## 4 Experiments
### Fourth Experiment  
To demonstrate the practical applicability of our proposed NBV method, a final experiment was conducted under laboratory conditions at the National Centre of Robotics in Slovakia. The setup was designed to partially replicate a real industrial manufacturing cell, featuring key components, such as a conveyor belt, an automatic tool changer, and part bins, to create a realistically complex and cluttered environment. This experiment served as a proof-of-concept to validate that the planner, developed and tested in simulation, can successfully guide a real robot to explore a complex, initially unknown environment. Based on the simulation results, which identified it as a well-balanced and efficient configuration, the fitness function weights were set to IG1_T025_R01. To prioritize rapid demonstration over high-fidelity mapping, the octomap voxel size was set to a coarse 12.5 cm. While this large voxel size risks missing smaller obstacles, it was deemed sufficient for proving the core functionality of the exploration algorithm on real hardware. A significant challenge in the real-world setup was the lack of hardware synchronization between the robot controller and the Kinect v2 camera. To ensure stable point cloud acquisition, the robot was programmed to pause at each planned viewpoint before triggering a scan and updating the map. Due to the absence of a ground-truth model of the workcell, the exploration could not be terminated based on a percentage of volume explored. Instead, the process was terminated manually after the robot had successfully mapped the primary operational areas. The exploration process was successfully executed on the real hardware. The robot first performed an initial scanning phase of 21 views to build a foundational map of its immediate surroundings. Following this, the NBV exploration algorithm took over, autonomously generating and executing trajectories for another 16 views. Figure 11 The explored volume of the workspace during the initial phase in the fourth experiment shows the state of the octomap after the 15/21 scans in the initial phase. While the robot's immediate vicinity is mapped, significant portions of the environment remain unknown, particularly the area behind the conveyor belt and the upper regions of the workcell. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measurement** & **JD [Rad]** & **CPC** & **CD [m]** & **OC [Rad]** & **RDM [m]** \\ \hline IG1\_T0\_R0 & 146.41 & 44.80 & 34.72 & 124.68 & 35.07 \\ IG0\_T1\_R0 & **99.56** & 30.7 & 22.32 & 111.58 & **22.40** \\ IG1\_T025\_R01 & 100.08 & **29.81** & **22.22** & **110.75** & 22.90 \\ IG1\_T05\_R01 & 133.38 & 40.55 & 29.97 & 121.72 & 30.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sums of criteria values computed by [26] in the third experiment. Figure 10: The explored volume of the workspace in each iteration in the third experiment. Figure 12 displays the final map after the 16-view exploration phase. The algorithm successfully guided the robot to explore these previously occluded areas, clearly mapping the full conveyor system, a nearby parts stand with totes, and the tool holders located behind the robot's initial position. It is important to note that the physical tool changer, mounted between the robot flange and the camera, was not explicitly present in the simulation due to the lack of a 3D model. However, its physical dimensions were accounted for by applying a corresponding static transformation to the sensor frame in simulation, ensuring that the robot's reach and the camera's viewpoint accurately reflected the real hardware. Overall, the experiment successfully demonstrated the suitability of the proposed NBV algorithm for real-world application. The robot navigated a complex environment, autonomously identifying and scanning unknown regions to build a comprehensive map. The primary limitation observed was the coarse map resolution due to the large voxel size.