基于混合PSOGSA算法的3R机械手优化设计

S. Panda, D. Mishra, B. B. Biswal
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引用次数: 1

摘要

更大的工作空间是机械臂优化设计的重要目标之一。在本研究中,最大化了3R机械手的工作空间体积。采用粒子群算法(PSO)和粒子群与引力搜索混合算法(GSA)求解了非线性约束优化问题。该算法结合PSO和GSA的搜索方法,增强了搜索能力,实现了比现有结果更大的工作空间体积。进一步,估算了总空隙截面面积,并对结果进行了定量分析,确定了影响运动参数的关键因素,并对约束条件进行了排序。最后给出了混合算法在两种工业机械臂上的应用实例。本文的一个重要含义是,所提出的混合算法在目标函数值和CPU时间方面提供了令人鼓舞的结果。
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Optimum design of 3R manipulator using hybrid PSOGSA algorithm
Larger workspace volume is one of the most important objectives in the optimum design of robot manipulator. In the present study, the workspace volume of a 3R manipulator has been maximised. The nonlinear constrained optimisation problem has been solved using the particle swarm optimisation (PSO) and a hybrid PSO and gravitational search algorithm (GSA). The proposed algorithm combines the search method of PSO and GSA with enhanced exploration ability to achieve higher workspace volume as compared to the established available results. Further, the total void cross section area has been estimated and a quantitative analysis of the results has been made to identify the key influencing kinematic parameters and prioritise the constraints. Applications of the hybrid algorithm on two industrial manipulators are presented as case studies. An important implication of this article is that the proposed hybrid algorithm provides encouraging result in terms of objective function values and CPU time.
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