# Grasping Assisting Algorithm in Tele-Operated Robotic Gripper
## 5 Results Analysis and Discussion
### Optimal Algorithm Results  
To achieve our optimal results for each object according to the classifications defined (\"power grasp\" vs. \"precision grasp\"), we required a list of relevant parameters for our computation.  
Our algorithm received a point cloud collection of the object's surface \(\mathcal{S}\), a theoretical maximal normal force at each contact point \(F_{N,max}\), estimated to be a different value for each type of grasp (\(F_{N}=5.94\) [N] for power grasps and \(F_{N}=1.1*M*g\) [N] for precision grasps), and lastly the coefficient of static friction, based on different values taken from the literature. Due to our uncertainty regarding the estimation of the coefficient of static  
Figure 6: Top view of the test area, compromised of control devices, robotic arm and gripper, computer and TV screen.  
friction and based on the values we encountered, we picked two values of \(\mu_{s}\) in order to be able to also check a stricter case of the grasp presented in Table 2.  
Our algorithm applies concepts from genetic algorithms. While running many simulations with our chosen \(\mu_{s}\), \(F_{n,max}\) we received a large diversity in our optimal quality measure for each of the objects, which suggests we have not found the true optimal grasp for the object.  
A number of explanations for this result are suggested. One of them is the disadvantage of the genetic algorithms due to the possibility to converge to local optimum and not the global optimal solution of the problem [32]. Another explanation is that the set of solutions is not constrained by the kinematics of the gripper, resulting in nonphysical solutions (two contact points too far apart). Lastly, a crossover of two good grasps could still result in a worse grasp, thus increasing the diversity of results.  
We conclude that the genetic algorithm improves the score of the fitness function (meaning that our final proposed grasp is better than our starting grasp) but different iterations of the same algorithm will not necessarily converge to the same solution. Under this assumption we wish to find a good score for our quality measure, understanding that it represents a local maximum of the grasp quality measure function and not a global maxima.  
For each object we simulated \(n=50\) iterations of optimal grasps, testing two different coefficients of static friction, where in each one of these we scanned 300-400 grasps (both force-closure and non force-closure), and then chose the highest power grasp or lowest precision grasp received score. Our \"optimal\" grasp quality measure was better than \(O\approx 10^{4}\) grasps, and therefore we are confident in our results.  
Table 3 shows our results and their diversity. The table displays the coefficient of static friction, the best quality measure, the average quality measure and the standard deviation of the quality measures, where the sample size is \(n=50\).  
\begin{table}
\begin{tabular}{c c  
In Figure 7 we show some of the visual output provided to the test subjects about the different optimal grasps. the suggested points of contact are marked by the red markers on the objects' surfaces.