Workspace optimization of parallel robot by using multi-objective genetic algorithm

For the narrow workspace problem of the universal-prismatic-universal( UPU) parallel robotwith fixed orientation, a kind of multi-objective genetic algorithm is studied to optimize the robot’sworkspace. The concept of the effective workspace and its solution method are given. The effectiveworkspace height(EWH) and global condition number index(GCI) of Jacobi matrix are selected asthe optimized objective functions. Setting the robot in two different orientations, the geometric pa-rameters are optimized by the multi-objective genetic algorithm named non-dominated sorting geneticalgorithm II(NSGA-II), and a set of structural parameters is obtained. The optimization results areverified by four indicators with the robot’ s moving platform at different orientations. The resultsshow that, after optimization, the fixed-orientation workspace volume, the effective workspace heightand the effective workspace volume increase by 32. 4%, 17. 8% and 72. 9% on average, respec-tively. GCI decreases by 6. 8% on average.