Applied studies in Digital Fabrication and Parametricism

Presented is a research investigating the ability of digital fabrication tools to provide an alternative method for creating complex forms as part of an ongoing PhD research. The digital tool provides a comprehensive digital basis for construction that, since the beginning of building industrialization, has been an illusion rather than a reality. Beyond catching up on technology, the digital fabrication tool hereby provokes fundamental changes in the architectural discipline: the mere relation of the digital reality of computers with the physical reality of architecture. As opposed to the experiments in the early days of digitization, the focus is no longer on form, rather it is on the physical improvement of the discipline. This paper therefore presents a review of contemporary attempts within the time frame of the past four years with a thorough analysis and breakdown of the prototypes. In comparison to these examples, and considering the same building materials, another case studies are explored as precedents in history that have challenged the traditional understanding of the production of architectural forms. The understanding of the essential factors that constitute the advanced design process is consequently put into discussion. The relations between these factors and their direct effect over the architectural process has changed with the digitizing processes. This is also discussed and analyzed in regards to the implications and possibilities emerging for an indefinite relation between the tool and the design. The paper suggests a new medium linking the virtual and built environments and highlights the limitations of these new trends

The role of physical education lesson in developing the psychosocial development of female students practicing track and field

The study aimed to find out the role of physical education lesson in developing the psychosocial growth of female students practicing track and field, a sample of 61 students was chosen randomly for some school students from Kasabet Amman directorate. After making sure of the sample validity and stability, the researcher applied the study tool on it. The study tool consisted of 21 paragraphs distributed on 4 themes: the importance of the material and its benefits, the psychological one, the family one, and the role of the teacher. The researcher used the descriptive method in the survey method in collecting and analyzing data.There is no direct relationship between physical education lesson and the psychosocial development of the female players, the psychological problems are one of the most important obstacles in practicing the physical education lesson, parents of females who practices sports are interested in sports news, but they do not practice it.And The most important recommendation was paying attention to activating physical education in a serious matter by teachers by using different strategies in physical education lessons because of its positive impact on the psychosocial behavior of female athletes and enhancing their social relations

INTERACTIVE PLAYSCAPES: EXPLORATIVE DESIGN AND ROBOTIC FABRICATION TECHNIQUES

This research focuses on material-based practices and explorations by utilizing the carbon fiber fabric performance characteristics as a significant driver in the design and fabrication strategies. While the integrative aspects of computational design have been extensively used for the inclusion of environmental, manufacturing, or economic considerations, material information should be similarly employed as a generative driver. The paper describes and evaluates a full-scale prototype of installation for social play actuated in the heart of Beirut city, hence integrating material research with methodologies optimizing fabrication techniques for complex, performance-driven structures. The introduction of carbon fiber composites into the construction sector defines potential challenges to the design process, knowing that these components need to be light and cost-effective in their production. At the same time, advanced technologies, such as digital fabrication, need to dwell upon their limitations regarding time optimization, material restrictions, and relations between automated and manual labor. Many applications show that carbon fiber system has proven to be a novel building material to improve structures. Regarding the fabrication techniques utilized, milling is a vital process, where the material subtraction rate is one of the essential features to be established in addition to its final weight. However, factors such as shape precision and surface quality are constraining factors in the increase of material removal regarding robotic fabrication. Hence, in this work, machining strength and surface roughness are considered restricting to the optimization of machining parameters in order to obtain a maximum material removal rate

Analysing the impact of learning inputs - Application to terrain traversability estimation

Data-driven approaches such as Gaussian Process (GP) regression have been used extensively in recent robotics literature to achieve estimation by learning from experience. To ensure satisfactory performance, in most cases, multiple learning inputs are required. Intuitively, adding new inputs can often contribute to better estimation accuracy, however, it may come at the cost of a new sensor, larger training dataset and/or more complex learning, some- times for limited benefits. Therefore, it is crucial to have a systematic procedure to determine the actual impact each input has on the estimation performance. To address this issue, in this paper we propose to analyse the impact of each input on the estimate using a variance-based sensitivity analysis method. We propose an approach built on Analysis of Variance (ANOVA) decomposition, which can characterise how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We apply the proposed approach to a terrain-traversability estimation method we proposed in prior work, which is based on multi-task GP regression, and we validate this implementation experimentally using a rover on a Mars-analogue terrain

An approach to autonomous science by modeling geological knowledge in a Bayesian framework

© 2017 IEEE. Autonomous Science is a field of study which aims to extend the autonomy of exploration robots from low level functionality, such as on-board perception and obstacle avoidance, to science autonomy, which allows scientists to specify missions at task level. This will enable more remote and extreme environments such as deep ocean and other planets to be studied, leading to significant science discoveries. This paper presents an approach to extend the high level autonomy of robots by enabling them to model and reason about scientific knowledge on-board. We achieve this by using Bayesian networks to encode scientific knowledge and adapting Monte Carlo Tree Search techniques to reason about the network and plan informative sensing actions. The resulting knowledge representation and reasoning framework is anytime, handles large state spaces and robust to uncertainty making it highly applicable to field robotics. We apply the approach to a Mars exploration mission in which the robot is required to plan paths and decide when to use its sensing modalities to study a scientific latent variable of interest. Extensive simulation results show that our approach has significant performance benefits over alternative methods. We also demonstrate the practicality of our approach in an analog Martian environment where our experimental rover, Continuum, plans and executes a science mission autonomously