New trends on digitisation of complex engineering drawings

Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that may provide rich source of information for industries. Analysing these drawings often requires applying a set of digital image processing methods to detect and classify symbols and other components. Despite the recent significant advances in image processing, and in particular in deep neural networks, automatic analysis and processing of these engineering drawings is still far from being complete. This paper presents a general framework for complex engineering drawing digitisation. A thorough and critical review of relevant literature, methods and algorithms in machine learning and machine vision is presented. Real-life industrial scenario on how to contextualise the digitised information from specific type of these drawings, namely piping and instrumentation diagrams, is discussed in details. A discussion of how new trends on machine vision such as deep learning could be applied to this domain is presented with conclusions and suggestions for future research directions

Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings.

The demand for digitisation of complex engineering drawings becomes increasingly important for the industry given the pressure to improve the efficiency and time effectiveness of operational processes. There have been numerous attempts to solve this problem, either by proposing a general form of document interpretation or by establishing an application dependant framework. Moreover, text/graphics segmentation has been presented as a particular form of addressing document digitisation problem, with the main aim of splitting text and graphics into different layers. Given the challenging characteristics of complex engineering drawings, this paper presents a novel sequential heuristics-based methodology which is aimed at localising and detecting the most representative symbols of the drawing. This implementation enables the subsequent application of a text/graphics segmentation method in a more effective form. The experimental framework is composed of two parts: first we show the performance of the symbol detection system and then we present an evaluation of three different state of the art text/graphic segmentation techniques to find text on the remaining image

Symbols classification in engineering drawings.

Technical drawings are commonly used across different industries such as Oil and Gas, construction, mechanical and other types of engineering. In recent years, the digitization of these drawings is becoming increasingly important. In this paper, we present a semi-automatic and heuristic-based approach to detect and localise symbols within these drawings. This includes generating a labeled dataset from real world engineering drawings and investigating the classification performance of three different state-of the art supervised machine learning algorithms. In order to improve the classification accuracy the dataset was pre-processed using unsupervised learning algorithms to identify hidden patterns within classes. Testing and evaluating the proposed methods on a dataset of symbols representing one standard of drawings, namely Process and Instrumentation (P&ID) showed very competitive results

Symbol Recognition: Current Advances and Perspectives

Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

The Parametric Facade: Optimization in Architecture through a Synthesis of Design, Analysis and Fabrication

Modular building systems that use only prefabricated parts, sometimes known as building “kits”, first emerged in the 1830s and 1840s in the form of glass and iron roof systems for urban transportation and distribution centers and multi-storey facade systems. Kit systems are still used widely today in the form of curtain wall assemblies for office and condominium towers, yet in all this time the formal flexibility of these systems (their ability to form complex shapes) has not increased greatly. This is in large part due to the fact that the systems still rely on mass-produced components. This lack of flexibility limits the degree to which these systems can be customized for particular contexts and optimized for such things as daylighting or energy efficiency. Digital design and fabrication tools now allow us to create highly flexible building facade systems that can be customized for different contexts as well as optimized for particular performance objectives. This thesis develops a prototype for a flexible facade system using parametric modeling tools. The first part of the thesis looks at how parametric modeling can be used to facilitate building customization and optimization by integrating the acts of design, analysis, fabrication and construction. The second part of the thesis presents the facade system prototype and documents key aspects of its development. The facade system is modeled in Grasshopper 3D, a parametric modeling plug-in for Rhinoceros 3D. The model has built-in analysis tools to help the user optimize the facade for daylighting, energy efficiency, or views within any given context, as well as tools that alert the designer when fabrication or construction constraints are being violated

Constraint-Based Graphic Statics - A geometrical support for computer-aided structural equilibrium design

