428 research outputs found

    Bearing-only Formation Control of multiple UAVs with an NMPC approach: architectures and methodologies

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    openUno dei maggior campi di sviluppo e d’innovazione nel nostro secolo riguarda l’ambito della robotica mobile in particolare lo studio del coordinamento di sistemi multi agenti. Questo tipo di tecnologia permette di essere impiegata in ambiti di calamitĂ  naturali o situazioni critiche dove i sistemi multi agenti posso operare missioni di search and rescue o monitoraggio ambientale. In campo vengono impiegati veicoli terrestri autonomi UGV (Unmanned Ground Vehicle) e veicoli aerei autonomi UAV(Unmanned Aerial Vehicle) che possono cooperare tra di loro. Gli ambienti esterni presentano diverse problematiche e una di queste puĂČ essere la mancanza di accesso alla rete GPS. Per tale motivo si studiano leggi di controllo che non impiegano questo tipo di informazioni come bearing-only formation control dove gli agenti rilevano tra di loro solo angoli relativi, i quali possono essere calcolati tramite una semplice camera 2D. Questo lavoro di tesi ha lo scopo di esplorare la teoria della bearing rigidity applicata a formazioni di agenti omogenei nello specifico quadricotteri sfruttando la tecnologia NMPC con MATMPC. La tesi va ad esplorare le differenze delle diverse architetture in particolare va ad implementare una architettura centralizzata e mette giĂč le basi per un possibile sviluppo di un’architettura decentralizzata. Il lavoro mostra come Ăš creare il modello per NMPC con diverse varianti del modello dell’agente (come Ăš stato modellizzato), diversi funzionali di costo e diverse formazioni. Per l’implementazione viene usato ROS2 per lo sviluppo dell’intero ecosistema di controllo e viene usata la piattaforma PX4 che mette a disposizione un ottima architettura di controllo a basso livello per la gestione del singolo quadricottero. Inoltre la tesi usa il sistema SITL(Software In The Loop) di PX4 per rendere le simulazioni di Gazebo il piĂč vicine alla realtĂ .One of the major fields of development and innovation in our century concerns mobile robotics, particularly the study of multi-agent system coordination. This type of technol- ogy can be employed in natural disaster scenarios or critical situations where multi-agent systems can perform search and rescue missions or environmental monitoring. In this field, autonomous ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) are used, which can cooperate with each other. Outdoor environments pose various challenges, and one of these challenges can be the lack of access to GPS network. For this reason, control laws that do not rely on GPS information are studied, such as ”bearing-only formation control,” where agents only perceive relative angles between each other, which can be calculated using a simple 2D camera. The purpose of this thesis work is to explore the theory of bearing rigidity applied to forma- tions of homogeneous agents, specifically quadcopters, using Nonlinear Model Predictive Control (NMPC) with MATMPC. The thesis aims to explore the differences between dif- ferent architectures, particularly by implementing a centralized architecture and laying the groundwork for a possible development of a decentralized architecture. The work demon- strates how to create the model for NMPC with various agent model variants,different cost functions, and different formations. ROS2 is used for the implementation of the entire control ecosystem, and the PX4 platform is used, which provides an excellent low-level control architecture for managing individual quadcopters. Additionally, the thesis utilizes the PX4 Software In The Loop (SITL) system to make Gazebo simulations as close to reality as possible

