  The optimization of parallel kinematic manipulators (PKM) involve several
constraints that are difficult to formalize, thus making optimal synthesis
problem highly challenging. The presence of passive joint limits as well as the
singularities and self-collisions lead to a complicated relation between the
input and output parameters. In this article, a novel optimization methodology
is proposed by combining a local search, Nelder-Mead algorithm, with global
search methodologies such as low discrepancy distribution for faster and more
efficient exploration of the optimization space. The effect of the dimension of
the optimization problem and the different constraints are discussed to
highlight the complexities of closed-loop kinematic chain optimization. The
work also presents the approaches used to consider constraints for passive
joint boundaries as well as singularities to avoid internal collisions in such
mechanisms. The proposed algorithm can also optimize the length of the
prismatic actuators and the constraints can be added in modular fashion,
allowing to understand the impact of given criteria on the final result. The
application of the presented approach is used to optimize two PKMs of different
degrees of freedom.
