基于自适应kalman滤波的机器人6DOF无标定视觉定位

Adaptive Kalman Filter-based Robot 6DOF Uncalibrated Vision Positioning

机器人的强耦合和强非线性使得6自由度视觉定位成为机器人视觉伺服领域的一个热点和难点问题。提出了一种基于自适应kalman滤波的机器人无标定6自由度视觉定位方法。首先利用图像的全局特征描述子—图像矩设计了一组图像矩组并以它的变化来表征摄像机与目标之间的相对平动与转动。在不标定摄像机与机器人坐标变换关系的情况下,应用自适应kalman滤波器来在线估计图像雅可比矩阵,并在此基础上设计视觉控制律从而计算出机器人的运动控制量,最后在MATLAB环境下建立了眼固定机器人无标定6自由度视觉定位Simulink模型,实现了机器人6自由度视觉定位。仿真实验结果表明,在噪声的统计特性不完全已知的情况下,所设计的自适应kalman滤波器能使6自由度机器人到达期望的位置,且定位精度高。

Robot 6 degree of freedom vision positioning is a popular and difficult topic in the field of robot visual servoing for its property of strong coupling and nonlinearity. A robot 6-degree of freedom uncalibrated vision positioning method was proposed using an adaptive kalman filter. Firstly, a set of image moments was designed to represent the relative translation and rotation motion between camera and object. Then, an adaptive kalman filter was designed to estimate the image Jacobian matrix on-line in the uncalibrated hand-eye coordination systems and visual control law was designed to calculate the motion control quantity of the robot. Finally, the Simulink model for a robot 6DOF uncalibrated vision positioning system with eye-to-hand configuration was built using Matlab. After this, the 6-degree of freedom vision positioning of robot was achieved. The experiment results of 6- degree of freedom vision positioning show that the adaptive kalman filter can guide the robot to the desired position with high accuracy under the partial known noises.