## 6 Conclusions  
This paper introduced a novel Next-Best-View (NBV) approach designed for the comprehensive exploration of a robotic arm's entire reachable workspace, a departure from traditional object-centric modeling methods. Our method leverages a potential field to guide view selection and a computationally efficient collision-based metric to estimate information gain. Through a series of three simulation experiments and a final real-world validation, we have demonstrated its effectiveness and systematically analyzed the trade-offs between exploration speed and path efficiency. The key findings indicate that prioritizing information gain accelerates volumetric coverage at the cost of path efficiency, while emphasizing travel distance minimization results in more reliable and shorter trajectories, albeit at a slower exploration pace. Our validation on a physical robotic platform confirmed that a balanced strategy, which moderately penalizes travel cost, provides a robust and practical solution for exploring complex, real-world environments. This work advances the field by providing a practical and computationally lightweight framework for creating comprehensive, on-demand 3D models of industrial workcells. Such models are a critical enabler for Industry 4.0 applications, including the development of accurate digital twins, enhancing collision-free path planning in dynamic settings, and fostering safer human-robot collaboration through complete workspace awareness. Despite its success, this study has limitations. The proposed method was validated primarily in static environments, and its performance relies on a fixed-weight fitness function, which may not be optimal for all stages of exploration. Furthermore, the real-world implementation, which required the robot to pause for each scan, highlighted a key area for advancement: enabling continuous mapping while the robot is in motion. Achieving this would require robust synchronization between the robot's state estimation and the camera's data acquisition to handle motion blur and accurately register point clouds in real-time. Finally, the coarse map resolution used in our validation underscores the need for finer voxel sizes when detailed object reconstruction is required. Future work will focus on addressing these limitations. Our primary goal is to develop an adaptive weighting scheme for the fitness function, allowing the robot to dynamically shift its priorities from efficient travel to detailed scanning as the map becomes more complete. We will also explore more sophisticated strategies for handling trajectory planning failures, such as re-attempting failed candidates or penalizing historically difficult regions. Additionally, we plan to extend the framework to handle dynamic environments by detecting and re-scanning areas with changed occupancy. Finally, we will investigate parallelizing the mapping and planning processes to enable real-time 3D reconstruction synchronized with robot motion, with the goal of migrating the entire system to ROS 2 to leverage its advancements in real-time capabilities and long-term support. Conceptualization, M.D. ([PERSON]), M.D. ([PERSON]) and F.D. ; methodology, M.D. ([PERSON]), J.I., M.D. ([PERSON]) and A.B. ; software, M.D. ([PERSON]) and J.I. ; validation, M.D. ([PERSON]), J.I. and R.M. ; formal analysis, M.D. ([PERSON]), J.I., A.B. and R.M. ; investigation, M.D. ([PERSON]) and M.D. ([PERSON]); resources, M.D. ([PERSON]), J.I., M.D. ([PERSON]) and R.M. ; data curation, M.D. ([PERSON]) and R.M. ; writing--original draft preparation, M.D. ([PERSON]) and R.M. ; writing--review and editing, M.D. ([PERSON]) and J.I. ; visualization, M.D. ([PERSON]), J.I. and R.M. ; supervision, M.D. ([PERSON]) and F.D. ; project administration, F.D. and A.B. ; funding acquisition, F.D. and A.B. ; All authors have read and agreed to the published version of the manuscript. The authors declare no conflict of interest.