Collision Avoidance on Unmanned Aerial Vehicles using Deep Neural Networks

Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries, being widely used not only among enthusiastic consumers but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is full of serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, focusing on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. First, the SoA principles for collision avoidance against stationary objects are reviewed. Afterward, a novel image processing approach that uses deep learning and optical flow is presented. This approach is capable of detecting and generating escape trajectories against potential collisions with dynamic objects. Finally, novel models and algorithms combinations were tested, providing a new approach for the collision avoidance of UAVs using Deep Neural Networks. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, created from scratch using the framework developed.Os veículos aéreos não tripulados (VANTs), embora dificilmente considerados uma nova tecnologia, ganharam recentemente um papel de destaque em muitas indústrias, sendo amplamente utilizados não apenas por amadores, mas também em situações profissionais de alta exigência, sendo expectável um impacto social massivo nos próximos anos. No entanto, a operação de VANTs está repleta de sérios riscos de segurança, como colisões com obstáculos dinâmicos (pássaros, outros VANTs ou objetos arremessados). Estes cenários de colisão são complexos para analisar em tempo real, às vezes sendo computacionalmente impossível de resolver com os algoritmos existentes, tornando o uso de VANTs um risco operacional e, portanto, reduzindo significativamente a sua aplicabilidade comercial em ambientes citadinos. Neste trabalho, uma arquitectura conceptual para VANTs autônomos e em rede é apresentada, com foco nos requisitos arquitetônicos do subsistema de prevenção de colisão para atingir níveis aceitáveis de segurança e confiabilidade. Os estudos presentes na literatura para prevenção de colisão contra objectos estacionários são revistos e uma nova abordagem é descrita. Esta tecnica usa técnicas de aprendizagem profunda e processamento de imagem, para realizar a prevenção de colisões em tempo real com objetos móveis. Por fim, novos modelos e combinações de algoritmos são propostos, fornecendo uma nova abordagem para evitar colisões de VANTs usando Redes Neurais Profundas. A viabilidade da abordagem foi demonstrada através de testes experimentais utilizando um VANT, desenvolvido a partir da arquitectura apresentada

Collision avoidance on unmanned aerial vehicles using neural network pipelines and flow clustering techniques

UIDB/04111/2020 PCIF/SSI/0102/2017 IF/00325/2015Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.publishersversionpublishe

Nursing epidemiological approach of hypertension management in a Public Health Service from the Northern Region of Portugal

Background: Epidemiological surveillance of a nursing diagnosis is an approach anchored in a post-modern epidemiology focused on a person’s health disease responses. Regarding public health priorities, the population where our study occurred had as a priority problem arterial hypertension. Related to this chronic disease, nursing diagnoses about health disease responses in primary healthcare has, as a major focus, Therapeutic Regimen Management. Our aim was to study the nursing diagnosis in this issue from an epidemiological approach. Methods: A descriptive study from an epidemiological approach was developed, analyzing nursing diagnoses in hypertensive patients. Results: We found 17.7% of undiagnosed patients and better diagnoses in patients with complications than in those without complications. Conclusions: Nursing records need to be improved in order to promote more robust studies in the post-modern epidemiology for the future.info:eu-repo/semantics/publishedVersio

Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images

Funding Information: This research was partially funded by Fundação para a Ciência e a Tecnologia under Projects UIDB/00066/2020, UIDB/04111/2020, foRESTER PCIF/SSI/0102/2017, and IF/00325/2015; Instituto Lusófono de Investigação e Desenvolvimento (ILIND) under Project COFAC/ILIND/COPELABS/1/2020; Project “(Link4S)ustainability—A new generation connectivity system for creation and integration of networks of objects for new sustainability paradigms [POCI-01-0247-FEDER-046122 | LISBOA-01-0247-FEDER-046122]” is financed by the Operational Competitiveness and Internationalization Programmes COMPETE 2020 and LISBOA 2020, under the PORTUGAL 2020 Partnership Agreement, and through the European Structural and Investment Funds in the FEDER component; and also IEoT: Intelligent Edge of Things under under Project LISBOA-01-0247-FEDER-069537. Publisher Copyright: © 2022 by the authors.This paper focuses on the gaps that occur inside plantations; these gaps, although not having anything growing in them, still happen to be watered. This action ends up wasting tons of liters of water every year, which translates into financial and environmental losses. To avoid these losses, we suggest early detection. To this end, we analyzed the different available neural networks available with multispectral images. This entailed training each regional and regression-based network five times with five different datasets. Networks based on two possible solutions were chosen: unmanned aerial vehicle (UAV) depletion or post-processing with external software. The results show that the best network for UAV depletion is the Tiny-YOLO (You Only Look Once) version 4-type network, and the best starting weights for Mask-RCNN were from the Tiny-YOLO network version. Although no mean average precision (mAP) of over 70% was achieved, the final trained networks managed to detect mostly gaps, including low-vegetation areas and very small gaps, which had a tendency to be overlooked during the labeling stage.publishersversionpublishe

