CAJ | 학술논문

针对采用传统反向传播( BP)神经网络算法进行逆运动学求解收敛速度慢的问题，提出将微分进化( DE)与粒子群优化( PSO)算法相结合，对用于机器人逆运动学求解的BP神经网络进行优化。基于机器人正解映射建立优化算法的目标函数，在PSO过程中，引入DE操作优化粒子进化方向，并将此混合算法用于BP神经网络权值与阈值的优化。对KUKA机器人进行仿真实验，结果表明：采用该文方法对机器人逆运动学问题的求解精度高，求得的关节角度误差小于0.1°；逆运动学求解结果所对应位姿矩阵的位置误差在0.1 mm数量级，具有较好的泛化能力。该文方法满足机器人位置和姿态方面的精度要求。
침대채용전통반향전파( BP)신경망락산법진행역운동학구해수렴속도만적문제，제출장미분진화( DE)여입자군우화( PSO)산법상결합，대용우궤기인역운동학구해적BP신경망락진행우화。기우궤기인정해영사건립우화산법적목표함수，재PSO과정중，인입DE조작우화입자진화방향，병장차혼합산법용우BP신경망락권치여역치적우화。대KUKA궤기인진행방진실험，결과표명：채용해문방법대궤기인역운동학문제적구해정도고，구득적관절각도오차소우0.1°；역운동학구해결과소대응위자구진적위치오차재0.1 mm수량급，구유교호적범화능력。해문방법만족궤기인위치화자태방면적정도요구。
Aiming at the problem of slow convergence speed of traditional back propagation ( BP ) neural network algorithms, differential evolution ( DE ) and particle swarm optimization ( PSO ) are combined to optimize BP neural network for robot inverse kinematics. An objective function of the op-timization algorithm is formulated based on the mapping of robot forward kinematics. DE operation is employed to optimize particle evolution direction in PSO,and the weights and thresholds of the BP neural network are optimized. A simulation experiment is proposed for a KUKA robot,and the result shows that:the solution accuracy of robot inverse kinematics of the algorithm proposed here is high, and the joint angle error is below 0 . 1 °;the position error between the initial pose matrix of the robot and that solved by the algorithm proposed here is of the order of magnitude of 0. 1 mm,and has good generalization ability. The algorithm proposed here satisfies the accuracy requirements of robot locations and postures.