Abstract
Solving the inverse kinematics for a manipulator is of great importance to the manipulator's pose control and trajectory planning. Aiming at the poor generality and difficulty of finding an optimal solution from the multiple inverse kinematics solutions, a novel solution approach based on the modified adaptive niche genetic algorithm is proposed in this study. The principle of 'most suppleness' is integrated into the fitness function such that the only optimal solution can be found; The clustering is introduced into the approach for enhancing the generality and the genetic algorithm is improved for increasing the convergence speed and accuracy. Simulation results based on a six degree of freedom manipulator show that the proposed approach is effective and high precision, and can find the optimal solution.
Highlights
逆运动学是一个多元多峰函数最值求取问题, 遗传算法( genetic algorithm,GA) 等智能优化算法也 可用于求解该类问题。 Çavdar[11] 提出了基于 人 工 蜂群的逆运动学求解算法,并与粒子群算法,和谐搜 索算法进行了对比分析。 Huang 等[12] 基 于 混 合 Taguchi DNA 群体智能算法对逆运动学进行了求解,并与 GA 进行了精度对比。 Tabandeh 等[13] 通过 自适应小生境遗传算法( adaptive niched genetic al⁃ gorithms,ANGA) 对逆运动学问题进行了分析,求得
Solving the inverse kinematics for a manipulator is of great importance to the manipulator′s pose control and trajectory planning
Aiming at the poor generality and difficulty of finding an optimal solution from the multiple inverse kinematics solutions, a novel solution approach based on the modified adaptive niche genetic algorithm is proposed in this study
Summary
逆运动学是一个多元多峰函数最值求取问题, 遗传算法( genetic algorithm,GA) 等智能优化算法也 可用于求解该类问题。 Çavdar[11] 提出了基于 人 工 蜂群的逆运动学求解算法,并与粒子群算法,和谐搜 索算法进行了对比分析。 Huang 等[12] 基 于 混 合 Taguchi DNA 群体智能算法对逆运动学进行了求解,并与 GA 进行了精度对比。 Tabandeh 等[13] 通过 自适应小生境遗传算法( adaptive niched genetic al⁃ gorithms,ANGA) 对逆运动学问题进行了分析,求得 以经典工业机械臂 PUMA560 为 例, 在 Matlab 环境下进行以下仿真实验: 仿真实验一: 采用改进 ANGA, 适应度函 数 取 (12) 式,求得“ 最柔顺” 原则下机械臂逆运动学的最 优解。 仿真实验二: 采用改进 ANGA, 适应度函 数 取 (8)式,求得机械臂逆运动学的 8 个解。 PUMA560 具有 6 个转动关节,前三关节确定腕 部参考点位置,后三关节决定腕部方位。 其 D⁃H 坐 标系如图 1 所示。
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