Intelligent automation, including robotics, is one of the current trends in the manufacturing industry in the context of “Industry 4.0”, where cyber-physical systems control the production at automated or semi-automated factories. Robots are perfect substitutes for a skilled workforce for some repeatable, general, and strategically-important tasks. However, this transformation is not always feasible and immediate, since certain technologies do not provide the required degree of flexibility. The introduction of collaborative robots in the industry permits the combination of the advantages of manual and automated production. In some processes, it is necessary to incorporate robots from different manufacturers, thus the design of these multi-robot systems is crucial to guarantee the maximum quality and efficiency. In this context, this paper presents a novel methodology for process automation design, enhanced implementation, and real-time monitoring in operation based on creating a digital twin of the manufacturing process with an immersive virtual reality interface to be used as a virtual testbed before the physical implementation. Moreover, it can be efficiently used for operator training, real-time monitoring, and feasibility studies of future optimizations. It has been validated in a use case which provides a solution for an assembly manufacturing process.

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Abstract. 3D object reconstruction from single image has been a noticeable research trend in recent years. The most common method is to rely on symmetries of real-life objects, but these are hard to compute in practice. However, a large class of everyday objects, especially when manufactured, can be generated by extruding a 2D shape through an extrusion axis. This paper proposes to exploit this property to acquire 3D object models using a single RGB + Depth image, such as those provided by available low-cost range cameras. It estimates the hidden parts by exploiting the geometrical properties of everyday objects, and both depth and color information are combined to refine the model of the object of interest. Experimental results on a set of 12 common objects are shown to demonstrate not only the effectiveness and simplicity of our approach, but also its applicability for tasks such as robotic grasping.

This paper presents a fast method for acquiring 3D models of unknown objects lying on a table, using a single viewpoint. The proposed algorithm is able to reconstruct a full model using a single RGB + Depth image, such as those provided by available low-cost range cameras. It estimates the hidden parts by exploiting the geometrical properties of everyday objects, and combines depth and color information for a better segmentation of the object of interest. A quantitative evaluation on a set of 12 common objects shows that our approach is not only simple and effective, but also the reconstructed model is accurate enough for tasks such as robotic grasping.

Abstract. 3D object reconstruction from single image has been a noticeable research trend in recent years. The most common method is to rely on symmetries of real-life objects, but these are hard to compute in practice. However, a large class of everyday objects, especially when manufactured, can be generated by extruding a 2D shape through an extrusion axis. This paper proposes to exploit this property to acquire 3D object models using a single RGB + Depth image, such as those provided by available low-cost range cameras. It estimates the hidden parts by exploiting the geometrical properties of everyday objects, and both depth and color information are combined to refine the model of the object of interest. Experimental results on a set of 12 common objects are shown to demonstrate not only the effectiveness and simplicity of our approach, but also its applicability for tasks such as robotic grasping.