A digital design framework for the dimensional optimization of parallel robots based on kinematic and elasto-dynamic performance

The dimensional optimization based on kinematic and dynamic performance indices is proven significant for improving the operational performance of parallel robots. As the pose-dependent performances vary with the dimensional parameters, there are still many challenges in the optimal design of parallel robots, such as the possible conflict amongst different performance indices and computational expensiveness. By considering a 4-DOF high-speed parallel robot as an illustrative example, this paper presents a framework for the optimal design of parallel robots by integrating skeleton modeling, CAD-CAE integration and multi-objective optimization techniques. In this approach, the models for kinematic and elasto-dynamic performance evaluation are first developed using the CAD and CAE techniques, respectively. After evaluating the performance distributions over the task workspace, several reference poses representative of multiple neighboring sample poses are determined to formulate corresponding local performance indices by using the Hard C-Means (HCM) clustering analysis algorithm. This consideration leads to the determination of the optimal dimensions by investigating the Pareto-optimal solutions of a multi-objective optimization problem, allowing the conflict amongst different performance indices to be appropriately handled and the computational time to be saved significantly. The results of the case study show that the proposed approach can significantly enhance the elastic dynamic performance while ensuring that the kinematic performance is not excessively compromised, when compared to the performance of existing physical prototype of the robot. A software package has been developed using the proposed framework, providing a highly accessible way for designing various types of parallel/hybrid robots based on CAD and CAE techniques.