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Novel integrated optimization algorithm for trajectory planning of robot manipulators based on integrated evolutionary programming

Novel integrated optimization algorithm for trajectory planning of robot manipulators based on integrated evolutionary programming

作     者:XiongLUO XiaopingFAN HengZHANG TefangCHEN 

作者机构:CollegeofInformationScienceandEnoneermgCentralSouthUniversityChangshaHunan410083China 

出 版 物:《控制理论与应用(英文版)》 (JOURNAL OF CONTROL THEORY AND APPLICATIONS)

年 卷 期:2004年第2卷第4期

页      面:319-331页

学科分类:080202[工学-机械电子工程] 08[工学] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 

基  金:This work was supported in part by National Natural Science Foundation of China (No. 69975003) and Foundation for Dissertation of Ph. D. Candidate of Central South University (No.030618) 

主  题:Trajectory planning Integrated optimization Evolutionary programming Robot manipulator 

摘      要:Optimal trajectory planning for robot manipulators plays an important role in implementing the high productivity for robots. The performance indexes used in optimal trajectory planning are classified into two main categories: optimum traveling time and optimum mechanical energy of the actuators. The current trajectory planning algorithms are designed based on one of the above two performance indexes. So far, there have been few planning algorithms designed to satisfy two performance indexes simultaneously. On the other hand, some deficiencies arise in the existing integrated optimi2ation algorithms of trajectory planning. In order to overcome those deficiencies, the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators. In the algorithm, two object functions are designed based on the specific weight coefficient method and ideal point strategy. Moreover, based on the features of optimization problem, the intensified evolutionary programming is proposed to solve the corresponding optimization model. Especially, for the Stanford Robot,the high-quality solutions are found at a lower cost.

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