This thesis introduces “constraint-based graphic statics”, a geometrical support for computer-aided structural design. This support increases the freedom with which the designer interacts with the plane static equilibriums being shaped. Constraint-based graphic statics takes full advantage of geometry, both its visual expressiveness and its capacity to solve complex problems in simple terms. Accordingly, the approach builds on the two diagrams of classical graphic statics: a form diagram describing the geometry of a strut-and-tie network and a force diagram vectorially representing its inner static quilibrium. Two new devices improve the control of these diagrams: (1) nodes — considered as the only variables — are constrained within Boolean combinations of graphical regions; and (2) the user modifies these diagrams by means of successive operations whose geometric properties do not at any time jeopardise the static equilibrium of the strut-and-tie network. These two devices offer useful features, such as the ability to describe, constrain and modify any static equilibrium using purely geometric grammar, the ability to compute and handle multiple solutions to a problem at the same time, the ability to switch the hierarchy of constraint dependencies, the ability to execute dynamic conditional statements graphically, the ability to compute full interdependency and therefore the ability to remove significantly the limitations of compass-and-straightedge constructions and, finally the ability to propagate some solution domains symbolically. As a result, constraint-based graphic statics encourages the emergence of new structural design approaches that are highly interactive, precognitive and chronology-free: highly interactive because forces and geometries are simultaneously and dynamically steered by the designer; precognitive because the graphical region constraining each points marks out the set of available solutions before they are even explored by the user; and chronology-free because the deductive process undertaken by the designer can be switched whenever desired

Plugin practice: recasting modularity for architects

Contemporary digital design practice is reframing a creative dialogue between design and making. Empowered by an increasingly seamless interface between data and material, the domain of the architect is expanding to engage diverse processes across design and fabrication. New practices of prototyping are emerging in which architects creatively extend opportunities for custom production, exploring relationships of form, material, fabrication, and aspects of performance. This research is driven by project work spanning such a broad domain across design and fabrication, through which I have developed a series of prototypes. In these projects I have created, used and appropriated numerous tools and techniques. In this dissertation, I focus on the ways in which I engage with such a diverse toolset, addressing the workflows of projects in order to frame a modularity of process. This modularity operates across multiple scales, from simple functions to more complex systems, and to varying degrees, from discrete elements to fuzzier arrangements. It is not derived from formulas for design but is instead grounded in expertise and experience. It emerges in response to specific demands for resilience and flexibility and frames a practice in which we plug together diverse processes to enable design and prototyping for architecture. The first contribution of this doctorate is to demonstrate a modularity of process and highlighting its role at multiple scales through a set of diagrams. Furthermore, I frame a series of implications of this modularity of process for architecture practice. Modularity is here more than just a means of organisation across design and fabrication. Nor is it employed to improve efficiency, as it is in some areas. Rather this modularity of process is important to enabling the generation and control differentiation, collaboration across fields of knowledge, and exploration of interdependent design criteria. These underpin a plugin practice in which designers can interrogate the ways we calibrate process and outcome, and create and reuse diverse forms of knowledg

Autocalibrating vision guided navigation of unmanned air vehicles via tactical monocular cameras in GPS denied environments

This thesis presents a novel robotic navigation strategy by using a conventional tactical monocular camera, proving the feasibility of using a monocular camera as the sole proximity sensing, object avoidance, mapping, and path-planning mechanism to fly and navigate small to medium scale unmanned rotary-wing aircraft in an autonomous manner. The range measurement strategy is scalable, self-calibrating, indoor-outdoor capable, and has been biologically inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals (particularly bats), designed to assume operations in previously unknown, GPS-denied environments. It proposes novel electronics, aircraft, aircraft systems, systems, and procedures and algorithms that come together to form airborne systems which measure absolute ranges from a monocular camera via passive photometry, mimicking that of a human-pilot like judgement. The research is intended to bridge the gap between practical GPS coverage and precision localization and mapping problem in a small aircraft. In the context of this study, several robotic platforms, airborne and ground alike, have been developed, some of which have been integrated in real-life field trials, for experimental validation. Albeit the emphasis on miniature robotic aircraft this research has been tested and found compatible with tactical vests and helmets, and it can be used to augment the reliability of many other types of proximity sensors