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Optimization on Trajectory of Stanford Manipulator based on Genetic Algorithm

机译:基于遗传算法的斯坦福机械手轨迹优化

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The optimization of robot manipulator’s trajectory has become a hot topic in academic and industrial fields. In this paper, a method for minimizing the moving distance of robot manipulators is presented. The Stanford Manipulator is used as the research object and the inverse kinematics model is established with Denavit-Hartenberg method. Base on the initial posture matrix, the inverse kinematics model is used to find the initial state of each joint. In accordance with the given beginning moment, cubic polynomial interpolation is applied to each joint variable and the positive kinematic model is used to calculate the moving distance of end effector. Genetic algorithm is used to optimize the sequential order of each joint and the time difference between different starting time of joints. Numerical applications involving a Stanford manipulator are presented.
机译:机器人操纵器的轨迹的优化已成为学术和工业领域的热门话题。在本文中,提出了一种最小化机器人操纵器的移动距离的方法。斯坦福机械手用作研究对象,并与Denavit-Hartenberg方法建立了反向运动学模型。基于初始姿态矩阵,逆运动学模型用于找到每个关节的初始状态。根据给定的开始时刻,将立方多项式插值应用于每个关节变量,并且正动脉模型用于计算末端执行器的移动距离。遗传算法用于优化每个关节的顺序和关节不同起始时间之间的时间差。提出了涉及斯坦福机械手的数值应用。

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