# Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing
## 1 Introduction  
Vision sensors are widely used to provide contactless knowledge about the environment. Adjusting the behaviour of robots to cope with uncertainties of unstructured environments is one of the main applications of vision sensors in industry [1]. The classical control laws are mostly formulated by minimizing a task function that corresponds to the achievement of a given goal. Typically, this primary role only concerns the location of the robot in relation to a goal, while the environment of the robot will not be taken into account. To incorporate servoing into a complex real-world robotic system, the control law must also ensure that unfavourable configurations are avoided, such as joint limits, kinematic singularities and occlusions [2]. Visual control, also known as visual servoing (VS), essentially consists of using data from one or more cameras as input to real-time closed-loop control schemes. The objective of VS is to control the movements of a dynamic system so that it achieves a task defined by a collection of visual constraints [3]. VS helps to modify the system in order to compensate for the deficiencies and to relax the mechanical inaccuracy of the robot [3].  
Controlling a robot using the image information has been the focus of a number of studies in the field of robotics and automation. There are a number of research studies that applied VS for solving industrial challenges and human-robot cooperation [4, 5, 6, 7, 8]. However, VS introduces new complexities in the image-space, joint space, and the intersection of these two; all of which must be considered [9].  
Image-based or 2D VS (IBVS), Position-based or 3D VS (PBVS), and Hybrid or 2 & 1/2D VS (HVS) are three main categories of VS control approaches. The IBVS method computes the feedback directly from extracted image-space features. This method is more resistant to the camera calibration and robot kinematic errors [10]. Additionally, image features are less likely to be lost from the image screen [11]. On the other hand, IBVS has some disadvantages; for instance, some controller commands are not physically executable for the robot (out of robot reachability) [12]. The poor conditioning of the image Jacobian matrix (Interaction matrix) might cause problems with the feature error convergence, like singularities and local minima [13]. Due to direct measurement of the camera velocities from the task space errors in the PBVS process, the interaction matrix problems (i.e., local minima and singularity) are avoided, and thus feasible trajectories for the robot can be generated [14]. In addition, any error in the camera calibration may create an error in 3D estimation of the target and consequently affects the entire tracking task [3].  
In order to overcome the drawbacks of classical VS methods, we proposed Decoupled Hybrid Visual Servoing (DHVS) method, which has a better controllability (higher manipulability) than other VS methods. The efficacy of the proposed method has been investigated in a sorting application (Figure 1) and it is detailed in Section 4.4. It should be mentioned that manipulability refers to the attribute of being controllable by motions of the manipulator [15]. The proposed framework is outlined in Figure 2 and detailed in Section 2.4.