# Vision-Based Robotic Grasping of Reels for Automatic Packaging Machines
## 6 Conclusions  
In this article, we presented a method to robustly estimate the 3D pose of a raw-material reel from a single image and prior information for picking purposes in an industrial environment. The work was justified by the industrial need of implementing a vision-based system for allowing a mobile robot equipped with a manipulator to autonomously pick three different types of reels from a deposit, and load them onto an automatic packaging machine. The envisioned solution was implemented because it was easy to share, document, maintain, and it was deployable on an industrial PC with limited power resources. Three methods for detecting a single ellipse out of a picture framing the reel core were proposed (i.e., contour, RBrown, and RHT), and the results coming from an extensive experimental campaign showed how, following an optimal selection policy, the success rate touched 99.6%. We showed how the different photometric characteristics of each reel affect the efficacy of each method. In particular, the most selected method was the contour one, used for the detection of both paper and tag reel, which presented the highest contrast between the reel core and the material wrapped around it. For the outer-envelope reel, which has the lowest contrast and is highly reflective, the RHT method proved to be the most effective, but also the one with the slowest performance. The given approach is indeed influenced by a number of tuning parameters that can be manually changed according to the features of the reel under consideration. The ellipse found in the image, which indicates the contour of the reel core, together with the prior knowledge of its real-world dimensions, is used to calculate the position of the reel center and the direction of its longitudinal axis, both expressed in the robot frame. This data is then sent to the robot to execute the picking of the object by inside grasping. All specifications were met, with the only exception of time constraints for the case of the outer-envelope reel. Due to the intrinsic computational burden of the RHT calculation, in fact, the overall estimation process exceeds the target maximum execution time by 0.831 s on average. A possible improvement to speed up the process is porting the same software implementation from Python language to a compiled programming language, such as C++. Additionally, a better subsampling strategy and better filters may help reduce the number of iterations executed by the algorithm. Lastly, without limitations on the hardware and, thus, a better-performing computing unit, more sophisticated approaches based on the most recent DL techniques may be pursued and lead to similar or better results. Conceptualization, S.C.; methodology, S.C.; software, S.C.; validation, S.C. and M.C. ; formal analysis, S.C.; investigation, S.C.; resources, S.C.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, M.C. ; visualization, S.C.; supervision, M.C. ; project administration, M.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. \begin{table}
\begin{tabular}{l c c l} \hline \hline
**Name** & **Value** & **Used by** & **Description** \\ \hline _min\_major\_axis_ & 1000 & H,R & See Table 3. \\ _max\_major\_axis_ & 1400 & H,R & See Table 3. \\ _min\_aspect\_ratio_ & 0.7 & H,R & See Table 3. \\ _max\_aspect\_ratio_ & 1.0 & H,R & See Table 3. \\ _rotation_ & 70 & H,R & See Table 4. \\ _rotation\_spau_ & 15 & H,R & See Table 4. \\ _min\_edge\_size_ & 100 & H,R & Minimum candidate edge length in pixels. \\ _sigma0_ & 4.0 & H,R & Initial blurring value for smoothing the image before edge detection. \\ _min\_feat_ & 4 & H,R & Minimum number of detected edges. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experiments settings for outer-envelope reel. Informed Consent Statement: Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** The authors would like to thank the Research & Innovation dept.