# Grasping Assisting Algorithm in Tele-Operated Robotic Gripper
## 6 Conclusions  
In this work we created an assisting algorithm for grasping objects with a tele-operated robotic gripper. The algorithm proposed in this research can be used with a vast number of grippers, and simulate grasping of different objects with different grasping scenarios (low friction, different grasping powers).  
For a given set of objects, with a known surface and a predefined coefficient of static friction, our algorithm attempted different type of grasps, and found an optimal grasp based on quality measures we defined. Optimal grasps were calculated for each object, for both the power and precision types of grasps, and with two different coefficients of static friction (likely case and severe case). For each grasp we created a visual output for our test subjects. The grasp results of the algorithm, while not being the true optimal grasp for a given object, were ranked higher than random grasps of an object, which makes the genetic algorithm a valid optimization method for grasping problems.  
In a series of tests we observed the performance of different operators using a tele-operated system. Each subject was asked to grasp the objects with and without the output from our algorithm, and we compared the results.  
For the case of power grasping we have seen an average improvement of at least 40%. Most of the participants improved their performance, where the younger subjects improved better than most of the older subjects. While some of the subjects were able to perform better than the optimal grasp quality measure calculated, we deduced that following the visual information of the optimal grasp improved the performance of most of the test subjects.  
For the case of precision grasping no noticeable changes in performance for any of the subjects were observed. Our assessment is that there is a minimal threshold of force exerted at the contact point on an object that stays the same for all grasps, and thus the circumscribing sphere of the GWS has minor changes.  
The study presented in this paper does have its limitations. The algorithm is not applicable to grasping of soft objects or grasping with contact areas, due to the physical model implemented. Initial assumptions about the forces and friction have to be assumed in order to receive results. Additionally, while not part of this study, translation of the visual output provided by the algorithm into visual instructions in augmented/virtual reality could drastically increase the quality of assistance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Criteria/Algorithm** & **Our Algorithm** & **Synergy Evaluation** & **Deep Learning** & **Planar OCOG** & **n-Finger OCOG** \\ \hline Objects database & One object & One object & Several objects & Several objects & Many objects \\ \hline Variety of grippers & Not relevant & Specific gripper & 2 fingers grippers & 3 fingers grippers & Not relevant \\ \hline Simulating force & & & & & \\ and friction & Yes & No & No & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 7: Algorithm comparison to selected algorithms.  
To conclude, the tests' results show that the proposed algorithm indeed assists the operator in securing an object with tele-operated devices, in spite of the challenges presented by the problem.