    On the motion planning & control of nonlinear robotic systems

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    In the last decades, we saw a soaring interest in autonomous robots boosted not only by academia and industry, but also by the ever in- creasing demand from civil users. As a matter of fact, autonomous robots are fast spreading in all aspects of human life, we can see them clean houses, navigate through city traffic, or harvest fruits and vegetables. Almost all commercial drones already exhibit unprecedented and sophisticated skills which makes them suitable for these applications, such as obstacle avoidance, simultaneous localisation and mapping, path planning, visual-inertial odometry, and object tracking. The major limitations of such robotic platforms lie in the limited payload that can carry, in their costs, and in the limited autonomy due to finite battery capability. For this reason researchers start to develop new algorithms able to run even on resource constrained platforms both in terms of computation capabilities and limited types of endowed sensors, focusing especially on very cheap sensors and hardware. The possibility to use a limited number of sensors allowed to scale a lot the UAVs size, while the implementation of new efficient algorithms, performing the same task in lower time, allows for lower autonomy. However, the developed robots are not mature enough to completely operate autonomously without human supervision due to still too big dimensions (especially for aerial vehicles), which make these platforms unsafe for humans, and the high probability of numerical, and decision, errors that robots may make. In this perspective, this thesis aims to review and improve the current state-of-the-art solutions for autonomous navigation from a purely practical point of view. In particular, we deeply focused on the problems of robot control, trajectory planning, environments exploration, and obstacle avoidance

    Virtual Structures Based Autonomous Formation Flying Control for Small Satellites

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    Many space organizations have a growing need to fly several small satellites close together in order to collect and correlate data from different satellite sensors. To do this requires teams of engineers monitoring the satellites orbits and planning maneuvers for the satellites every time the satellite leaves its desired trajectory or formation. This task of maintaining the satellites orbits quickly becomes an arduous and expensive feat for satellite operations centers. This research develops and analyzes algorithms that allow satellites to autonomously control their orbit and formation without human intervention. This goal is accomplished by developing and evaluating a decentralized, optimization-based control that can be used for autonomous formation flight of small satellites. To do this, virtual structures, model predictive control, and switching surfaces are used. An optimized guidance trajectory is also develop to reduce fuel usage of the system. The Hill-Clohessy-Wiltshire equations and the D\u27Amico relative orbital elements are used to describe the relative motion of the satellites. And a performance comparison of the L1, L2, and L∞ norms is completed as part of this work. The virtual structure, MPC based framework combined with the switching surfaces enables a scalable method that allows satellites to maneuver safely within their formation, while also minimizing fuel usage

