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Inverse kinematics using single- and multi-objective particle swarm optimization

机译：使用单目标和多目标粒子群算法的逆运动学

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摘要

Robotic manipulators are currently extensively used in a wide spectrum of industrial, medical and commercial facilities. While it is becoming a trend to use over-actuated manipulators, solution of related inverse kinematics problems becomes more challenging. Recently, more efforts have been directed towards utilizing alternative computationally efficient methods for solving such problems. During the last decade, the use of evolutionary algorithms involving particle swarm optimization as well as neural networks has been on the rise as a plausible alternative. This paper presents a new choice of parameters for the single-objective particle swarm optimization that leads to accurate and computationally efficient solutions. Furthermore, a multi-objective particle swarm optimization technique, which offers more flexibility in the manipulator motion, is introduced. Mathematical formulation of the problem and applications on different robotic manipulators are presented in the paper with good simulation results.
机译：机器人操纵器目前广泛用于各种各样的工业，医疗和商业设施。尽管使用过度操纵的机械手已成为一种趋势，但相关逆运动学问题的解决方案却更具挑战性。近来，已经做出了更多努力来利用替代的计算有效的方法来解决这些问题。在过去的十年中，作为可能的替代方案，涉及粒子群优化和神经网络的进化算法的使用正在增加。本文提出了用于单目标粒子群优化的参数的新选择，该参数导致了准确且计算效率高的解决方案。此外，介绍了一种多目标粒子群优化技术，该技术在操纵器运动中提供了更大的灵活性。本文提出了该问题的数学公式及其在不同机器人操纵器上的应用，并取得了良好的仿真结果。

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