计算机工程与应用2016，Vol.52Issue(17)：54-58,151,6.DOI:10.3778/j.issn.1002-8331.1512-0254
欠驱动平面机器人逆运动学求解研究--粒子群优化神经网络算法求解
Particle swarm neural network solution to inverse kinematics of underactuated planar robot
摘要
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 fall-ing 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.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Key words
underactuated planar robot/inverse kinematics/particle swarm/neural network/angle error分类
信息技术与安全科学引用本文复制引用
何元烈,徐扣..欠驱动平面机器人逆运动学求解研究--粒子群优化神经网络算法求解[J].计算机工程与应用,2016,52(17):54-58,151,6.基金项目
广东省产学研合作专项资金资助项目（No.2014B090904080）；广东省应用型科技研发专项资金项目（No.2015B090922012）。 （）
