基于混合学习果蝇优化算法的冗余机械臂逆运动学求解

Inverse Kinematics Solution of Redundant Manipulator Based on Hybrid Learning Fruit Fly Optimization Algorithm

针对常规方法难以有效求解冗余机械臂逆运动学的不足,提出了一种基于改进果蝇优化算法的逆运动学解决方案。改进算法采用线性候选解产生机制,克服了基本果蝇优化算法不能搜索负值空间及无法在指定的区域内均匀搜索的缺陷。通过混合学习嗅觉搜索策略的构建,有效增强并合理平衡算法的全局探索与局部开发。此外,通过视觉实时更新策略的引入,提升了算法的搜索效率及加速了算法的收敛速度。以7自由度冗余机械臂的逆运动学求解为例展开对比试验分析,结果表明所提出算法在寻优速度、精度以及结果稳定性等方面明显优于对比算法,说明该方法能够有效解决冗余机械臂的逆运动学问题。

In order to effectively solve the inverse kinematics problem of redundant manipulators,an inverse kinematics solution based on improved fruit fly optimization algorithm was proposed.The improved algorithm adopted a linear candidate solution generation mechanism,which overcame the shortcomings that the fruit fly optimization algorithm could not search negative space and could not search uniformly in the specified area.Through the construction of hybrid learning olfactory search strategy,the global exploration and local exploitation of the algorithm were effectively enhanced and reasonably balanced.In addition,through the introduction of the real-time visual updating strategy,the search efficiency of the algorithm was improved,and the convergence rate was also effectively accelerated.Taking the inverse kinematics solution of a 7-DOF redundant manipulator as an example,the results showed that the proposed algorithm was superior to the comparative algorithms in terms of convergence rate,convergence accuracy and results stability,which indicated that the method can be used to effectively solve the inverse kinematics problem of redundant manipulators.