关键词: 欠驱动平面机器人, 逆运动学, 粒子群, 神经网络, 角度误差

Abstract: A simplified model of three-link underactuated planar robot is created, and the dynamic equation and integral feature of the model is analyzed. The method of model degradation is applied to calculate the relationship of angle between the joints constraint by reducing the partly integrable equation of the model to two completely integrable equations. To overcome some defects of particle swarm algorithm of which slow convergence rate for inverse kinematics and easily falling into a local optimum, particle swarm optimization neural network learning algorithm is proposed, which is based on the joint angle constraints and a fitness function which is the sum of the squares of the errors between practice and ideal angle. The method is proved to be effective in the simulation experiments.

Key words: underactuated planar robot, inverse kinematics, particle swarm, neural network, angle error