基于粒子滤波的雅可比矩阵在线估计技术

研究基于图像的机器人视觉伺服技术中雅可比矩阵的在线估计方法.以雅可比矩阵的元素构成系统状态向量,将问题转化为对系统的状态估计问题.引入非线性非高斯系统的粒子滤波算法,在该算法的框架下在线估计图像雅可比矩阵.以非高斯环境下二自由度机械手跟踪运动目标这一应用背景为例,分别对新提出方法与已有基于Kalman滤波的估计方法进行了实验比较.结果证明,前者具有更高的估计精度和更强的鲁棒性,基于粒子滤波的方法不仅可以避免系统标定,而且对系统噪声的类型没有具体要求.

On-Line Estimation of Jacobian Matrix Based on Particle Filter

Proposes a new method of estimating Jacobian matrixes on-line for image-based robot visual servo systems.A vector is firstly formed from the elements of a Jacobian matrix,and the problem is converted into one of state-estimation.Particle filtering suitable for non-liner nonGaussian systems is utilized to solve the Jacobian estimation problem.The proposed method and the one based on Kalman filtering are tested to track a moving target on a two-degree-of-freedom system with non-Gaussian noise.The results showed the effectiveness and the robustness of the proposed method.System calibrations can be avoided and no specification on system noises is needed.