Application of Remotely Piloted Unmanned Aerial Vehicles in Construction Management

Construction projects may face challenges due to long project duration, uncertainties and big size.

In recent times, remarkable research work has been done on automation of construction.

Unmanned Aerial Vehicles are exponentially being utilized in various civil engineering areas like land surveying, crack detection, construction logistic management, highway asset management and site inspection.

It is always difficult to monitor and track the status of a large construction site. However, unmanned aerial vehicles collect huge data of a construction project quickly.

Remotely located large-scaled construction sites can be monitor by using advanced IT technology in a frequent manner. In this research endeavor, a drone has been used for construction monitoring of the G+6 building with the help of Pix4D software.

This research proposed the unmanned aerial vehicle enabled site to automation BIM (Building Information Modeling). Unmanned aerial vehicle-captured visual data can be utilized effectively with the help of Pix4Dbim. The robotic data collection during construction monitoring can provide enormous benefits to building information modeling.

Read more: https://link.springer.com/chapter/10.1007/978-981-15-5195-6_25

Utilizing Airborne LiDAR and UAV Photogrammetry Techniques in Local Geoid Model Determination and Validation

This investigation evaluates the performance of Digital Terrain Models (DTMs) generated in different vertical datums by aerial LiDAR and UAV (Unmanned Aerial Vehicle) photogrammetry techniques, for the determination and validation of local geoid models.

Many engineering projects require the point heights referring to a physical surface, i.e., geoid, rather than an ellipsoid. When a high-accuracy local geoid model is available in the study area, the physical heights are practically obtained with the transformation of Global Navigation Satellite System (GNSS) ellipsoidal heights of the points.

Besides the commonly used geodetic methods, this study introduces a novel approach for the determination and validation of the local geoid surface models using photogrammetry. The numeric tests were carried out in the Bergama region, in the west of Turkey. Using direct georeferenced airborne LiDAR and indirect georeferenced UAV photogrammetry-derived point clouds, DTMs were generated in ellipsoidal and geoidal vertical datums, respectively.

After this, the local geoid models were calculated as differences between the generated DTMs. Generated local geoid models in the grid and pointwise formats were tested and compared with the regional gravimetric geoid model (TG03) and a high-resolution global geoid model (EIGEN6C4), respectively. In conclusion, the applied approach provided sufficient performance for modeling and validating the geoid heights with centimeter-level accuracy. 

Read more at https://www.researchgate.net/publication/344146054_Utilizing_Airborne_LiDAR_and_UAV_Photogrammetry_Techniques_in_Local_Geoid_Model_Determination_and_Validation

Attention-Based Road Registration for GPS-Denied UAS Navigation

Matching and registration between aerial images and prestored road landmarks are critical techniques to enhance UAS (Unmanned Aerial System) navigation in the Global Positioning System (GPS)-denied urban environments.

Current registration processes typically consist of two separate stages of road extraction and road registration. These two-stage registration approaches are time-consuming and less robust to noise.

To that end, it has been investigated the problem of end-to-end Aerial-Road registration. Using deep learning, it has been developed a novel attention-based neural network architecture for Aerial-Road registration.

In this model, it has been constructed two-branch neural networks with shared weights to map two input images into a common embedding space. Besides, considering that road features are sparsely distributed in images, it has been incorporated a novel multibranch attention module to filter out false descriptor matches from the indiscriminative background in order to improve registration accuracy.

Finally, the results from extensive experiments show that compared with state-of-the-art approaches, the mean absolute errors of the approach in rotation angle and the translations in the x- and y-directions are reduced down by a factor of 1.24, 1.38, and 1.44, respectively. Furthermore, as a byproduct, the experimental results prove the feasibility of a neural network multitask learning approach to simultaneously achieve accurate Aerial-Road matching and registration, thus providing an efficient and accurate UAS geolocalization.