    Droonien autonominen kamerapohjainen navigointi metsÀssÀ

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    In recent years, the autonomous flying of drones has been an actively researched topic in both commercial and academic organizations. Most autopilots can fly autonomously in open areas where Global navigation satellite systems (GNSS) are available. However, inside dense forest environments, the localization of the drone cannot rely on GNSS, and the drone also has to avoid obstacles in the path. The objective of this thesis was to design and implement a prototype of an autonomous drone flying under the canopy for boreal forest research purposes. To establish a starting point, a literature survey on available open-source solutions was performed. Based on the literature survey, EGO-Planner-v2 with VINS-Fusion localization and stereo depth camera-based mapping was chosen as the base of the implemented prototype. The system was tested both in a simulator and in real forest environments with custom drone hardware. The performance of the system, and its suitability for boreal forest environments, were evaluated based on the success of the mission, reliability of the obstacle avoidance, and the accuracy of the localization. Based on the results, the performance of the system was promising in sparse forests. In the sparse mixed forest, eight of nine flights were successful, when approximate flight distances varied between 13 m and 18 m. However, in dense forests, the sensing of small needleless branches needs to be improved to increase reliability. In the dense spruce forest, nine of 19 test flights were successful, when approximate flight distances varied between 35 m and 80 m. In the longest, approximately 80 m long, test flight, the error of the VINS-Fusion estimate of the trajectory length was approximately 1 m.Viime vuosina droonien autonominen lentÀminen on ollut aktiivisesti tutkittu aihe sekÀ akateemisissa ettÀ kaupallisissa organisaatioissa. Useimmat droonien autopilotit pystyvÀt lentÀmÀÀn autonomisesti avoimilla alueilla, missÀ paikannuksessa voidaan hyödyntÀÀ globaaleja paikannussatelliitteja (GNSS). LennettÀessÀ tiheissÀ metsissÀ satelliittipaikannus ei kuitenkaan ole mahdollista, ja droonin tÀytyy lisÀksi kyetÀ vÀistelemÀÀn esteitÀ. TÀmÀn työn tavoitteena oli toteuttaa metsÀtutkimukseen tarkoitettu prototyyppi autonomisesti metsÀn sisÀllÀ lentÀvÀstÀ droonista. Työn alussa toteutettiin kirjallisuustutkimus viime aikoina julkaistuihin avoimen lÀhdekoodin ratkaisuihin metsÀn sisÀllÀ lentÀvistÀ autonomisista drooneista. Kirjallisuustutkimuksen perusteella toteutettavan prototyypin pohjaksi valittiin EGO-Planner-v2, joka kÀytti paikannukseen VINS-Fusionia ja kartoitukseen stereo-syvyyskameraa. SysteemiÀ testattiin sekÀ simulaattorissa ettÀ oikeissa metsissÀ itserakennetulla droonilla. SuorituskykyÀ ja soveltuvuutta pohjoisiin metsÀympÀristöihin arvioitiin lentojen onnistumisen, esteiden vÀistelyn luotettavuuden ja paikannuksen tarkkuuden perusteella. Tulosten perusteella suorituskyky oli lupaava puustoltaan harvoissa metsissÀ. Puustoltaan harvassa sekametsÀssÀ kahdeksan yhdeksÀstÀ testilennosta onnistui, kun testilentojen pituudet vaihtelivat noin 13 metristÀ noin 18 metriin. Kuitenkin tiheissÀ metsissÀ pienten havuttomien oksien havainnointia tÀytyy kehittÀÀ navigoinnin luotettavuuden parantamiseksi. TiheÀssÀ kuusimetsÀssÀ yhdeksÀn 19 testilennosta onnistui, kun lentojen pituudet vaihtelivat noin 35 metristÀ 80 metriin. PisimmÀssÀ, noin 80 metriÀ pitkÀssÀ, testilennossa VINS-Fusionin estimaatissa lennon pituudeksi virhe oli noin yksi metri

    MODELING OF INNOVATIVE LIGHTER-THAN-AIR UAV FOR LOGISTICS, SURVEILLANCE AND RESCUE OPERATIONS

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    An unmanned aerial vehicle (UAV) is an aircraft that can operate without the presence of pilots, either through remote control or automated systems. The first part of the dissertation provides an overview of the various types of UAVs and their design features. The second section delves into specific experiences using UAVs as part of an automated monitoring system to identify potential problems such as pipeline leaks or equipment damage by conducting airborne surveys.Lighter-than-air UAVs, such as airships, can be used for various applications, from aerial photography, including surveying terrain, monitoring an area for security purposes and gathering information about weather patterns to surveillance. The third part reveals the applications of UAVs for assisting in search and rescue operations in disaster situations and transporting natural gas. Using PowerSim software, a model of airship behaviour was created to analyze the sprint-and-drift concept and study methods of increasing the operational time of airships while having a lower environmental impact when compared to a constantly switched-on engine. The analysis provided a reliable percentage of finding the victim during patrolling operations, although it did not account for victim behaviour. The study has also shown that airships may serve as a viable alternative to pipeline transportation for natural gas. The technology has the potential to revolutionize natural gas transportation, optimizing efficiency and reducing environmental impact. Additionally, airships have a unique advantage in accessing remote and otherwise inaccessible areas, providing significant benefits in the energy sector. The employment of this technology was studied to be effective in specific scenarios, and it will be worth continuing to study it for a positive impact on society and the environment

