基于RBF的冗余机械手运动学逆解

A redundant manipulator inverse kinematic resolution based on RBF net

针对冗余机械手运动学中存在的非线性、强耦合以及时变性的特点,采用了一种基于径向基函数(RadialBasicFunc-tion,RBF)网络的非线性拟合和系统辨识的方法,利用RBF神经网络的逼近能力较优、收敛速度快、非线性处理能力强等特点,可以有效地对冗余机械手的运动学逆问题进行求解;同时,运用遗传算法选取RBF网络的中心,提高网络的性能及效率;用Matlab实现对6自由度机械手运动学的建模、仿真.网络求解的结果表明了该方法的有效性.

Aiming at the characteristics of nonlinear, strong coupling and time-varying in the field of redundant manipulator kinematics, a method of nonlinear fitting and system identification based on radial basic function （RBF） neural network is adopted. RBF neural network ,with the capabilities of good approximation, fast convergence, strong nonlinear processing ability and etc. , can effectively resolve the redundant manipulator inverse kinematics problem. Meanwhile, the center parameters of RBF network are selected by genetic algorithm to improve the performance and efficiency. The 6-DOF manipulator kinematic modeling and simulation is implemented with Matlab and the calculated results verify the effectiveness of the method proposed in this paper.