# Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing
## 5 Conclusions  
In this study, a hybrid visual servoing approach was proposed, called Decoupled Hybrid Visual Servoing (DHVS). The proposed method was developed to address the drawbacks of the classical IBVS, PBVS and HVS approaches, and to improve their performances. In the DHVS method, three rotations and translations along the \(Z\)-axis have been decoupled from the image-Jacobian and controlled by 3D reconstruction of visual features. Instead of using a constant gain, adaptive gains were used to reduce the convergence time. The damped least square approach was used to smooth out the discontinuities and reduce the effect of robot singularities. In addition, image singularities generated by translation in the \(Z\)-axis, as well as rotations, are avoided in the proposed DHVS method. The reason is that the non-linear independent columns and rows, which cause the interaction matrix to lose rank, will be removed by decoupling the image Jacobian. It was found that the robot functionality increased during VS by defining manipulability as a secondary task,  
Figure 10: DHVS method is used for tracking the visual features attached to a Lithium-ion battery. Manipulability is considered as a secondary task for the controller. By using the proposed DHVS, the robot arm performs the task of sorting the battery; **(a)** the robot will follow the visual features of the object online; the tracked path of each feature is shown in the camera screen. **(b)** The robot goes down straight to detect the surface by the force feedback. **(c,d)** The object has been lifted by the vacuum suction gripper, and then released the battery in the corresponding basket. **(e)** The feature errors converged to zero during Visual Servoing. **(f)** The force value in the \(Z\)-axis for detecting the object surface.  
and solving redundancy resolution with the classical projection operator process. The proposed method not only has an optimised solution for the robot EE trajectory, but it also takes into account the image-space optimised trajectories of the features. Moreover, it is less likely to lose the object from the camera FOV in comparison with PBVS and HVS methods, and it is more robust to camera calibrations compared to PBVS. The proposed DHVS method has been compared with other VS methods in simulation and validated in the real worldSimulation and experimental results suggested that DHVS method has better performance in tracking the visual features in comparison with the other VS methods. In future study, Deep Neural Networks will be used to extract the features and to overcome the complexity of feature selection.  
Not applicable.  
The video that supports the findings of this study (Improving the manipulability of a redundant arm using Decoupled Hybrid Visual Servoing) is openly available in Figshare ([[https://figshare.com/articles/media/Improving_the_manipulability_of_a_redundant_arm_using_Decoupled_Hybrid_Visual_Servoing/17040620](https://figshare.com/articles/media/Improving_the_manipulability_of_a_redundant_arm_using_Decoupled_Hybrid_Visual_Servoing/17040620)]([https://figshare.com/articles/media/Improving_the_manipulability_of_a_redundant_arm_using_Decoupled_Hybrid_Visual_Servoing/17040620](https://figshare.com/articles/media/Improving_the_manipulability_of_a_redundant_arm_using_Decoupled_Hybrid_Visual_Servoing/17040620)) (accessed on 23 November 2021)) with doi (10.6084/m9.figshare.17040620). The authors declare that they have no conflict of interest.