    Portable Robotic Navigation Aid for the Visually Impaired

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    This dissertation aims to address the limitations of existing visual-inertial (VI) SLAM methods - lack of needed robustness and accuracy - for assistive navigation in a large indoor space. Several improvements are made to existing SLAM technology, and the improved methods are used to enable two robotic assistive devices, a robot cane, and a robotic object manipulation aid, for the visually impaired for assistive wayfinding and object detection/grasping. First, depth measurements are incorporated into the optimization process for device pose estimation to improve the success rate of VI SLAM\u27s initialization and reduce scale drift. The improved method, called depth-enhanced visual-inertial odometry (DVIO), initializes itself immediately as the environment\u27s metric scale can be derived from the depth data. Second, a hybrid PnP (perspective n-point) method is introduced for a more accurate estimation of the pose change between two camera frames by using the 3D data from both frames. Third, to implement DVIO on a smartphone with variable camera intrinsic parameters (CIP), a method called CIP-VMobile is devised to simultaneously estimate the intrinsic parameters and motion states of the camera. CIP-VMobile estimates in real time the CIP, which varies with the smartphone\u27s pose due to the camera\u27s optical image stabilization mechanism, resulting in more accurate device pose estimates. Various experiments are performed to validate the VI-SLAM methods with the two robotic assistive devices. Beyond these primary objectives, SM-SLAM is proposed as a potential extension for the existing SLAM methods in dynamic environments. This forward-looking exploration is premised on the potential that incorporating dynamic object detection capabilities in the front-end could improve SLAM\u27s overall accuracy and robustness. Various experiments have been conducted to validate the efficacy of this newly proposed method, using both public and self-collected datasets. The results obtained substantiate the viability of this innovation, leaving a deeper investigation for future work

    Tracking and Grasping of Moving Objects Using Aerial Robotic Manipulators: A Brief Survey

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    Unmanned Aerial Vehicles (UAV) has evolved in recent years, their features have changed to be more useful to the society, although some years ago the drones had been thought to be teleoperated by humans and to take some pictures from above, which is useful; nevertheless, nowadays the drones are capable of developing autonomous tasks like tracking a dynamic target or even grasping different kind of objects. Some task like transporting heavy loads or manipulating complex shapes are more challenging for a single UAV, but for a fleet of them might be easier. This brief survey presents a compilation of relevant works related to tracking and grasping with aerial robotic manipulators, as well as cooperation among them. Moreover, challenges and limitations are presented in order to contribute with new areas of research. Finally, some trends in aerial manipulation are foreseeing for different sectors and relevant features for these kind of systems are standing out

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Cyber-Human Systems, Space Technologies, and Threats

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    CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp

    Vision-based Marker-less Landing of a UAS on Moving Ground Vehicle

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    In recent years the use of unmanned air systems (UAS) has seen extreme growth. These small, often inexpensive platforms have been used to aid in tasks such as search and rescue, medicinal deliveries, disaster relief and more. In many use cases UAS work alongside unmanned ground vehicles (UGVs) to complete autonomous tasks. For end-to-end autonomous cooperation, the UAS needs to be able to autonomously take off and land on the UGV. Current autonomous landing solutions often use fiducial markers to aid in localizing the UGV relative to the UAS, an external ground computer to aid in computation, or gimbaled cameras on-board the UAS. This thesis seeks to demonstrate a vision-based autonomous landing system that does not rely on the use of fiducial markers, completes all computations on-board the UAS, and uses a fixed, non-gimbaled camera. Algorithms are tailored towards low size, weight, and power constraints as all compute and sensing components weigh less than 100 grams. The foundation of this thesis extends upon current efforts by localizing the UGV relative to the UAS using neural network object detection and camera intrinsic properties instead of common place fiducial markers. An object detection neural network is used to detect the UGV within an image captured by the camera on-board the UAS. Then a localization algorithm utilizes the UGV’s pixel position within the image to estimate the UGV’s position relative to the UAS. This estimated position of the UGV will be passed into a command generator that sends setpoints to the on-board PX4 flight control unit (FCU). This autonomous landing system was developed and validated within a high-fidelity simulation environment before conducting outdoor experiments
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