Experimental results of a terrain relative navigation algorithm using a simulated lunar scenario

This paper deals with the problem of the navigation of a lunar lander based on the Terrain Relative Navigation approach. An algorithm is developed and tested on a scaled simulated lunar scenario, over which a tri-axial moving frame has been built to reproduce the landing trajectories. At the tip of the tri-axial moving frame, a long-range and a short-range infrared distance sensor are mounted to measure the altitude. The calibration of the distance sensors is of crucial importance to obtain good measurements. For this purpose, the sensors are calibrated by optimizing a nonlinear transfer function and a bias function using a least squares method. As a consequence, the covariance of the sensors is approximated with a second order function of the distance. The two sensors have two different operation ranges that overlap in a small region. A switch strategy is developed in order to obtain the best performances in the overlapping range. As a result, a single error model function of the distance is found after the evaluation of the switch strategy. Because of different environmental factors, such as temperature, a bias drift is evaluated for both the sensors and properly taken into account in the algorithm. In order to reflect information of the surface in the navigation algorithm, a Digital Elevation Model of the simulated lunar surface has been considered. The navigation algorithm is designed as an Extended Kalman Filter which uses the altitude measurements, the Digital Elevation Model and the accelerations measurements coming from the moving frame. The objective of the navigation algorithm is to estimate the position of the simulated space vehicle during the landing from an altitude of 3 km to a landing site in the proximity of a crater rim. Because the algorithm needs to be updated during the landing, a crater peak detector is conceived in order to reset the navigation filter with a new state vector and new state covariance. Experimental results of the navigation algorithm are presented in the paper

Spacecraft rendezvous by differential drag under uncertainties

At low Earth orbits, differentials in the drag forces between spacecraft can be used for controlling their relative motion in the orbital plane. Current methods for determining the drag force may result in errors due to inaccuracies in the density models and drag coefficients. In this work, a methodology for relative maneuvering of spacecraft based on differential drag, accounting for uncertainties in the drag model, is proposed. A dynamical model composed of the mean semimajor axis and the argument of latitude is used for describing long-range maneuvers. For this model, a linear quadratic regulator is implemented, accounting for the uncertainties in the drag force. The actuation is the pitch angle of the satellites, considering saturation. The control scheme guarantees asymptotic stability of the system up to a certain magnitude of the state vector, which is determined by the uncertainties. Numerical simulations show that the method exhibits consistent robustness to accomplish the maneuvers, even in the presence of realistic modeling of density fields, drag coefficients, the corotation of the atmosphere, and zonal harmonics up to J(8)

Analytic Solution of the Time-Optimal Control of a Double Integrator from an Arbitrary State to the State-space Origin

This brief note presents known results about the minimum-time control of a double integrator system from an arbitrary initial state to the state-space origin (minimum-time regulation problem, or special problem). The main purpose of this note is didactical. Results are presented in all details and following a step by step procedure.Comment: 5 pages, 1 figure, 3 table

Autonomous crater detection on asteroids using a fully-convolutional neural network

This paper shows the application of autonomous Crater Detection using the U-Net, a Fully-Convolutional Neural Network, on Ceres. The U-Net is trained on optical images of the Moon Global Morphology Mosaic based on data collected by the LRO and manual crater catalogues. The Moon-trained network will be tested on Dawn optical images of Ceres: this task is accomplished by means of a Transfer Learning (TL) approach. The trained model has been fine-tuned using 100, 500 and 1000 additional images of Ceres. The test performance was measured on 350 never before seen images, reaching a testing accuracy of 96.24%, 96.95% and 97.19%, respectively. This means that despite the intrinsic differences between the Moon and Ceres, TL works with encouraging results. The output of the U-Net contains predicted craters: it will be post-processed applying global thresholding for image binarization and a template matching algorithm to extract craters positions and radii in the pixel space. Post-processed craters will be counted and compared to the ground truth data in order to compute image segmentation metrics: precision, recall and F1 score. These indices will be computed, and their effect will be discussed for tasks such as automated crater cataloguing and optical navigation

Spacecraft Proximity Navigation and Autonomous Assembly based on Augmented State Estimation: Analysis and Experiments

AIAA Guidance, Navigation, and Control Conference 2 - 5 August 2010, Toronto, Ontario CanadaThis paper presents a spacecraft relative navigation scheme based on a tracking technique. The augmented state estimation technique is a variable-dimension filtering approach, originally introduced by Bar-Shalom and Birmiwal [1]. In this technique, the state model for a target spacecraft is augmented by introducing, as extra state components, the target's control inputs. The maneuver, modeled as accelerations, is estimated recursively along with the other states associated with position and velocity, while a target maneuvers. By using the proposed navigation method, a chaser spacecraft can estimate the relative position, the attitude and the control inputs of a target spacecraft, flying in its proximity. It is assumed that the chaser spacecraft is equipped with on-board sensors able to measure the relative position and relative attitude of the target spacecraft. The available sensors would provide a measurement update sample time of the order of one second and be subject to random measurement interruption longer than one second. As preliminary analysis, this work introduces the technique applied to the planar, three-degree-of-freedom, spacecraft relative motion. The proposed approach is validated via hardware-in-the-loop experimentation, using four autonomous three-degree-of-freedom robotic spacecraft simulators, floating on a flat floor. The proposed navigation method is proved to be more robust than a standard Kalman Filter estimating relative position and attitude only

Physics-Informed Extreme Theory of Functional Connections Applied to Data-Driven Parameters Discovery of Epidemiological Compartmental Models

