# Grasping Assisting Algorithm in Tele-Operated Robotic Gripper
## 5 Results Analysis and Discussion
### Discussion  
Based on the results we obtained, we detected a general improvement in the grasping performance of the human operators. Table 6 compares the performance before using the algorithm and after using it. Using our algorithm, we saw a general improvement in performance of power grasping, by 42%-125%. However, for the case of precision grasping only negligible changes in performance were detected, ranging between \(-0.04\%\) and \(0.01\%\) performance change (presented in Table 6). Individual changes in performance are presented in Figure 11.  
Some of the participants improved drastically while others did not. Comparing the performance against the optimal grasp obtained in Section 5.1, we see that while some of the test subjects reached a better score than the algorithm, more than half of the subjects improved their performance by more than 50% in relation to the score obtained by the algorithm. While a general improvement in the power grasp performances was noticed, we did not see major changes in the precision grasps, even for objects that are delicate to handle (tweezers). In practice, this means that the distance from the origin of the grasp wrench space to the farthest vertex of the convex hull remained the same. A possible explanation is that there will always be one contact point where the force exerted has to be of a minimal size (to both sustain force-closure and counter the self weight of the object). Looking at the results established the hypothesis that human operator performance is influenced by the following parameters:  
* Age--older subjects had lower initial scores and lower improvement measures in comparison to younger subjects. It is possible that teaching older people how to grasp different objects is therefore a harder task than teaching young subjects. * Working experience--unlike our initial hypothesis that experienced subjects that have many years of training with grasping tools in uncomfortable environments would preform better, we could not discern their performance in relation to other subjects' performances. Our conclusion is that working experience has no influence on performance with tele-operated devices. * Intuition with tele-operated system--subjects who had previous experience with the specific experimental system or with similar gear (playing video games with controllers) showed better results. * Object/type of grasp--from the results we detected that the best performances were obtained with the screwdriver, a convenient tool, and not with the heavy hammer or the delicate tweezers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Object**} & \multicolumn{2}{c}{\(\overline{Q}\) **before [N]**} & \multicolumn{2}{c}{\(\overline{Q}\) **after [N]**} & \multicolumn{2}{c}{**Improvement (\%)**} \\ \cline{2-7}  & **Power** & **Precision** & **Power** & **Precision** & **Power** & **Precision** \\ \hline Hammer & 2.72 & 206.57 & 3.81 & 214.95 & 42.5 & \(-0.04\) \\ \hline Screwdriver & 2.48 & 19.05 & 4.76 & 19.29 & 125.2 & \(-0.006\) \\ \hline Tweezers & 1.48 & 41.42 & 3.53 & 43.56 & 43.13 & 0.01 \\ \hline \end{tabular}
\end{table} 
Table 6: Grasp quality improvement with and without assisting algorithm. Figure 11: Improvement per subject in power grasp. To conclude our discussion, we would wish to compare our algorithm with a few other algorithms presented in this paper [9; 16; 17; 18]. A comparison is presented in Table 7.  
The algorithm proposed in this paper is intended to grasp specific pre-defined objects, and thus does not require extensive initial information (although it could be implanted with an object database), in comparrison to algorithms using a large database of objects. It is not gripper-specific due to the non-relevance of the gripper's geometry, which is relevant in other algorithms, and can be implemented with an infinite number of grippers.