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.

Industrial robots, that are designed to carry out operations fast, repeatedly and accurately, usually have been fixed to one physical location and manipulate objects on the assembly line. However, for several industrial applications, particularly for the applications involving large part manufacturing such as aerospace industry or shipbuilding industry, large parts are worked on in a stationary production cell. In such a production environment, specialized, stationary robotic systems are not economical and a mobile manipulator is desirable. In this paper, we present the systematic architecture designed for a mobile manipulator working together with human co-workers in an unstructured environment. We detail hardware specification of robot as well as software architectures under consideration of safety, efficiency and load balance issues of industrial robotics, in particular for the aerospace manufacturing industry. A configurable graphical user interface has also been presented to test and validate our system design. Two commonly existing exemplary tasks in aerospace manufacturing industry - sealant applying and visual inspection, have been discussed within this systematic architecture.

Infrared imaging is based on the measurement of radiation of an object and its conversion to temperature. A vital parameter of the conversion procedure is emissivity, which defines the capability of a material to absorb and radiate energy. For most applications, emissivity is assumed to be constant. In applications measuring the temperature of objects with high emissivity, this is not problematic, as slight variations in the chosen emissivity value cause only minor changes in the resulting surface temperatures. However, when emissivities are low, as in steel strips, considering emissivity as a constant can lead to significant errors in temperature measurement. To overcome problems generated by variations in emissivity, one solution is to measure temperature where the steel strip forms a wedge, acting as a cavity. In the deepest part of the wedge, emissivity is sufficiently close to one. This work presents a real time image processing system to acquire infrared line scans for steel strips using the wedge method. The proposed system confronts two challenges: extracting infrared line scans in real time from the deepest part of the wedge in rectangular infrared images, and translating pixels from the line scan into real-world units.