In this work we apply a novel, accurate, fast, and robust physics-informed neural network framework for data-driven parameters discovery of problems modeled via parametric ordinary differential equations (ODEs) called the Extreme Theory of Functional Connections (X-TFC). The proposed method merges two recently developed frameworks for solving problems involving parametric DEs, 1) the Theory of Functional Connections (TFC) and 2) the Physics-Informed Neural Networks (PINN). In particular, this work focuses on the capability of X-TFC in solving inverse problems to estimate the parameters governing the epidemiological compartmental models via a deterministic approach. The epidemiological compartmental models treated in this work are Susceptible-Infectious-Recovered (SIR), Susceptible-Exposed-Infectious-Recovered (SEIR), and Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIR). The results show the low computational times, the high accuracy and effectiveness of the X-TFC method in performing data-driven parameters discovery of systems modeled via parametric ODEs using unperturbed and perturbed data

INCIDÊNCIA DE GRÃOS ARDIDOS E RENDIMENTO DE GRÃOS EM MILHO COM APLICAÇAO DE NITROGÊNIO EM DIFERENTES ESTADIOS FENOLÓGICOS

O grande crescimento na produção da cultura do milho ocorreu com o advento do Sistema Plantio Direto, correção e fertilização do solo, manejo de plantas invasoras, doenças e pragas, com tecnologias como maquinários e utilização de organismos geneticamente modificados. O milho é uma cultura que remove grandes quantidades de nitrogênio e requer o uso de adubação nitrogenada em cobertura para potencialização da produção. Diante disso, o objetivo neste trabalho foi avaliar a incidência grãos ardidos e rendimento de grãos em milho com aplicação de nitrogênio em diferentes estádios fenológicos. O experimento foi conduzido em uma área com Sistema Plantio Direto consolidado há mais de 10 anos, na cidade de Ouro Verde, SC, safra 2014/2015. Foi utilizado o híbrido simples P 2530 de ciclo superprecoce, com parcelas compostas de 6 linhas de 5 metros de comprimento com 5 repetições por tratamento e densidade de 3,4 sementes por metro linear, totalizando 72.000 sementes por ha. O espaçamento utilizado foi de 0,45m e estádios de aplicação do nitrogênio em V4 e V6 (padrão), V2, V4, V6, V8, V2 e V8, V6 e V8 totalizando 7 tratamentos. A dose de nitrogênio utilizada conforme o produto comercial ureia foi 354 kg/ha, de acordo com a análise de solo da área. Para o rendimento de grãos de milho bem como para grãos ardidos, não foram observadas diferenças significativas em razão dos diferentes estádios para aplicação de nitrogênio; o rendimento médio foi de 14.209 kg ha-1 e apresentou em média 1,2% de grãos ardidos.Palavras-chave: Produtividade. Zea mays. Superprecoce

World Press Photo 2012: the discursive construction of the Arab Spring

O presente artigo pretende analisar a forma como o fenómeno da Primavera Árabe foi retratado nas fotografias vencedoras do concurso World Press Photo em 2012. As fotografias jornalísticas, embora se apresentem como índices do real, condicionam frequentemente a percepção dos indivíduos e influenciam as suas práticas sociais. Também as fotografias vencedoras do World Press Photo, um dos concursos mais prestigiados de fotojornalismo, em 2012 não fogem ao construtivismo discursivo que molda a representação e a percepção dos acontecimentos. A partir da análise crítica do discurso, nomeadamente dos instrumentos teóricos da semiótica barthesiana e da semiótica social de Gunther Kress e Theo van Leeuwen, procurámos interpretar as 38 fotografias vencedoras do World Press Photo 2012, a fim de reflectirmos sobre o modo como contribuíram para a compreensão da Revolta Árabe e como despertaram o nosso interesse e a nossa imaginação para o desenrolar do conflito.ABSTRACT:This article aims to analyze how the Arab Spring phenomenon was represented in the award-winning photographs of the World Press Photo contest in 2012. Although news photographs are usually seen as indexes of the real, they often limit the perception of individuals and influence their social practices. Also the winning photographs in 2012 of the World Press Photo, one of the most prestigious photojournalism contests, do not escape the discursive constructivism that shapes the representation and perception of events. Drawing from critical discourse analysis, namely from the theoretical tools of barthesian semiotics and Gunther Kress’s and Theo van Leeuwen’s social semiotics, we sought to interpret the 38 award-winning photographs of the World Press Photo contest in 2012, in order to reflect on the way they have contributed to our understanding of the Arab Revolt and aroused our interest and imagination to the unfolding of the conflict.info:eu-repo/semantics/publishedVersio

Maturation signatures of conventional dendritic cell subtypes in COVID‐19 suggest direct viral sensing

Growing evidence suggests that conventional dendritic cells (cDCs) undergo aberrant maturation in COVID-19, which negatively affects T-cell activation. The presence of effector T cells in patients with mild disease and dysfunctional T cells in severely ill patients suggests that adequate T-cell responses limit disease severity. Understanding how cDCs cope with SARS-CoV-2 can help elucidate how protective immune responses are generated. Here, we report that cDC2 subtypes exhibit similar infection-induced gene signatures, with the upregulation of interferon-stimulated genes and interleukin (IL)-6 signaling pathways. Furthermore, comparison of cDCs between patients with severe and mild disease showed severely ill patients to exhibit profound downregulation of genes encoding molecules involved in antigen presentation, such as MHCII, TAP, and costimulatory proteins, whereas we observed the opposite for proinflammatory molecules, such as complement and coagulation factors. Thus, as disease severity increases, cDC2s exhibit enhanced inflammatory properties and lose antigen presentation capacity. Moreover, DC3s showed upregulation of anti-apoptotic genes and accumulated during infection. Direct exposure of cDC2s to the virus in vitro recapitulated the activation profile observed in vivo. Our findings suggest that SARS-CoV-2 interacts directly with cDC2s and implements an efficient immune escape mechanism that correlates with disease severity by downregulating crucial molecules required for T-cell activation