In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works.

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.

Most industrial companies (e.g., SMEs or start-ups) have very limited assets (time, human, and financial efforts, etc.) for the development of their R&D projects. To be competitive, these organizations must carefully evaluate the feasibility of their R&D projects and select the one that best suits their business priorities and their needs. Introducing new technologies can provide a significant competitive advantage, but it also involves risk. Moreover, in accessing public funding, the maturity level of a technology is one of the factors to be considered in terms of access to specific calls for proposals. In recent times, one of the most common measures to assess the maturity of a technology is based on the concept of “technology readiness level” (TRL), which allows the classification of the technology according to its maturity (research, development, or market readiness of the solution). But it has some drawbacks related to the SMEs’ lack of specialized resources and the subjectivity of the current assessing methods. Determining the TRL of a given technology in an objective way, based on indicators, would be of great help for the formulation of R&D projects. This paper considers whether it is possible to use existing methodologies for the determination of the TRL from indicators in organizations with limited access to bibliographic resources. Based on the state-of-the-art, modifications of the methodologies will be proposed to make them applicable to organizations with fewer resources. The outcome of this study will form the basis of a novel approach to achieve a more appropriate methodology for these organizations.