This study proposes an optimized hybrid visual servoing approach to overcome the imperfections of classical two-dimensional, three-dimensional and hybrid visual servoing methods. These imperfections are mostly convergence issues, non-optimized trajectories, expensive calculations and singularities. The proposed method provides more efficient optimized trajectories with shorter camera path for the robot than image-based and classical hybrid visual servoing methods. Moreover, it is less likely to lose the object from the camera field of view, and it is more robust to camera calibration than the classical position-based and hybrid visual servoing methods. The drawbacks in two-dimensional visual servoing are mostly related to the camera retreat and rotational motions. To tackle these drawbacks, rotations and translations in Z-axis have been separately controlled from three-dimensional estimation of the visual features. The pseudo-inverse of the proposed interaction matrix is approximated by a neuro-fuzzy neural network called local linear model tree. Using local linear model tree, the controller avoids the singularities and ill-conditioning of the proposed interaction matrix and makes it robust to image noises and camera parameters. The proposed method has been compared with classical image-based, position-based and hybrid visual servoing methods, both in simulation and in the real world using a 7-degree-of-freedom arm robot.

本研究提出了一种优化的混合视觉伺服方法，以克服经典二维、三维和混合视觉伺服方法的缺陷。这些缺陷主要是收敛问题、未优化的轨迹、昂贵的计算和奇点。与基于图像和经典的混合视觉伺服方法相比，所提出的方法为机器人提供了更有效的优化轨迹和更短的相机路径。此外，它不太可能从相机视野中丢失物体，并且与经典的基于位置和混合视觉伺服方法相比，它对相机标定具有更强的鲁棒性。二维视觉伺服的缺点主要与相机后退和旋转运动有关。为了解决这些缺点，Z 中的旋转和平移-axis 已从视觉特征的三维估计中单独控制。所提出的交互矩阵的伪逆由称为局部线性模型树的神经模糊神经网络近似。使用局部线性模型树，控制器避免了所提出的交互矩阵的奇异性和病态，并使其对图像噪声和相机参数具有鲁棒性。所提出的方法已经与经典的基于图像、基于位置和混合视觉伺服方法进行了比较，无论是在模拟还是在现实世界中，都使用 7 自由度手臂机器人。