In this thesis, the synthesis of a grasping and manipulation controller of the Barrett hand, which is an archetypal example of a multi-fingered robotic hand, is investigated in some detail. This synthesis involves not only the dynamic modelling of the robotic hand but also the control of the joint and workspace dynamics as well as the interaction of the hand with object it is grasping and the environment it is operating in. Grasping and manipulation of an object by a robotic hand is always challenging due to the uncertainties, associated with non-linearities of the robot dynamics, unknown location and stiffness parameters of the objects which are not structured in any sense and unknown contact mechanics during the interaction of the hand’s fingers and the object. To address these challenges, the fundamental task is to establish the mathematical model of the robot hand, model the body dynamics of the object and establish the contact mechanics between the hand and the object. A Lagrangian based mathematical model of the Barrett hand is developed for controller implementation. A physical SimMechanics based model of the Barrett hand is also developed in MATLAB/Simulink environment. A computed torque controller and an adaptive sliding model controller are designed for the hand and their performance is assessed both in the joint space and in the workspace. Stability analysis of the controllers are carried out before developing the control laws. The higher order sliding model controllers are developed for the position control assuming that the uncertainties are in place. Also, this controllers enhance the performance by reducing chattering of the control torques applied to the robot hand. A contact model is developed for the Barrett hand as its fingers grasp the object in the operating environment. The contact forces during the simulation of the interaction of the fingers with the object were monitored, for objects with different stiffness values. Position and force based impedance controllers are developed to optimise the contact force. To deal with the unknown stiffness of the environment, adaptation is implemented by identifying the impedance. An evolutionary algorithm is also used to estimate the desired impedance parameters of the dynamics of the coupled robot and compliant object. A Newton-Euler based model is developed for the rigid object body. A grasp map and a hand Jacobian are defined for the Barrett hand grasping an object. A fixed contact model with friction is considered for the grasping and the manipulation control. The compliant dynamics of Barrett hand and object is developed and the control problem is defined in terms of the contact force. An adaptive control framework is developed and implemented for different grasps and manipulation trajectories of the Barrett hand. The adaptive controller is developed in two stages: first, the unknown robot and object dynamics are estimated and second, the contact force is computed from the estimated dynamics. The stability of the controllers is ensured by applying Lyapunov’s direct method.

This research presents an approach in assisting the control and operation of a rehabilitation robot manipulator to execute simple grasping tasks for persons with severe disabilities. It outlines the development of an end-effector sensory suite that includes the BarrettHand end-effector, laser range finder, and a low cost camera. The approach taken in this research differs greatly from the currently available rehabilitation robot arms in that it requires minimal user instruction, it is easy to operate and more effective for persons severely disabled. A thorough study of the currently available systems; Manus, Raptor and Kares II arm, is also presented. In order to test the end-effector sensory suite, experiments were performed to find the centroid of an object of interest to direct the robot end-effector towards it with minimal error. Analyses of centroid location data to ensure accurate results are also presented. The long term goal of this research is to significantly enhance the ability of severely disabled persons to perform activities of daily living using wheelchair mounted robot arms. The sensory suite developed through this project is expected to be integrated into a seven-degree of freedom wheelchair mounted robot arm currently under development at the Rehabilitation Robots Laboratory at the University of South Florida.