## 4 Experiments
### Experiment IV: Vision for Multi-Robot Grasping  
The multi-robot grasping determination experiment has been performed using the TWINBOT underwater training simulation server as input. The vision module gets the current image of the pipe as input, taken from the on-hand camera, and applies three actions: (1) object recognition (or ROI provided by the user, see Figure 21), (2) object tracking and visual servoing control to center the model in the image (see Figure 22), and (3) grasping determination algorithm for stable robot team manipulation (see Figure 23). \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **CERN** & \multicolumn{2}{c}{**Underwater**} \\ \cline{2-4}  & **4G** & **RF** & **Acoustic** \\ \hline Bit rate (kbps) & 11100 & 1.9 & 3.2 \\ Intrinsic delay (ms) & 20 & 85 \(\pm\) 10.9302 & 443 \(\pm\) 363.853 \\ Jitter (ms) & 15.2 & 13.8806 \(\pm\) 10.16 & 412.99 \(\pm\) 316.894 \\ Prop. Time (ms/m) & 3.3\(\times 10^{-6}\) & 3.3\(\times 10^{-6}\) & 0.67 \\ Max. distance (m) & - & 5 & 3500 \\ Min. distance (m) & 0 & 0.5 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results obtained in underwater communication experiments. The two first phases of the vision have been developed and tested within the CERNTAURO project (Figure 24). As a matter of fact, the significant contribution of this tracking system is that it enables the recognition of metallic objects, very common in CERN scenarios. The third action (multi-robot grasping determination) is a work in progress decoupled solution based on previous experiments at Jaume I University (UJI) for single robot manipulation. Current efforts are performed to integrate this grasping determination algorithm for the TWINBOT and CERNTAURO real experiments. Grasping determination can be applied to any camera input selected by the operator, belonging to any of the robots. To apply this to a big object, it is necessary to have a more complete view of the object, where several grasps can be offered to the user to be tracked and executed by each particular robot. The computer vision system, which runs on the server side, will be activated from the HRI as a server behaviour. Phase 1 (see Figure 21) uses the Speeded-Up Robust Features (SURF) [41] algorithm to identify the object in the scene. It can also be selected by the user operator or can be recognized through the object recognition module. The second phase uses as input the ROI of the object and applies a tracking-based guidance system (see Figure 22), which overcomes the well-known weakness of feature extractor/descriptor and tracking algorithms such as reescalation, rotation, stability, or partial occlusions, achieving good results on harsh conditions either in metallic or underwater scenarios. This algorithm, which is a novel contribution from CERN [42], calculates the distance to the target by using a single monocular on-hand camera based on the Sinus Theorem (see Equation (3)), which is fully deployed in [30]. To summarize, the tracking system uses a global ROI which takes control of the correct behaviour of the four sub-trackings responsible for fulfilling the necessities upon hazardous and industrial scenarios. \[DE_{x,y}=-T_{x,y}\cdot\sin(\alpha_{x,y})/\sin(\pi-\alpha_{x,y}-\beta_{x,y}) \tag{3}\]  
The third phase has been studied in a decoupled manner using underwater training simulation, considering the multi-robot grasping determination procedure and not yet the multi-robot execution to avoid collisions. Once the target has been approached appropriately using on-hand visual servoing, the contour of the object (i.e., pipe) is extracted and analysed using a saliency detection-based algorithm [43]. Then, the grasping determination algorithm explained in [44] is applied, which gives the geometrical best-fit ellipse analysis (minimum and maximum inertia axis: \(I_{min}\),\(I_{max}\)) and grasping points for a stable single-arm robot. Then, the pipe contour is divided into two sections, considering  
Figure 21: Vision phase 1: object identification (automatic or provided by the user).