Motion Mode Decomposition And Multi-objective Optimization Design Of Several Parallel Mechanisms

With the deepening of the research and application of parallel robot and the improvement of the performance requirements of the mechanism,it is very necessary to analyze the kinematic characteristics and performance of the mechanism.The analysis of motion/force transmission and constraint performance of the parallel mechanism is also the key and premise of the application of the mechanism.Firstly,based on quaternion and SINGULAR software,several classical parallel mechanisms were decomposed into motion modes,and all operation modes of the mechanism were obtained through quasi prime decomposition of the constraint equation ideal.Tsai’s 3-UPU has a variety of operation modes,and it is difficult to realize the three-translational operation mode.Therefore,the operation modes of two asymmetrical3-UPU parallel mechanisms are analyzed and compared,and the motion mode decomposition makes the kinematic characteristics of the mechanism clearer.Secondly,the concepts and physical meanings of motion and force transmission as well as constraint indexes are introduced in detail.Then,based on the concept of energy efficiency coefficient,the local and global energy evaluation mathematical models are established,the transmission performance of several classical parallel mechanisms is analyzed,and the performance atlases are drawn.The transmission performance of the mechanism was greatly improved by scale synthesis based on the motion/force transmission performance indexes.Based on the optimized dimension parameters,the constraint performance of various mechanisms was analyzed,and the global transmission and constraint index values of the mechanisms were calculated.Finally,the multi-objective optimization design of each parallel mechanism was carried out based on multiple indicators of transmission and constraint performance.Based on the game theory algorithm,the size parameters of 3-SPR,3-RPS,and three3-UPU parallel mechanisms are gamed.The BP neural network model was optimized based on genetic algorithm and the optimal solution was found.A variety of intelligent algorithms verify each other to ensure the accuracy of the results,and then the performance values obtained by various optimization design methods are compared and analyzed to realize the multi-objective optimization of the mechanism.