基于模糊神经网络的包装机械臂定位方法研究

目的 为提高包装机械臂的抓取精度，文中基于模糊神经网络设计一种包装机械臂定位方法。方法 将激光测距仪与工业相机融合，可实现目标点的初步定位并得到位姿偏差。以机械臂末端位置误差补偿为例，设计一种模糊神经网络控制器，可实现PID控制关键参数的在线调整以提高误差补偿精度。进一步地，采用果蝇优化算法实现神经网络控制器初始值的优化，可提高控制系统性能。最后，进行实验研究。结果 实验结果表明，机械臂定位算法可使最大绝对误差从7.704 9 mm下降到1.424 2 mm；平均绝对定位误差降低约82.5%；机械臂执行效率与对照组相当。结论 该定位方法可以大幅度提高包装机械臂定位精度，可满足包装、化工、食品等相关行业要求。

Positioning Method of Packaging Mechanical Arm Based on Fuzzy Neural Network

This paper aims to improve the grasping precision of the packaging manipulator through a positioning method of the packaging manipulator based on fuzzy neural network. By combining laser rangefinder with industrial camera, the initial positioning of the target point and the attitude deviation could be achieved. A fuzzy neural network controller was designed to improve the accuracy of error compensation by adjusting the key parameters of PID control. Furthermore, fruit fly optimization algorithm was used to optimize the initial value of neural network controller, which could improve the performance of the control system. Finally, experimental research was carried out. The experimental results showed that the maximum absolute error could be reduced from 7.704 9 mm to 1.424 2 mm. The average absolute positioning error was reduced by about 82.5%. The execution efficiency of the manipulator was similar to that of the control group. The positioning method can greatly improve the positioning accuracy of robots and meet the requirements of packaging, chemical, food and other related industries.