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The optimization of parallel kinematic manipulators (PKM) involve severalconstraints that are difficult to formalize, thus making optimal synthesisproblem highly challenging. The presence of passive joint limits as well as thesingularities and self-collisions lead to a complicated relation between theinput and output parameters. In this article, a novel optimization methodologyis proposed by combining a local search, Nelder-Mead algorithm, with globalsearch methodologies such as low discrepancy distribution for faster and moreefficient exploration of the optimization space. The effect of the dimension ofthe optimization problem and the different constraints are discussed tohighlight the complexities of closed-loop kinematic chain optimization. Thework also presents the approaches used to consider constraints for passivejoint boundaries as well as singularities to avoid internal collisions in suchmechanisms. The proposed algorithm can also optimize the length of theprismatic actuators and the constraints can be added in modular fashion,allowing to understand the impact of given criteria on the final result. Theapplication of the presented approach is used to optimize two PKMs of differentdegrees of freedom.

An efficient combined local and global search strategy for optimization of parallel kinematic mechanisms with joint limits and collision constraints