Dynamic Optimization of a Three Translational Degrees of Freedom Parallel Robot Based on a Multi-Objective Genetic Algorithm

Abstract:

This work tends to deal with the multi-objective dynamic optimization problem of a three translational degrees of freedom parallel robot. Two global dynamic indices are proposed as the objective functions for the dynamic optimization: the index of dynamic dexterity, the index describing the dynamic fluctuation effects. The length of the linkages and the circumradius of the platforms were chosen as the design variables. A multi-objective optimal design problem, including constrains on the actuating and passive joint angle limits and geometrical interference is then formulated to find the Pareto solutions for the robot in a desired workspace. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to solve the constrained nonlinear multi-objective optimization problem. The simulation results obtained shows that the robot can achieve better dynamic dexterity and less dynamic fluctuation simultaneously after the optimization.

• Periodical: Applied Mechanics and Materials (Volumes 789-790)
• Pages: 723-734
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.789-790.723
• Online since: September 2015
• Authors: Xing Guo Lu*, Ming Liu, Min Xiu Kong
• Keywords: Global Dynamic Dexterity Conditioning Index, Global Dynamic Dexterity Fluctuation Conditioning Index, Multi-Objective Dynamic Optimization, NSGA-II, Parallel Robot

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