本文将 Kane 动力学方法与假设模态法相结合.给出一种分析机器人手臂弹性动力学的新方法.首先基于 Kane 方法的运动学概念,并应用假设模态法建立了手臂弹性运动学.推导出完整的弹性动力学方程.并以一简例说明了其应用过程.这种方法比较...本文将 Kane 动力学方法与假设模态法相结合.给出一种分析机器人手臂弹性动力学的新方法.首先基于 Kane 方法的运动学概念,并应用假设模态法建立了手臂弹性运动学.推导出完整的弹性动力学方程.并以一简例说明了其应用过程.这种方法比较简洁,兼具 Lagrange 法和 Newton-Euler 法的优点而克服了其不足,便于计算机数值分析.展开更多
The mechanism type plays a decisive role in the mechanical performance of robotic manipulators. Feasible mechanism types can be obtained by applying appropriate type synthesis theory, but there is still a lack of effe...The mechanism type plays a decisive role in the mechanical performance of robotic manipulators. Feasible mechanism types can be obtained by applying appropriate type synthesis theory, but there is still a lack of effective and efficient methods for the optimum selection among different types of mechanism candidates. This paper presents a new strategy for the purpose of optimum mechanism type selection based on the modified particle swarm optimization method. The concept of sub-swarm is introduced to represent the different mechanisms generated by the type synthesis, and a competitive mechanism is employed between the sub-swarms to reassign their population size according to the relative performances of the mechanism candidates to implement the optimization. Combining with a modular modeling approach for fast calculation of the performance index of the potential candidates, the proposed method is applied to determine the optimum mechanism type among the potential candidates for the desired manipulator. The effectiveness and efficiency of the proposed method is demonstrated through a case study on the optimum selection of mechanism type of a heavy manipulator where six feasible candidates are considered with force capability as the specific performance index. The optimization result shows that the fitness of the optimum mechanism type for the considered heavy manipulator can be up to 0.578 5. This research provides the instruction in optimum selection of mechanism types for robotic manipulators.展开更多
文摘本文将 Kane 动力学方法与假设模态法相结合.给出一种分析机器人手臂弹性动力学的新方法.首先基于 Kane 方法的运动学概念,并应用假设模态法建立了手臂弹性运动学.推导出完整的弹性动力学方程.并以一简例说明了其应用过程.这种方法比较简洁,兼具 Lagrange 法和 Newton-Euler 法的优点而克服了其不足,便于计算机数值分析.
基金supported by National Natural Science Foundation of China (Grant No. 51075259)Program for New Century Excellent Talents in University of Ministry of Education, China (Grant No. NCET-10-0579)+1 种基金National Basic Research Program of China (973 program, Grant No.2006CB705407)Key Technologies R&D Program of Shanghai,China (Grant No. 10111100203)
文摘The mechanism type plays a decisive role in the mechanical performance of robotic manipulators. Feasible mechanism types can be obtained by applying appropriate type synthesis theory, but there is still a lack of effective and efficient methods for the optimum selection among different types of mechanism candidates. This paper presents a new strategy for the purpose of optimum mechanism type selection based on the modified particle swarm optimization method. The concept of sub-swarm is introduced to represent the different mechanisms generated by the type synthesis, and a competitive mechanism is employed between the sub-swarms to reassign their population size according to the relative performances of the mechanism candidates to implement the optimization. Combining with a modular modeling approach for fast calculation of the performance index of the potential candidates, the proposed method is applied to determine the optimum mechanism type among the potential candidates for the desired manipulator. The effectiveness and efficiency of the proposed method is demonstrated through a case study on the optimum selection of mechanism type of a heavy manipulator where six feasible candidates are considered with force capability as the specific performance index. The optimization result shows that the fitness of the optimum mechanism type for the considered heavy manipulator can be up to 0.578 5. This research provides the instruction in optimum selection of mechanism types for robotic manipulators.
