摘要
传统的基于图像视觉伺服控制需要计算雅可比矩阵和解雅克比矩阵的逆,其结构复杂、计算量大且系统的实时性不够理想。基于粒子群遗传算法优化的BP(Back Propagation)神经网络(PSO-GA-BP:Particle Swarm Optimization-Genetic Algorithm-BP)通过学习图像特征空间到机器人运动空间的映射关系,实现了“眼在手上”的机器人视觉伺服控制,通过优化BP神经网络的权值和阈值,防止了其训练时间长、收敛速度慢等弊端。实验结果表明,优化后的算法运算效率较高,所设计的控制器能使机器人末端执行器在更短的时间内达到预期位置,图像特征点运动位置的实际值与期望值平均误差约为2个像素,具有良好的收敛速度和控制精度。相关结论可为机器人视觉伺服控制提供优化依据,提高算法的应用性能。
Traditional image-based visual servo control needs to calculate Jacobian matrix and inverse of Jacobian matrix,which is complex in structure,large amount calculation and unsatisfactory real-time performance.The BP neural network optimized by PSO(Particle Swarm Optimization)genetic algorithm realizes the vision servo control of“eye on hand”robot by learning the mapping relationship between image feature space and robot motion space.By optimizing the weight and threshold of BP neural network,the disadvantages of long training time and slow convergence speed are prevented.The experimental results show that the optimized algorithm has high efficiency.The designed controller can make the robot end actuator reach the expected position in a shorter time.The average error between the actual value and the expected value of the motion position of the image feature points is about two pixels,which has good convergence speed and control accuracy.The relevant conclusions can provide the basis for the optimization of robot visual servo control and improve the application performance of the algorithm.
作者
赵航
岳晓峰
方博
袁晓磊
马国元
郭宋吾铭
ZHAO Hang;YUE Xiaofeng;FANG Bo;YUAN Xiaolei;MA Guoyuan;GUO Songwuming(College of Mechanical and Electrical Engineering,Changchun University of Technology,Changchun 130012,China)
出处
《吉林大学学报（信息科学版）》
CAS
2020年第2期172-178,共7页
Journal of Jilin University（Information Science Edition）
基金
吉林省科技厅重点科技攻关基金资助项目(2017020410GX)。
关键词
PSO-GA-BP神经网络
视觉伺服
粒子群优化算法
遗传算法
particle swarm optimization-genetic algorithm-BP(PSO-GA-BP)neural network
visual servo
particle swarm optimization(PSO)
genetic algorithm(GA)
