基于BP和RBF神经网络的机器人逆运动学算法

针对单一神经网络求逆运动学时存在求解精度不高、泛化能力差的问题,研究了工业机器人的工作空间及逆运动学求解算法。在对BP神经网络和RBF神经网络分析的基础上,提出了一种BP与RBF网络并行的7输入6输出神经网络模型。以一种协作型工业机械臂为例,首先建立其运动学模型并分析工作空间,然后求解正运动学获得数据集用于训练、验证和测试网络,最后得到符合要求的网络模型。仿真结果验证了网络的正确性,并行网络方法提高了单一神经网络的求解精度,同时求解速度优于解析法求逆运动学速度,证明了该方法的实用性。

Inverse Kinematics Algorithm of Robot Based on BP and RBF Neural Networks

Aiming at the problem of inverse kinematics in a single neural network, such as the low resolution and poor generalization ability, the working space and inverse kinematics algorithm of industrial robots are studied. Based on the analysis of Back Propagation (BP) and Radial Base Function (RBF) neural network, a 7-input 6-output neural network model with parallel BP network and RBF network was proposed. A cooperative industrial robot arm was taken as an example. Firstly, its kinematics model was established and the workspace was analyzed, then the positive kinematics was solved to obtain the data set, which was used to train, verify and test the network, and finally the network model was gotten that meets the requirements. The simulation results verify the correctness of the network, which shows that the parallel network method improves the solution accuracy of the single neural network, and the solution speed is faster than the inverse kinematics speed of the analytical method, which proves the practicality of the method.