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

作　　者：何元烈[1] 徐扣 HE Yuanlie;XU Kou(School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China)

机构地区：[1]广东工业大学计算机学院,广州510006

出　　处：《计算机工程与应用》2016年第17期54-58,151,共6页Computer Engineering and Applications

基　　金：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)

摘　　要：针对欠驱动平面机器人三连杆简化模型,分析模型的动力学方程及其积分特性,采用模型退化方法,将该模型的方程式从部分可积降阶为两个完全可积的子方程式,计算关节之间的角度约束关系。为克服粒子群算法实现逆运动学求解收敛慢、易陷入局部最优等缺陷,依据得到的关节角度约束关系,以实际角度与理想角度的误差平方和作为适应度函数,提出利用粒子群优化神经网络的学习型算法进行求解。仿真实验验证了方法的有效性。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.

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 

分 类 号：TP242.2[自动化与计算机技术—检测技术与自动化装置]