Abstract

Abstract
This paper addresses the concept of Industry 4.0 from the perspective of the molds industry, a key industry in today's industrial panorama. With its constant modernization, several technologies have been introduced, in particular regarding machining equipment. With each brand and model requiring different (proprietary) interfaces and communication protocols, this technological diversity renders the automatic interconnection with production management software extremely challenging. In this paper a methodology to build monitoring solutions for machining devices is defined, based on the main equipment and operations used by molds industry companies. For a standardized approach, OPC UA is used for high-level communication between the various systems. As a key result of this paper, and given the variety of monitoring systems and communication protocols, the developed approach combines various different machine interfaces on a single system, in order to cover a relevant subset of machining equipment currently in use by the molds industry. This kind of all-in-one approach will give production managers access to the information needed for a continuous monitoring and improvement of the entire production process. (C) 2021 The Authors. Published by Elsevier B.V.

Abstract
Although advances have been made in reducing the time needed for manhole inspection, the procedure is still mostly done manually, with workers having to enter and visually assess the areas being inspected. There is also a growing need to have these structures inspected regularly, in order to prevent casualties and services interruption, as well as the higher cost of rebuilding instead of repairing these structures, which is possible only if pathologies are identified at early stages. This situation renders the task a good target for automation. This paper reviews a set of existing manhole, tunnel and duct inspection systems to ascertain the main features required for the task, as well as the technologies currently used. Most of the present-day solutions are rather expensive and cumbersome, requiring the deployment of relatively heavy equipment and specialized personnel to operate them. With the recent development of laser range sensors and depth (RGBD) cameras with small form factors and weights, the development of solutions with higher portability and lower cost become feasible. Such a solution could improve considerably the rate at which manholes are inspected, and the technology could be used to generate textured models to be analyzed and reported by a remotely located specialist, both online and offline. The work presented here lays the ground for the development of such a system in our research group who has been working on low-cost systems for the generation of 3D textured models for automated inspection. © 2021, Springer Nature Switzerland AG.

Abstract
In a context of greater complexity of Smart Factories, the commissioning time for automated systems needs to be shortened. The use of virtual commissioning tools is a good contribution to achieve this goal. Ideally, those tools should be part of a virtual engineering environment sharing same virtual model, the digital twin, through the complete lifecycle of the automated system, namely the project, simulation, implementation and execution/monitoring/supervision and, eventually decommissioning phases. Such vision includes a digital twin with a broader use, which is consistent with the real system and one that can be used after the early design and commissioning phases. Finding a complete set of tools able to comply with the above requirements can be extremely challenging. In this paper we explore the use of the ABB RobotStudio software combined with the OPC UA standard with this vision in mind. Methodologies were defined to integrate both new generation and legacy equipment, as well as robot controllers and guidelines for equipment development. A key result of this work is the development of a set of virtual engineering tools and methodologies based on OPC UA and implemented using RobotStudio in order to accomplish the complete lifecycle support of an automated system, from the project and simulation phases, to the monitoring and supervision phases, suitable for integration in Industry 4.0 factories. Results are described for a test scenario with different devices.

Abstract
Autonomous driving is currently a widely researched topic worldwide. With a large research effort being taken by industrial research units in the automotive sector, it is no longer exclusive to academic research labs. Essential to this ongoing effort towards level-5 vehicle autonomy, are the sensors used for tracking and detection, mainly lasers, radars and cameras. Most of the cameras for automotive application systems use wide-angle or fish-eye lens, which present high distortion levels. Cameras need to be calibrated for correct perception, particularly for capturing geometry features, or for distance-based calculations. This paper describes a case-study concerning monocular camera calibration for a small scale autonomous driving vehicle vision system. It describes the fundamentals on camera calibration and implementation, with results given for different lenses and distortion models. The aim of the paper is not only to provide a detailed and comprehensive review on the application of these calibration methods, but to serve also as a reference document for other researchers and developers starting to use monocular vision in their robotic applications.