Automated multi-rotor draft survey of large vessels

In maritime sector draft survey has a significant importance as it is used to determine many important factors used in maritime transportation. Draft is the vertical displacement from the bottom of the keel (the bottom-most element of a vessel) to the water line (the line of meeting point of hull and the water surface). It is used to measure the minimum water depth for safe navigation of vessel and to evaluate mass of cargo in the vessel by the change in displacement on the draft scale after loading of the cargo in the vessel. Draft measurement of a vessel has a vital role in maritime sector to ensure a safe equilibrium between maximum and minimum cargo that can be loaded in the vessel. Draft survey performed at the time of loading and unloading of cargo (Iron Ore) at the Narvik port to read out draft markings traditionally involved a round trip around the vessel in a small crew boat and it is a time consuming and challenging task specially in darkness (during night), shadows and when difficult to safely reach the crew boat close enough due to anchors and buoys. The goal of this study is to develop an autonomous multi-rotor system that can survey the large vessel to capture all the necessary draft measurements by reaching close enough even in challenging environments like nighttime and in presence of obstacles. This involves developing the solution for path planning to perform flight operation autonomously, developing guidance and control algorithm for the flight operation to enable the multi-rotor to follow the designated path and perform the inspection while avoiding all the hurdles using collision avoidance system. Along with developing the specifications for a multi-rotor that can perform the inspection and suggest necessary system components including multi-rotor itself and additional components such as sensors, lights and camera, and necessities for on-board data handling

Precision Agriculture Techniques and Practices: From Considerations to Applications

Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work

NeBula: TEAM CoSTAR’s robotic autonomy solution that won phase II of DARPA subterranean challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR’s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.Peer ReviewedAgha, A., Otsu, K., Morrell, B., Fan, D. D., Thakker, R., Santamaria-Navarro, A., Kim, S.-K., Bouman, A., Lei, X., Edlund, J., Ginting, M. F., Ebadi, K., Anderson, M., Pailevanian, T., Terry, E., Wolf, M., Tagliabue, A., Vaquero, T. S., Palieri, M., Tepsuporn, S., Chang, Y., Kalantari, A., Chavez, F., Lopez, B., Funabiki, N., Miles, G., Touma, T., Buscicchio, A., Tordesillas, J., Alatur, N., Nash, J., Walsh, W., Jung, S., Lee, H., Kanellakis, C., Mayo, J., Harper, S., Kaufmann, M., Dixit, A., Correa, G. J., Lee, C., Gao, J., Merewether, G., Maldonado-Contreras, J., Salhotra, G., Da Silva, M. S., Ramtoula, B., Fakoorian, S., Hatteland, A., Kim, T., Bartlett, T., Stephens, A., Kim, L., Bergh, C., Heiden, E., Lew, T., Cauligi, A., Heywood, T., Kramer, A., Leopold, H. A., Melikyan, H., Choi, H. C., Daftry, S., Toupet, O., Wee, I., Thakur, A., Feras, M., Beltrame, G., Nikolakopoulos, G., Shim, D., Carlone, L., & Burdick, JPostprint (published version

NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)

Visual Servoing NMPC Applied to UAVs for Photovoltaic Array Inspection

The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on photogrammetry. However, the photogrammetry approach presents limitations, such as an increased amount of useless data during flights, potential issues related to image resolution, and the detection process during high-altitude flights. In this work, we develop a visual servoing control system applied to a UAV with dynamic compensation using a nonlinear model predictive control (NMPC) capable of accurately tracking the middle of the underlying PV array at different frontal velocities and height constraints, ensuring the acquisition of detailed images during low-altitude flights. The visual servoing controller is based on the extraction of features using RGB-D images and the Kalman filter to estimate the edges of the PV arrays. Furthermore, this work demonstrates the proposal in both simulated and real-world environments using the commercial aerial vehicle (DJI Matrice 100), with the purpose of showcasing the results of the architecture. Our approach is available for the scientific community in: https://github.com/EPVelasco/VisualServoing_NMPCComment: This paper is under review at the journal "IEEE Robotics and Automation Letters

Development of New Cotton Defoliation Sprayer Using Unmanned Ground Vehicle and Pulse Width Modulation Technology

