Optimization of the workspace of a MEMS hexapod nanopositioner using an adaptive genetic algorithm

This paper presents workspace optimization of a MEMS flexure-based hexapod nanopositioner previously built by the National Institute of Standards and Technology (NIST). Workspace is one of the most important quality criteria for positioning devices. Given a lot of literature on workspace optimization of rigid body parallel robots, there is relatively less work done in their compliant counterparts due to the challenges in determining the workspace. In this paper, we present an analytical formulation and a search algorithm to determine the workspace of the flexure based parallel mechanisms. A novel adaptive genetic algorithm has been developed to conduct the single and bi-objective optimization for maximum translational and rotational workspace. These optimization results provide a guidance for the designer to improve the device for specific design requirements.