结合MPGA-RBFNN的一般机器人逆运动学求解

针对一般机器人逆运动学求解过程中存在的求解速度慢、精度低的问题,将多种群遗传算法(multiple population genetic algorithm,MPGA)引入径向基函数神经网络(radial basis functions neural network,RBFNN),提出一种适用于一般机器人的高精度MPGA-RBFNN算法。该算法采用3层结构的RBFNN进行一般机器人逆运动学求解,结合一般机器人的正运动学模型,采用MPGA优化RBFNN的网络结构和连接权值的方法,同时应用混合编码和演化的方式,实现了从机器人工作空间位姿到关节角度的非线性映射,从而避免了复杂的公式推导并提高了求解速度。采用6R一般机器人作为实验平台进行实验,实验结果表明:MPGA-RBFNN算法不仅提高了一般机器人在逆运动学中的求解速度,而且MPGA-RBFNN算法的训练成功率和逆解的计算准确率也得到了提高。

A general robot inverse kinematics solution based on MPGA-RBFNN

In order to solve the problem of the inverse kinematics in a general robot, such as slow speed in problem-solving and lower solution accuracy, a high-precision algorithm is proposed for general robots, which introduces Multiple Population Genetic Algorithm into Radial Basis Functions neural network (MPGA-RBFNN). Combined with the positive kinematics model of general robots, a three-layer structure of RBFNN was used to solve the inverse kinematics, and the MPGA was adopted to optimize the network structure and connection weights of the RBFNN. By using hybrid coding and simultaneous evolutionary means, the non-linear mapping of the position of the robot in the working space to the joint angle was realized, avoiding complicated formula derivation and improving the speed of problem-solving. Finally, an experiment was conducted using the general 6R robot. The results showed that the speed of solving the problem of the inverse kinematics of a general robot was improved by the MPGA-RBFNN algorithm, and the training success rate of the MPGA-RBFNN algorithm and the calculation accuracy of the inverse kinematics were enhanced.