Chemical spraying is one of the most important and frequently performed intercultural agriculture operations. It is imperative to utilize appropriate spraying technology as a selection of ineffective one leads to waste of agrochemicals to the non‐target area. Several precision technologies have been developed in the past few decades, such as image processing based on real‐time variable‐rate chemical spraying systems, autonomous chemical sprayers using machine vision and nozzle control, and use of unmanned aerial and ground vehicles. Cotton (Gossypium hirsutum L.) is an important industrial crop. It is a perennial crop with indeterminate growth habit; however, in most parts of the United States, it is grown as an annual crop and managed using growth regulators. Cotton defoliation is a natural physiological phenomenon, but untimely and/or inadequate defoliation by natural processes necessitates the application of chemical defoliants for efficient harvest. Defoliation is a major production practice influencing harvester efficiency, fiber trash content, cotton yield, and fiber quality. Currently, defoliant spraying is done by conventional ground driven boom sprayer or aerial applicator and both systems spray chemical vertically downwards into the canopy, which results in less chemical reaching the bottom of the canopy. Thus, a new autonomous ground sprayer was developed using robotics and pulse width modulation, which travels between two rows covering the whole canopy of the plant. Field research was conducted to evaluate the (i) effect of duty cycles (20%,40%, and 60%) on droplet characteristic (droplet distribution, deposition, and drift potential), defoliation cotton fiber and (ii) effect of duty cycles on cotton yield and II fiber quality. Droplet characteristics (droplet distribution, density, and potential droplet drift) were non-significant across the treatments and results from the water‐sensitive paper field test showed adequate penetration with low flow rates. Therefore, a 20% duty cycle was sufficient to defoliate based on the result of the field experiment. Likewise, the defoliants could be applied safely at the duty cycles tested without influencing fiber quality except for nep/gm, length (Ln), L (5%), short fiber content (SFCn), trash content in field 1 and micronaire, nep size, length (Ln), span length (5%), SFC, and fiber fineness in field 2 which were significant. However, the 20% duty cycle significantly reduced the amount of defoliant and would be a good choice for the autonomous cotton defoliation. This is a significant development as there is a huge potential to save on the cost of applying defoliant chemicals and the environment

Agenda of the Fourth Annual Summer Conference, NASA/USRA University Advanced Design Program

Presentations given by the participants at the fourth annual summer conference of the NASA/USRA University Advanced Design Program are summarized. The study topics include potential space and aeronautics projects which could be undertaken during a 20 to 30 year period beginning with the Space Station Initial Operating Configuration (IOC) scheduled for the early to mid-1990's. This includes system design studies for both manned and unmanned endeavors; e.g., lunar launch and landing facilities and operations, variable artificial gravity facility for the Space Station, manned Mars aircraft and delivery system, long term space habitat, construction equipment for lunar bases, Mars oxygen production system, trans-Pacific high speed civil transport, V/STOL aircraft concepts, etc

Desarrollo de geotecnologías aplicadas a la inspección y monitorización de entornos industriales

