# Vision-Based Robotic Grasping of Reels for Automatic Packaging Machines
## 5 Experiments and Results  
To test the efficacy of the proposed methods, we assigned the mobile robot the task of picking each reel many consecutive times. Specifically, the operation consists in approaching the wagon, scanning the targeted pile from a predefined perspective (discussed in Section 2) and, if found, inserting the gripper in its core for an inside grasping, as shown in Figure 10.  
Figure 9: Method selection flowchart.  
The pose estimation is considered successful if no issues are detected during the insertion phase. In fact, once the reel pose is known, the robot moves its tool-center point (TCP) (in this case, located at the tip of the gripper fingers) 10 cm in front of its center, and, to insert the gripper fingers, moves linearly toward the reel in the \(Z\)-direction of its reference frame (i.e., the longitudinal axis of the cylinder that represents the reel core) until contact. Because the thickness of the reel is known, given the predefined approach distance and the geometry of the gripper, it is possible to guess the expected length of the path up to flushing. If the estimation is not exact, due to the little clearance between the size of the gripper fingers and the diameter of the reel, the gripper will hit the reel before running the whole insertion distance, resulting in a failure. On the contrary, if a contact is not detected beyond the expected insertion length, an error is raised.  
The wagon approach operation introduces a small uncertainty due to the positioning error of the mobile platform with respect to the stationary wagon, typically less than 1 cm in position and \(2^{\circ}\) in orientation. This inconvenience is exploited here to enhance the robustness of the process against small changes in the resulting framing perspective, which is computed in the mobile robot frame. Moreover, reels at different locations on the shelf, from the one at the front to the one at the back, were used during the test to avoid biases derived from their position in the pile.  
Table 2 shows the results of the whole experimental campaign in terms of computational time and final outcome.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Reel Type** & **Total Trials** & **Average Time** & **Maximum Time** & **Failures** \\ \hline _Paper_ & 109 & 0.749 s & 0.857 s & 0 \\ _Tags_ & 118 & 0.786 s & 1.445 s & 1 \\ _Outer Envelope_ & 55 & 3.831 s & 4.392 s & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiments results.  
Figure 10: Picking preliminary operations.  
Figure 11: Computational times for reel-core-pose estimation. The dashed line represents the average time and the red marker the maximum time experienced (see Table 2).  
The contour method was successfully employed in all cycles of paper-reel and tag-reel detection, except for a single iteration in the latter one, in which none of the proposed approaches led to success. For the outer-envelope reel, instead, the Hough method proved to be the most suitable given the zero failures achieved. As expected, the contour method is the fastest, allowing the system to provide a pose estimate in less than 1 s on average, as shown in Figure 11. The largest values are related to an adverse light condition that pushed the algorithm to autotune the parameters before computing a valid response, as described in Section 3.1. The Hough method, despite being very effective and robust, is also the slowest by a large amount. The iterative procedure that looks over all possible combinations in the given (reduced) search space introduces a computational burden which is hard to avoid. As a consequence, the time constraints for this particular reel could not be satisfied (the target was 3.0 s) even if by a relatively small amount on average. However, the outer-envelope reel is the one which takes longer to be consumed by the automatic machine. Therefore, a little extra time is still acceptable as it does not significantly impact the overall reel-changing time.