This thesis presents methods and tools for enabling the successful use
of robotic hands. For highly dexterous and/or anthropomorphic robotic hands, these
methods have to share some common goals, such as overcoming the potential
complexity of the mechanical design and the ability of performing accurate
tasks with low and efficient computational cost.  

A prerequisite for dexterity is to increase the workspace of the robotic
hand. For this purpose, the robotic hand must be considered as a single
multibody system. Solving the inverse kinematics problem of the whole robotic
hand is an arduous task due to the high number of degrees of freedom involved
and the possible mechanical limitations, singularities and other possible
constraints.

The redundancy has proven to be of a great usefulness for dealing with
potential constraints. To be able to exploit the redundancy for dealing with
constraints, the adopted method for solving the inverse kinematics must be
robust and extendable. Obviously, addressing such complex problem, the method
will certainly be computationally heavy. Thus, one of the aims of this thesis
is to resolve the inverse kinematics problem of the whole robotic hand under
constraints, taking into account the computational cost. To this end, this
thesis extends and reduces the most recent Selectively
Damped Least Squares method which is based on
the computation of all singular values, to deal with constraints with a minimum
computational cost. New estimation algorithm of singular values and their
corresponding singular vectors is proposed to reduce the computational cost.
The reduced extended selectively damped least squares method is simulated and
experimentally evaluated using an anthropomorphic robotic hand as a test bed. On
the other hand, dexterity depends not only on the accuracy of the position control,
but also on the exerted forces. The tendon driven modern robotic hands, like
the one used in this work, are strongly nonlinear dynamic systems, where
motions and forces are transmitted remotely to the finger joints. The problem
of modeling and control of position and force simultaneously at low level
control is then considered. A new hybrid control structure based on the
succession of two sliding mode controllers is proposed. The force is controlled
by its own controller which does not need a contact model. The performance of
the proposed controller is evaluated by performing the force control directly
using the force sensor information of the fingertip, and indirectly using the
torque control of the actuator.

Finally,
we expect that the applications of the methods presented in this thesis can be
extended to cover different issues and research fields and in particular they
can be used in a variety of algorithms that require the estimation of singular
values.