Tesis por compendio de publicaciones[ES]El desarrollo tecnológico de las últimas dos décadas ha supuesto un cambio radical que está llevando a un nuevo paradigma en el que se entremezclan el mundo físico y el digital. Estos cambios han influido enormemente en la sociedad, modificando las formas de comunicación, acceso a información, ocio, trabajo, etc. Asimismo, la industria ha adoptado estas tecnologías disruptivas, las cuales están contribuyendo a lograr un mayor control y automatización del proceso productivo. En el ámbito industrial, las tareas de mantenimiento son críticas para garantizar el correcto funcionamiento de una planta o instalación, ya que influyen directamente en la productividad y pueden suponer un elevado costo adicional. Las nuevas tecnologías están posibilitando la monitorización continua y a la inspección automatizada, proporcionando herramientas auxiliares a los inspectores que mejoran la detección de fallos y permiten anticipar y optimizar la planificación de las tareas de mantenimiento. Con el objetivo de desarrollar herramientas que aporten mejoras en las tareas de mantenimiento en industria, la presente tesis doctoral se basa en el estudio de como las geotecnologías pueden aportar soluciones óptimas en la monitorización e inspección. Debido a la gran variedad de entornos industriales, las herramientas de apoyo al mantenimiento deben adaptarse a cada caso en concreto. En este aspecto, y con el fin de demostrar la adaptabilidad de la geomática y las geotecnologías, se han estudiado instalaciones industriales de ámbitos muy diversos, como una sala de máquinas (escenario interior), plantas fotovoltaicas (escenario exterior) y soldaduras (interior y exterior). La escala de los escenarios objeto de estudio ha sido muy variada, desde las escalas más pequeñas, para el estudio de las soldaduras y la sala de máquinas, a las escalas más grandes, en los estudios de evolución de la vegetación y presencia de masas de agua en plantas fotovoltaicas. Las geotecnologías demuestran su versatilidad para trabajar a distintas escalas, con soluciones que permiten un gran detalle y precisión, como la fotogrametría de rango cercano y el sistema de escaneado portátil (Portable Mobile Mapping System - PMMS), y otras que pueden abarcar zonas más amplias del territorio, como es el caso de la teledetección o la fotogrametría con drones. Según lo expuesto anteriormente, el enfoque de la tesis ha sido el estudio de elementos o instalaciones industriales a diferentes escalas. En el primer caso se desarrolló una herramienta para el control de calidad externo de soldaduras utilizando fotogrametría de rango cercano y algoritmos para la detección automática de defectos. En el segundo caso se propuso el uso de un PMMS para optimizar la toma de datos en las tareas de inspección en instalaciones fluidomecánicas. En el tercer caso se utilizó la fotogrametría con drones y la combinación de imágenes RGB y térmicas con algoritmos de visión computacional para la detección de patologías en paneles fotovoltaicos. Finalmente, para la monitorización de la vegetación y la detección de masas de agua en el entorno de plantas fotovoltaicas, se empleó la teledetección mediante el cálculo de índices espectrales. [EN]The technological development of the last two decades has brought about a radical change that is leading to a new paradigm in which the physical and digital worlds are intertwined. These changes have had a great impact on society, modifying communication methods, access to information, leisure, work, etc. In addition, the industry has adopted these disruptive technologies, which are contributing to achieving greater control and automation of the production process. In the industrial sector, maintenance tasks are critical to ensuring the proper operation of a plant or facility, as they directly influence productivity and can involve high additional costs. New technologies are making continuous monitoring and automated inspection possible, providing auxiliary tools to inspectors that improve fault detection and allow for the anticipation and optimization of maintenance task planning. With the aim of developing tools that provide improvements in maintenance tasks in industry, this doctoral thesis is based on the study of how geotechnologies can provide optimal solutions in monitoring and inspection. Due to the great variety of industrial environments, maintenance support tools must adapt to each specific case. In this regard, and in order to demonstrate the adaptability of geomatics and geotechnologies, industrial installations from very diverse areas have been studied, such as a machine room (indoor scenario), photovoltaic plants (outdoor scenario), and welding (indoor and outdoor scenarios). The scale of the studied scenarios has been very varied, ranging from smaller scales for the study of welds and machine rooms, to larger scales in the studies of vegetation evolution and the presence of bodies of water in photovoltaic plants. Geotechnologies demonstrate their versatility to work at different scales, with solutions that allow for great detail and precision, such as close-range photogrammetry and the Portable Mobile Mapping System (PMMS), as well as others that can cover larger areas of the territory, such as remote sensing or photogrammetry with drones. The focus of the thesis has been the study of industrial elements or installations at different scales. In the first case, a tool was developed for external quality control of welding, using close-range photogrammetry and algorithms for automatic defect detection. In the second case, the use of a PMMS is proposed to optimize data collection in fluid-mechanical installation inspection tasks. In the third case, drone photogrammetry and the combination of RGB and thermal images with computer vision algorithms were used for the detection of pathologies in photovoltaic panels. Finally, for the monitoring of vegetation and the detection of water masses in the environment of photovoltaic plants, remote sensing was employed through the calculation of spectral indices

The use of modern tools for modelling and simulation of UAV with Haptic

Unmanned Aerial Vehicle (UAV) is a research field in robotics which is in high demand in recent years, although there still exist many unanswered questions. In contrast, to the human operated aerial vehicles, it is still far less used to the fact that people are dubious about flying in or flying an unmanned vehicle. It is all about giving the control right to the computer (which is the Artificial Intelligence) for making decisions based on the situation like human do but this has not been easy to make people understand that it’s safe and to continue the enhancement on it. These days there are many types of UAVs available in the market for consumer use, for applications like photography to play games, to map routes, to monitor buildings, for security purposes and much more. Plus, these UAVs are also being widely used by the military for surveillance and for security reasons. One of the most commonly used consumer product is a quadcopter or quadrotor. The research carried out used modern tools (i.e., SolidWorks, Java Net Beans and MATLAB/Simulink) to model controls system for Quadcopter UAV with haptic control system to control the quadcopter in a virtual simulation environment and in real time environment. A mathematical model for the controlling the quadcopter in simulations and real time environments were introduced. Where, the design methodology for the quadcopter was defined. This methodology was then enhanced to develop a virtual simulation and real time environments for simulations and experiments. Furthermore, the haptic control was then implemented with designed control system to control the quadcopter in virtual simulation and real time experiments. By using the mathematical model of quadcopter, PID & PD control techniques were used to model the control setup for the quadcopter altitude and motion controls as work progressed. Firstly, the dynamic model is developed using a simple set of equations which evolves further by using complex control & mathematical model with precise function of actuators and aerodynamic coefficients Figure5-7. The presented results are satisfying and shows that flight experiments and simulations of the quadcopter control using haptics is a novel area of research which helps perform operations more successfully and give more control to the operator when operating in difficult environments. By using haptic accidents can be minimised and the functional performance of the operator and the UAV will be significantly enhanced. This concept and area of research of haptic control can be further developed accordingly to the needs of specific applications