A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0

Industry 4.0 (also referred to as digitization of manufacturing) is characterized by cyber physical systems, automation, and data exchange. It is no longer a future trend and is being employed worldwide by manufacturing organizations, to gain benefits of improved performance, reduced inefficiencies, and lower costs, while improving flexibility. However, the implementation of Industry 4.0 enabling technologies is a difficult task and becomes even more challenging without any standardized approach. The barriers include, but are not limited to, lack of knowledge, inability to realistically quantify the return on investment, and lack of a skilled workforce. This study presents a systematic and content-centric literature review of Industry 4.0 enabling technologies, to highlight their impact on the manufacturing industry. It also provides a strategic roadmap for the implementation of Industry 4.0, based on lean six sigma approaches. The basis of the roadmap is the design for six sigma approach for the development of a new process chain, followed by a continuous improvement plan. The reason for choosing lean six sigma is to provide manufacturers with a sense of familiarity, as they have been employing these principles for removing waste and reducing variability. Major reasons for the rejection of Industry 4.0 implementation methodologies by manufactures are fear of the unknown and resistance to change, whereas the use of lean six sigma can mitigate them. The strategic roadmap presented in this paper can offer a holistic view of phases that manufacturers should undertake and the challenges they might face in their journey toward Industry 4.0 transition

Asynchronous Visualization of Spatiotemporal Information for Multiple Moving Targets

In the modern information age, the quantity and complexity of spatiotemporal data is increasing both rapidly and continuously. Sensor systems with multiple feeds that gather multidimensional spatiotemporal data will result in information clusters and overload, as well as a high cognitive load for users of these systems. To meet future safety-critical situations and enhance time-critical decision-making missions in dynamic environments, and to support the easy and effective managing, browsing, and searching of spatiotemporal data in a dynamic environment, we propose an asynchronous, scalable, and comprehensive spatiotemporal data organization, display, and interaction method that allows operators to navigate through spatiotemporal information rather than through the environments being examined, and to maintain all necessary global and local situation awareness. To empirically prove the viability of our approach, we developed the Event-Lens system, which generates asynchronous prioritized images to provide the operator with a manageable, comprehensive view of the information that is collected by multiple sensors. The user study and interaction mode experiments were designed and conducted. The Event-Lens system was discovered to have a consistent advantage in multiple moving-target marking-task performance measures. It was also found that participants’ attentional control, spatial ability, and action video gaming experience affected their overall performance

Automation and Control

Advances in automation and control today cover many areas of technology where human input is minimized. This book discusses numerous types and applications of automation and control. Chapters address topics such as building information modeling (BIM)–based automated code compliance checking (ACCC), control algorithms useful for military operations and video games, rescue competitions using unmanned aerial-ground robots, and stochastic control systems

Advancing Robot Autonomy for Long-Horizon Tasks

Autonomous robots have real-world applications in diverse fields, such as mobile manipulation and environmental exploration, and many such tasks benefit from a hands-off approach in terms of human user involvement over a long task horizon. However, the level of autonomy achievable by a deployment is limited in part by the problem definition or task specification required by the system. Task specifications often require technical, low-level information that is unintuitive to describe and may result in generic solutions, burdening the user technically both before and after task completion. In this thesis, we aim to advance task specification abstraction toward the goal of increasing robot autonomy in real-world scenarios. We do so by tackling problems that address several different angles of this goal. First, we develop a way for the automatic discovery of optimal transition points between subtasks in the context of constrained mobile manipulation, removing the need for the human to hand-specify these in the task specification. We further propose a way to automatically describe constraints on robot motion by using demonstrated data as opposed to manually-defined constraints. Then, within the context of environmental exploration, we propose a flexible task specification framework, requiring just a set of quantiles of interest from the user that allows the robot to directly suggest locations in the environment for the user to study. We next systematically study the effect of including a robot team in the task specification and show that multirobot teams have the ability to improve performance under certain specification conditions, including enabling inter-robot communication. Finally, we propose methods for a communication protocol that autonomously selects useful but limited information to share with the other robots.Comment: PhD dissertation. 160 page

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

Construction safety and digital design: a review

As digital technologies become widely used in designing buildings and infrastructure, questions arise about their impacts on construction safety. This review explores relationships between construction safety and digital design practices with the aim of fostering and directing further research. It surveys state-of-the-art research on databases, virtual reality, geographic information systems, 4D CAD, building information modeling and sensing technologies, finding various digital tools for addressing safety issues in the construction phase, but few tools to support design for construction safety. It also considers a literature on safety critical, digital and design practices that raises a general concern about ‘mindlessness’ in the use of technologies, and has implications for the emerging research agenda around construction safety and digital design. Bringing these strands of literature together suggests new kinds of interventions, such as the development of tools and processes for using digital models to promote mindfulness through multi-party collaboration on safet

Automated Trading Systems VS Manual Trading in Forex Exchange Market

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementIn the recent decades, automated trading has been widely used in Forex and Money Markets, as well as in financial markets. This auto trading provided substantial benefits to transaction efficiency. Many trading robots have been created to substitute humans, capable of simulating trading strategies and continuously making profits. Nevertheless, programs cannot reproduce all human behaviour and most robots are over-sensitive, therefore, it is difficult to have the same results as human traders. The study focuses on evaluating the trading machines sensitivity and effectiveness. The economic markets can benefit from the machine in several ways, through continuous operation, increasing diversification, short/term trading opportunities and by forecasting opportunities e. g. currency price changes. The further investigation indicates that the majority of forex trading robots are profitable, in fact, there is a great tendency for curve-fitting or data-mining. There are some impressive robots out there; of course, these systems maintain an advantage and successfully manage risk. The best ones are more about position sizing and cutting losses quickly and less about high win rates. The greater the sensitivity the greater the trading opportunities, but this decreases the performance. This research will contain interviews with experts that will validate the study

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

The progress made by levels 1, 2, and 3 of the Office of Space Station in developing and applying advanced automation and robotics technology is described. Emphasis is placed upon the Space Station Freedom Program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) progress report 10, the flight telerobotic servicer, and the Advanced Development Program. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for the Space Station Freedom