Multi-criteria Design Optimization of Parallel Robots. Unal, R., Kiziltas, G., & Patoglu, V. In IEEE International Conference on Cybernetics and Intelligent Systems and IEEE International Conference on Robotics, Automation and Mechatronics (CIS-RAM 2008), 2008. abstract bibtex This paper presents a framework for multi-criteria design optimization of parallel mechanisms. Pareto methods characterizing the trade-off between multiple design criteria are advocated for multi-criteria optimization over widely used scalarization approaches and Normal Boundary Intersection method is applied to efficiently obtain the Pareto-front hyper-surface. The proposed framework is compared against sequential optimization and weighted sum approaches. Dimensional synthesis of a sample parallel mechanism (five-bar mechanism) is demonstrated through estimation of the relative weights of performance indices that are implicit in the Pareto plot. The framework is computational efficient, applicable to any set of performance indices, and extendable to include any number of design criteria that is required by the application.
@InProceedings{Unal2008b,
title = {{Multi-criteria Design Optimization of Parallel Robots}},
booktitle = {IEEE International Conference on Cybernetics and Intelligent Systems and IEEE International Conference on Robotics, Automation and Mechatronics (CIS-RAM 2008)},
author = {Ramazan Unal and Gullu Kiziltas and Volkan Patoglu},
year = {2008},
abstract ={This paper presents a framework for multi-criteria design optimization of parallel mechanisms. Pareto methods characterizing the trade-off between multiple design criteria are advocated for multi-criteria
optimization over widely used scalarization approaches and Normal Boundary Intersection method is applied to efficiently obtain the Pareto-front hyper-surface. The proposed framework is compared against sequential optimization and
weighted sum approaches. Dimensional synthesis of a sample parallel mechanism (five-bar mechanism) is demonstrated through estimation of the relative weights of performance indices that are implicit in the Pareto plot. The
framework is computational efficient, applicable to any set of performance indices, and extendable to include any number of design criteria that is required by the application.}
}
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