Autonomous environment generator for uav-based simulation

Funding Information: Funding: This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 783119. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Belgium, Czech Republic, Finland, Germany, Greece, Italy, Latvia, Norway, Poland, Portugal, Spain, Sweden. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The increased demand for Unmanned Aerial Vehicles (UAV) has also led to higher demand for realistic and efficient UAV testing environments. The current use of simulated environments has been shown to be a relatively inexpensive, safe, and repeatable way to evaluate UAVs before real-world use. However, the use of generic environments and manually-created custom scenarios leaves more to be desired. In this paper, we propose a new testbed that utilizes machine learning algorithms to procedurally generate, scale, and place 3D models to create a realistic environment. These environments are additionally based on satellite images, thus providing users with a more robust example of real-world UAV deployment. Although certain graphical improvements could be made, this paper serves as a proof of concept for an novel autonomous and relatively-large scale environment generator. Such a testbed could allow for preliminary operational planning and testing worldwide, without the need for on-site evaluation or data collection in the future.publishersversionpublishe

VITASENIOR-MT: A distributed and scalable cloud-based telehealth solution

VITASENIOR-MT is a telehealth platform that allows to remotely monitor biometric and environmental data in a domestic environment, designed specifically to the elderly population. This paper proposes a highly scalable and efficient architecture to transport, process, store and visualize the data collected by devices of an Internet of Things (IoT) scenario. The cloud infrastructure follows a microservices architecture to provide computational scalability, better fault isolation, easy integration and automatic deployment. This solution is complemented with a pre-processing and validation of the collected data at the edge of the Internet by using the Fog Computing concept, allowing a better computing distribution. The presented approach provides personal data security and a simplified way to collect and present the data to the different actors, allowing a dynamic and intuitive management of patients and equipment to caregivers. The presented load tests proved that this solution is more efficient than a monolithic approach, promoting better access and control in the data flowing from heterogeneous equipment.This work has been financially supported by the IC&DT project VITASENIOR-MT CENTRO-01-0145- FEDER-023659 with FEDER funding through programs CENTRO2020 and FCT.info:eu-repo/semantics/publishedVersio

How to build a 2d and 3d aerial multispectral map?—all steps deeply explained

UIDB/04111/2020 PCIF/SSI/0102/2017 IF/00325/2015 UIDB/00066/2020The increased development of camera resolution, processing power, and aerial platforms helped to create more cost-efficient approaches to capture and generate point clouds to assist in scientific fields. The continuous development of methods to produce three-dimensional models based on two-dimensional images such as Structure from Motion (SfM) and Multi-View Stereopsis (MVS) allowed to improve the resolution of the produced models by a significant amount. By taking inspiration from the free and accessible workflow made available by OpenDroneMap, a detailed analysis of the processes is displayed in this paper. As of the writing of this paper, no literature was found that described in detail the necessary steps and processes that would allow the creation of digital models in two or three dimensions based on aerial images. With this, and based on the workflow of OpenDroneMap, a detailed study was performed. The digital model reconstruction process takes the initial aerial images obtained from the field survey and passes them through a series of stages. From each stage, a product is acquired and used for the following stage, for example, at the end of the initial stage a sparse reconstruction is produced, obtained by extracting features of the images and matching them, which is used in the following step, to increase its resolution. Additionally, from the analysis of the workflow, adaptations were made to the standard workflow in order to increase the compatibility of the developed system to different types of image sets. Particularly, adaptations focused on thermal imagery were made. Due to the low presence of strong features and therefore difficulty to match features across thermal images, a modification was implemented, so thermal models could be produced alongside the already implemented processes for multispectral and RGB image sets.publishersversionpublishe

Ffau—framework for fully autonomous uavs

Nr. 024539 (POCI-01-0247-FEDER-024539) under grant agreement No 783221 UID/EEA/00066/2019Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world.publishersversionpublishe

Numerical 3D modeling of heat transfer in human tissues for microwave radiometry monitoring of Brown fat metabolismo

Background: Brown adipose tissue (BAT) plays an important role in whole body metabolism and could potentially mediate weight gain and insulin sensitivity. Although some imaging techniques allow BAT detection, there are currently no viable methods for continuous acquisition of BAT energy expenditure. We present a non-invasive technique for long term monitoring of BAT metabolism using microwave radiometry. Methods: A multilayer 3D computational model was created in HFSS™ with 1.5 mm skin, 3-10 mm subcutaneous fat, 200 mm muscle and a BAT region (2-6 cm3) located between fat and muscle. Based on this model, a log-spiral antenna was designed and optimized to maximize reception of thermal emissions from the target (BAT). The power absorption patterns calculated in HFSS™ were combined with simulated thermal distributions computed in COMSOL® to predict radiometric signal measured from an ultra-low-noise microwave radiometer. The power received by the antenna was characterized as a function of different levels of BAT metabolism under cold and noradrenergic stimulation. Results: The optimized frequency band was 1.5-2.2 GHz, with averaged antenna efficiency of 19%. The simulated power received by the radiometric antenna increased 2-9 mdBm (noradrenergic stimulus) and 4-15 mdBm (cold stimulus) corresponding to increased 15-fold BAT metabolism. Conclusions: Results demonstrated the ability to detect thermal radiation from small volumes (2-6 cm3) of BAT located up to 12 mm deep and to monitor small changes (0.5°C) in BAT metabolism. As such, the developed miniature radiometric antenna sensor appears suitable for non-invasive long term monitoring of BAT metabolism