摘要
针对于机器人的无标定视觉伺服问题,本文提出一种基于同步扰动随机逼近算法优化Kalman滤波在线估计图像雅克比矩阵的方法。该方法将机器人图像雅克比矩阵作为系统状态,使用Kalman滤波器对系统状态进行观测。为提高滤波器性能,采用同步扰动随机逼近算法对滤波器参数进行优化。应用此方法估计出图像雅克比矩阵并设计控制率,避免了复杂的系统标定过程。仿真结果表明,所提出的方法能够实现无标定环境下六自由度机器人的视觉定位,且精度和稳定性较高。
Considering the problem of robot uncalibrated visual servoing,this paper presents a method for online estimation of image Jacobian matrix based on Kalman filter optimized by simultaneous perturbation stochastic approximation algorithm.This method takes the robot image Jacobian matrix as the system state,and uses Kalman filter to observe the system state.In order to improve the performance of the filter,the simultaneous perturbation stochastic approximation algorithm is used to optimize the filter parameters.This method is used to estimate the image Jacobian matrix and to design the control strategy,which can avoid complicated system calibration process.The simulation results indicate that the proposed method can achieve the visual positioning of the 6-degree of freedom robot with high accuracy and stability under the uncalibrated situation.
作者
章进强
张宪霞
Zhang Jinqiang;Zhang Xianxia(Shanghai Key Laboratory of Power Station Automation Technology,School of Mechatronics and Automation,Shanghai University,Shanghai 200072,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2018年第12期4754-4759,共6页
Journal of System Simulation
基金
国家自然科学基金(61273182)