More info:

https://ieeexplore.ieee.org/document/9184263

Pronóstico mundial para 2025: adopción cada vez mayor de la industria 4.0 y del IIoT

Research and Markets acaba de lanzar al mercado un interesante informe titulado "Mercado de sensores láser: crecimiento, tendencias, pronósticos (2020-2025)". Según el informe, el mercado global de sensores láser alcanzó un valor superior a los 900 millones de dólares durante el pasado año 2019, y se estima que alcanzará un valor superior a los 1.500 millones de dólares para el año 2025. Entre otros factores que van a potenciar este crecimiento destaca la implementación de las tecnologías industriales englobadas en el concepto de Industria 4.0 y la evolución de la fabricación tradicional hacia una fabricación basada en el IIoT (Industrial IoT) (Industrial Internet of Things)

Más información:

https://www.prnewswire.com/news-releases/global-laser-sensors-industry-forecast-to-2025---increasing-adoption-of-industry-4-0-and-smart-manufacturing-practices-301121888.html

The New Shop Class

The New Shop Class connects the worlds of the maker and hacker with that of the scientist and engineer.

If you are a parent or educator or a budding maker yourself, and you feel overwhelmed with all of the possible technologies, this book will get you started with clear discussions of what open source technologies like 3D Printers can really do in the right hands.

Written by real "rocket scientist" Joan Horvath, author of Mastering 3D Printing, and 3D Printing expert Rich Cameron, The New Shop Class is a friendly, down-to-earth chat about how hands-on making things can lead to a science career.

Get practical suggestions about how to use technologies like 3D printing, Arduino, and simple electronics. Learn how to stay a step ahead of the young makers in your life and how to encourage them in maker activities. Discover how engineers and scientists got their start, and how their mindsets mirror that of the maker.

Read more:

https://link.springer.com/book/10.1007%2F978-1-4842-0904-2#toc

Renishaw, HiETA & nTopology Support Cobra Aero in the Design, Development and Production of a Novel UAV Engine

Cobra Aero, a successful producer of two-stroke engines for UAV applications approached Renishaw to understand how they could incorporate additive manufacturing into their existing manufacturing portfolio.

Cobra had a vision for the use of metal Additive Manufacturing (AM) in their business, and enlisted additional help from HiETA and nTopology to help drive the development of an innovative engine design.

Leveraging the design opportunities of AM and the expertise of the partners involved, Cobra have devised a pioneering and extremely performant new engine design.

Moreover, Cobra have explored the applications space including production of tooling, complex componentry and highly customized components in their sister motorcycle business, Cobra Moto.

Primary Topics: • Design for Manufacture • Aerospace Design • Complex Structures for Heat Exchange • Product Innovation and Testing Speaker: Kevin Brigden Additive Applications Engineer, Renishaw

Kevin has a master's degree in engineering with honors in motorsports engineering from the University of Central Lancashire, England. A member of a team of technical specialists, he brings a skill-set centered in computer-aided engineering (CAE) including computer-aided design (CAD), finite element analysis (FEA) and computational fluid dynamics (CFD). During Kevin's time with Renishaw, he has led and consulted on numerous design projects in collaboration with partners and customers from aerospace, automotive, space and defense and medical engineering. Kevin is at the forefront of the design for additive manufacture (DfAM) movement, with many of his characteristic and innovative designs widely recognized and imitated.

More info:

https://register.gotowebinar.com/register/2915462139651536141?source=web

A plug-and-play Hyperspectral Imaging Sensor using low-cost equipment

HSIs (Hyperspectral Imaging Sensors) obtain spectral information from an object, and they are used to solve problems in Remote Sensing, Food Analysis, Precision Agriculture, and others.

This paper took advantage of modern high-resolution cameras, electronics, and optics to develop a robust, low-cost, and easy to assemble HSI device. This device could be used to evaluate new algorithms for hyperspectral image analysis and explore its feasibility to develop new applications on a low-budget.

It weighs up to 300 g, detects wavelengths from 400 nm–1052 nm, and generates up to 315 different wavebands with a spectral resolution up to 2.0698 nm. Its spatial resolution of 116 × 110 pixels works for many applications. Furthermore, with only 2% of the cost of commercial HSI devices with similar characteristics, it has shown high spectral accuracy in controlled light conditions as well as ambient light conditions.

Unlike related works, the proposed HSI system includes a framework to build the proposed HSI from scratch. This framework decreases the complexity of building an HSI device as well as the processing time. It contains every needed 3D model, a calibration method, the image acquisition software, and the methodology to build and calibrate the proposed HSI device. Therefore, the proposed HSI system is portable, reusable, and lightweight.

https://www.sciencedirect.com/science/article/pii/S2468067219300513