## 5 Discussion  
The majority of Next-Best-View methods applied to industrial robots with hand-eye camera systems typically focus on 3D object modeling within a localized robot workspace. In contrast, our proposed NBV approach provides a methodology for exploring the entire  
Figure 11: The explored volume of the workspace during the initial phase in the fourth experiment. Figure 12: (**a**) The fourth experiment setup; (**b**) the occupied and unknown space after the exploration phase. reachable workspace of the robotic arm. The resulting comprehensive environment model is a key enabler for Industry 4.0 applications, holding significant potential for advanced industrial scenarios requiring detailed scene understanding. These include the development of accurate digital twins for offline programming and simulation, enhanced collision-free path planning for dynamic manufacturing environments, and fostering safer human-robot collaboration through complete workspace awareness. The theoretical novelty of our approach is rooted in its combination of a potential field for guiding view selection with an information gain metric based on collision detection. In contrast to the common volumetric NBV methods, which use ray-casting for information gain estimation, our NBV uses a collision check between the view frustum and unknown voxels. This provides a computationally efficient proxy for information gain that is well-suited for on-robot deployment. Through a series of three distinct simulation experiments and a final real-world validation, including scenarios with varying environmental complexity and one featuring a realistic robotic workcell, we demonstrated the proposed method's functionality. The experiments systematically studied the impact of different weightings in the fitness function. Based on these results, we can conclude the following behaviors, which are summarized in Table 4.  
Prioritizing information gain generally leads to a faster volumetric search of the space. However, this can result in candidate views being dispersed far from the current robot pose. In complex environments, this increases the risk of trajectory planning failures or encountering unreachable candidates, which might be prematurely discarded but could become accessible later from a different vantage point. This challenge is more pronounced when a large proportion of the environment remains unknown after the initial exploration phase. Conversely, emphasizing the minimization of travel distances (translation and rotation) tends to select closer candidates. This facilitates simpler trajectory planning, reduces failures, and significantly improves the overall path quality and total distance traveled. The trade-off is a potential increase in the number of iterations required, especially in simpler environments. A high preference for minimizing rotational differences between the current and candidate views did not consistently yield better trajectory quality or lower total orientation changes (OC criterion). This is because a candidate with minimal orientation change might be distant, leading to complex paths with more frequent reorientations. However, a modest weight for rotation minimization can be beneficial, acting as a tie-breaker between candidates otherwise similarly rated by information gain and travel distance.