Geometric Segmentation of 3D Scanned Surfaces for Multi-Sensor Coordinate Metrology

In modern industry, multi-sensor metrology methods are increasingly applied for fast and accurate 3D data acquisition. These method typically start with fast initial digitization by an optical digitizer, the obtained 3D data is analyzed to extract information to provide guidance for precise re-digitization and multi-sensor data fusion. The raw output measurement data from optical digitizer is dense unsorted points with defects. Therefore a new method of analysis has to be developed to process the data and prepare it for metrological verification. This article presents a novel algorithm to manage measured data from optical systems. A robust edge-points recognition method is proposed to segment edge-points from a 3D point cloud. The remaining point cloud is then divided into different patches by applying the Euclidean distance clustering. A simple RANSAC-based method is used to identify the feature of each segmented data patch and derive the parameters. Subsequently, a special region growing algorithm is designed to refine segment the under-segmentation regions. The proposed method is experimentally validated on various industrial components. Comparisons with state-of-the-art methods indicate that the proposed method for feature surface extraction is feasible and capable of achieving favorable performance and facilitating automation of industrial components.

Non-linear fabrication: A dialogue between architecture and robotic wire-arc additive manufacture

<p><b>Within architecture the joint is an integral part of a building’s dialogue withits users and audience. Steel joints allow for the extension of membersand fundamental changes between columns and beams. However, dueto the limitation of traditional metal fabrication, highly parametric andcomputational informed designs are constrained by the conditions ofmass production. High output low cost, limiting the highly variable natureof geometrical informed individual joints.</b></p> <p>Through the fabrication of computational informed architectural steelconnection in combination with non-linear geometric segmentation and6-axis robotic wire arc additive manufacture (WAAM), this researchaims to optimise the aesthetic opportunities and practical fabrication oflarge-scale steel architectural joints. Currently, there exists a disconnectbetween the dialogue of metal joints and architectural intent. Throughthe innovative use of non-linear fabrication and member informed multidirectionalslicing, this research aims to construct a closed digital designloop though to fabrication. Utilising the additive fabrication processof WAAM, to construct a mass-customisable metal node which stillmaintains an aesthetic architectural opportunity.</p> <p>The aims of this research are to outline the potential of fabricating masscustomisablesteel joints through the process of non-linear segmentation.</p> <p>Describing the methods and techniques used to fabricate a computationalinformed metal joint through the manufacturing process of WAAM.</p>

Non-linear fabrication: A dialogue between architecture and robotic wire-arc additive manufacture

<p><b>Within architecture the joint is an integral part of a building’s dialogue withits users and audience. Steel joints allow for the extension of membersand fundamental changes between columns and beams. However, dueto the limitation of traditional metal fabrication, highly parametric andcomputational informed designs are constrained by the conditions ofmass production. High output low cost, limiting the highly variable natureof geometrical informed individual joints.</b></p> <p>Through the fabrication of computational informed architectural steelconnection in combination with non-linear geometric segmentation and6-axis robotic wire arc additive manufacture (WAAM), this researchaims to optimise the aesthetic opportunities and practical fabrication oflarge-scale steel architectural joints. Currently, there exists a disconnectbetween the dialogue of metal joints and architectural intent. Throughthe innovative use of non-linear fabrication and member informed multidirectionalslicing, this research aims to construct a closed digital designloop though to fabrication. Utilising the additive fabrication processof WAAM, to construct a mass-customisable metal node which stillmaintains an aesthetic architectural opportunity.</p> <p>The aims of this research are to outline the potential of fabricating masscustomisablesteel joints through the process of non-linear segmentation.</p> <p>Describing the methods and techniques used to fabricate a computationalinformed metal joint through the manufacturing process of WAAM.</p>

Fast and Efficient Boolean Matrix Factorization by Geometric Segmentation

Boolean matrix has been used to represent digital information in many fields, including bank transaction, crime records, natural language processing, protein-protein interaction, etc. Boolean matrix factorization (BMF) aims to find an approximation of a binary matrix as the Boolean product of two low rank Boolean matrices, which could generate vast amount of information for the patterns of relationships between the features and samples. Inspired by binary matrix permutation theories and geometric segmentation, we developed a fast and efficient BMF approach, called MEBF (Median Expansion for Boolean Factorization). Overall, MEBF adopted a heuristic approach to locate binary patterns presented as submatrices that are dense in 1's. At each iteration, MEBF permutates the rows and columns such that the permutated matrix is approximately Upper Triangular-Like (UTL) with so-called Simultaneous Consecutive-ones Property (SC1P). The largest submatrix dense in 1 would lie on the upper triangular area of the permutated matrix, and its location was determined based on a geometric segmentation of a triangular. We compared MEBF with other state of the art approaches on data scenarios with different density and noise levels. MEBF demonstrated superior performances in lower reconstruction error, and higher computational efficiency, as well as more accurate density patterns than popular methods such as ASSO, PANDA and Message Passing. We demonstrated the application of MEBF on both binary and non-binary data sets, and revealed its further potential in knowledge retrieving and data denoising.

A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices

Teleception is defined as sensing that occurs remotely, with no physical contact with the object being sensed. To emulate innate control systems of the human body, a control system for a semi- or fully autonomous assistive device not only requires feedforward models of desired movement, but also the environmental or contextual awareness that could be provided by teleception. Several recent publications present teleception modalities integrated into control systems and provide preliminary results, for example, for performing hand grasp prediction or endpoint control of an arm assistive device; and gait segmentation, forward prediction of desired locomotion mode, and activity-specific control of a prosthetic leg or exoskeleton. Collectively, several different approaches to incorporating teleception have been used, including sensor fusion, geometric segmentation, and machine learning. In this paper, we summarize the recent and ongoing published work in this promising new area of research.