferent populations. Marked interactions were observed in the afferents' responses to object curvature and force direction. Similar findings were obtained for the onset latency in individual afferents. Accordingly, for ensembles of afferents, the order by which individual afferents initially discharge to fingertip events effectively represents parameters of fingertip stimulation. This neural code probably represents the fastest possible code for transmission of parameters of fingertip stimuli to the CNS.