# Self-Recognition Grasping Operation with a Vision-Based Redundant Manipulator System
## 1 Introduction  
Nowadays, manipulators have an increasing degree of requirement in many fields, since grasping operations [1; 2] with manipulators can be competent for various works that substitute human beings. A redundant manipulator which possesses more DOF (degree of freedom) and dexterity in three-dimensional space has a better performance in self-perception and self-learning when in an unstructured environment and amongst clutter. The ability of autonomy is increasingly becoming more and more important for manipulators in complex scene applications. Thus, it is significant for redundant manipulators to possess the ability of self-recognition, especially for grasping operations. To achieve self-recognized grasping operation, redundant manipulators need to complete object detection, object location, and grasp planning autonomously. In object detection, the target object is distinguished from others and the environment. In terms of the object location, the 3D (three dimensional) position and orientation angle of the target object are obtained in a pixel coordinate system. Then, via pose transformation, a joint angle sequence is derived for manipulator control via the pose (position and orientation) expressed in the pixel coordinate system. Vision systems which can obtain abundant information about the present environment have already been integrated in manipulator grasping operation [3; 4]. In the industry field, templates of objects (components or work piece) are made in advance. Features such as SIFT (scale-invariantfeature transform) [5] and SURF (speeded-up robust features) [6] are extracted from the image to achieve shape-matching [7]. Since the position of an object is traceable in the workbench, methods such as the optical flow method [8] are applied for simplified location and motion tracking. However, these methods rely on the known classification and position of objects, and they cannot be adapted for unstructured environments and those where clutter objects with unknown information exist. Indeed, with vision systems, target object detection and location can be achieved preferentially for self-recognition grasping operation. Benefit from the studies on deep learning, CNNs [9; 10] (convolutional neural networks) have been used to achieve object recognition, leading to various methods such as image classification [11; 12], object detection [13; 14], semantic segmentation [15; 16], and instance segmentation [17; 18]. Many frameworks of instance segmentation have been proposed, such as SDS (simultaneous detection and segmentation) [19], CFM (convolutional feature masking) [20], MNC (multi-task network cascades) [21], and so on. Mask R-CNN (regions with CNN features) [22], which achieves object classification and contour description simultaneously, can accurately determine target objects from an unstructured environment. Besides, the contour has a smaller region than the classification box for object pose estimation. After obtaining the contour and classification of an object by instance segmentation, the location of the object should be determined. In traditional methods, an RGB image is used to achieve pose estimation for the target object by matching local features [23; 24]. However, this is worse in performance when locating textureless objects. However, RGB-D images, which describe the depth information with a point cloud, can directly obtain the 3D position objects [25; 26; 27]. The benefit from grasping databases [28; 29; 30] and orientation estimation is converged into an classification problem [31] via discretizing orientation angles. As a result, the pose of target object can be located in the pixel coordinate system. Pose transformation can be divided into two steps. Firstly, the pose expressed in pixel coordinates is transferred into Cartesian coordinates via the parameters of the camera, which is explained in [32; 33]. Second, the pose is transferred into the joint space of the manipulator via inverse kinematics.