基于遗传算法的护理机器人逆运动学求解方法

护理机器人结构复杂、耦合度高且机械臂不满足PIEPER准则，标准遗传算法难以精准地对其逆运动学进行求解，以至于机器人手臂末端位姿误差较大。针对标准遗传算法求解过程中早熟和局部搜索能力差的问题，采用等分区间替代随机命令产生的初始种群个体，划分5个小区间以提高种群个体的分散程度和搜索效率；在适应度函数中引入可变权因子，将位置误差的变化值作为可变权因子的变量，进化过程中可变权因子在0.5~1.0之间变化，且实时有效分配位置和姿态误差权重，确保解的收敛。通过仿真和实验验证，结果表明:改进后的遗传算法能够大幅提升收敛精度和速度，并且可以同时实现对位置和姿态的精确控制，极大地减小了机器人手臂的位姿误差。 

Inverse kinematic solution of nursing robot based on genetic algorithm

The nursing robot's arm does not meet the PIEPER criterion and is constructed with a high degree of coupling and complexity. The standard genetic algorithm is difficult to accurately solve its inverse kinematics. As a result, the pose error of the end of the robot arm is relatively large. In order to solve the problem that premature and poor local search ability in the standard genetic algorithm solution process, the following methods are proposed. Firstly, use equal partitions to replace the initial population of individuals generated by random commands. Divide 5 small areas to improve the dispersion of the population and search efficiency. Secondly, introduce variable weight factors in the fitness function. The change value of the pose error is used as the variable of the variable weight factor. The weight factor varies between 0.5-1.0 during the evolution process. And effectively assign position and attitude error weights. Finally, verified by simulation and experiment. The results show that the improved genetic algorithm can greatly improve the accuracy and speed of convergence, achieve precise control of position and attitude at the same time, and greatly reduce the pose error of the robot arm.