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Genetic Algorithm for Solving the Inverse Kinematics Problem for General 6R Robots

机译:遗传算法解决一般6R机器人逆运动学问题的遗传算法

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The problem of inverse kinematics for general 6R robots was provided for calculation process, and also exist many inverse kinematics and geometric structure which did not meet the PIEPER criterion. In order to solve these problems, an inverse kinematics algorithm with high accuracy based on multiple population genetic algorithm (MPGA) was proposed. Multiple population was performed to accelerate the convergence rate and avoid the defect of the least part point. For illustrating the performance of the MPGA, the simulation results attained from MPGA are compared with those obtained from well-known single-population genetic algorithm (SGA). Experiments on Panasonic TA1400 robot verified that the algorithm could calculate all globally optimal solutions of general geometric structure and the pose error also can have up to two digits after the decimal point. So this algorithm can be used to guarantee higher control accuracy.
机译:为计算过程提供了一般6R机器人逆运动学的问题,并且还存在许多不符合焊盘标准的逆运动学和几何结构。为了解决这些问题,提出了一种基于多种群体遗传算法(MPGA)高精度的反向运动学算法。进行多种群体以加速收敛速度并避免最小部分的缺陷。为了说明MPGA的性能,将从MPGA获得的模拟结果与从众所周知的单群遗传算法(SGA)获得的那些进行比较。松下TA1400机器人的实验验证了该算法可以计算一般几何结构的全局最佳解决方案,并且在小数点之后也可以有多达两位数的姿势。因此,该算法可用于保证更高的控制精度。

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