Discrete event simulation and virtual reality use in industry: new opportunities and future trends

This paper reviews the area of combined discrete event simulation (DES) and virtual reality (VR) use within industry. While establishing a state of the art for progress in this area, this paper makes the case for VR DES as the vehicle of choice for complex data analysis through interactive simulation models, highlighting both its advantages and current limitations. This paper reviews active research topics such as VR and DES real-time integration, communication protocols, system design considerations, model validation, and applications of VR and DES. While summarizing future research directions for this technology combination, the case is made for smart factory adoption of VR DES as a new platform for scenario testing and decision making. It is put that in order for VR DES to fully meet the visualization requirements of both Industry 4.0 and Industrial Internet visions of digital manufacturing, further research is required in the areas of lower latency image processing, DES delivery as a service, gesture recognition for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets

Future directions for the development of Virtual Reality within an automotive manufacturer

Virtual Reality (VR) can reduce time and costs, and lead to increases in quality, in the development of a product. Given the pressure on car companies to reduce time-to-market and to continually improve quality, the automotive industry has championed the use of VR across a number of applications, including design, manufacturing, and training. This paper describes interviews with 11 engineers and employees of allied disciplines from an automotive manufacturer about their current physical and virtual properties and processes. The results guided a review of research findings and scientific advances from the academic literature, which formed the basis of recommendations for future developments of VR technologies and applications. These include: develop a greater range of virtual contexts; use multi-sensory simulation; address perceived differences between virtual and real cars; improve motion capture capabilities; implement networked 3D technology; and use VR for market research

Delay Tolerant Networking over the Metropolitan Public Transportation

We discuss MDTN: a delay tolerant application platform built on top of the Public Transportation System (PTS) and able to provide service access while exploiting opportunistic connectivity. Our solution adopts a carrier-based approach where buses act as data collectors for user requests requiring Internet access. Simulations based on real maps and PTS routes with state-of-the-art routing protocols demonstrate that MDTN represents a viable solution for elastic nonreal-time service delivery. Nevertheless, performance indexes of the considered routing policies show that there is no golden rule for optimal performance and a tailored routing strategy is required for each specific case

Object Pose Detection to Enable 3D Interaction from 2D Equirectangular Images in Mixed Reality Educational Settings

In this paper, we address the challenge of estimating the 6DoF pose of objects in 2D equirectangular images. This solution allows the transition to the objects’ 3D model from their current pose. In particular, it finds application in the educational use of 360° videos, where it enhances the learning experience of students by making it more engaging and immersive due to the possible interaction with 3D virtual models. We developed a general approach usable for any object and shape. The only requirement is to have an accurate CAD model, even without textures of the item, whose pose must be estimated. The developed pipeline has two main steps: vehicle segmentation from the image background and estimation of the vehicle pose. To accomplish the first task, we used deep learning methods, while for the second, we developed a 360° camera simulator in Unity to generate synthetic equirectangular images used for comparison. We conducted our tests using a miniature truck model whose CAD was at our disposal. The developed algorithm was tested using a metrological analysis applied to real data. The results showed a mean difference of 1.5° with a standard deviation of 1° from the ground truth data for rotations, and 1.4 cm with a standard deviation of 1.5 cm for translations over a research range of ±20° and ±20 cm, respectively