72 research outputs found

    An Incrementally Deployed Swarm of MAVs for Localization UsingUltra-Wideband

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    Knowing the position of a moving target can be crucial, for example when localizing a first responder in an emergency scenario. In recent years, ultra wideband (UWB) has gained a lot of attention due to its localization accuracy. Unfortunately, UWB solutions often demand a manual setup in advance. This is tedious at best and not possible at all in environments with access restrictions (e.g., collapsed buildings). Thus, we propose a solution combining UWB with micro air vehicles (MAVs) to allow for UWB localization in a priori inaccessible environments. More precisely, MAVs equipped with UWB sensors are deployed incrementally into the environment. They localize themselves based on previously deployed MAVs and on-board odometry, before they land and enhance the UWB mesh network themselves. We tested this solution in a lab environment using a motion capture system for ground truth. Four MAVs were deployed as anchors and a fifth MAV was localized for over 80 second at a root mean square (RMS) of 0.206 m averaged over five experiments. For comparison, a setup with ideal anchor position knowledge came with 20 % lower RMS, and a setup purely based on odometry with 81 % higher RMS. The absolute scale of the error with the proposed approach is expected to be low enough for applications envisioned within the scope of this paper (e.g., the localization of a first responder) and thus considered a step towards flexible and accurate localization in a priori inaccessible, GNSS-denied environments.acceptedVersio

    Continuous Autonomous UAV Inspection for FPSO vessels

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    This Master's thesis represents the preliminary design study and proposes the unmanned aerial vehicle (UAV) -based inspection framework, comprising several multirotors with automatic charging and deployment for 24/7 integrity inspection tasks. This project has three main topics. First one describes the operational environment and existing regulations that cover use of UAVs. It forms the basis for proposal of the relevant use-case scenarios. Third part comprises two chapters, where design of concept and framework is being based on the previous factors. It shows that before implementation of fully autonomous inspection system, there is a need to cover both regulatory and technical gaps. It can be explained by the fact that there does not exist any autonomous inspection system today. Thus, this project can be seen as a base for future development of the UAV-based inspection system, as it focuses on creation of a general framework

    Swarm Robotics

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    Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

    Towards UAV-assisted monitoring of onshore geological CO2 storage site

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    Scientists all over the world look for solutions to reduce greenhouse gas emissions in an effort to achieve proclaimed emissions reduction targets. An intriguing candidate with the potential to make a substantial contribution to this attempt is carbon capture and storage (CCS). The key advantage of CCS is that it provides the possibility to make a significant impact on the reduction of anthropogenic carbon dioxide (CO2) emissions from power plants and carbon-rich industry processes while maintaining existing fossil fuel energy infrastructure. The technique could therefore be used as a transitional solution until fossil fuels can be eliminated from the energy generation mix, and the energy efficiency of industrial processes as well as appliances and products is further improved. Like other technologies, CCS comes with its risks and rewards. To minimize possible negative impacts on humans as well as on the environment, it is necessary to understand the risks and to address them accordingly. A range of monitoring solutions for geological CO2 storage sites is available. However, a cost-effective solution for the regular observation of atmospheric CO2 concentrations (or tracer concentrations) of large areas above onshore geological CO2 storage sites has yet to be developed. This thesis discusses the use of a helicopter unmanned aerial vehicle (UAV) to fill this gap. The robot platform and its autopilot are designed to cope with ongoing sensor developments in addition to providing safety features necessary for the beyond line-of-sight operation of the UAV. The design focuses on the use of commercial off-the-shelf components for the aerial platform in order to shorten the development time and to reduce costs. The autopilot does neither enforce a specific helicopter model nor defines a set position estimation unit to be used. Access to the control loop enables low-level extensions like obstacle avoidance to be implemented. The developed solution allows the monitoring of an area of approximately 750m2 with one set of batteries in one altitude with a spatial resolution of 2m by 2m. Experiments show that point source leaks of as low as 100kg CO2 per day can be detected and their source located. As opposed to autonomous take-offs of the helicopter UAV, autonomous landings on small dedicated helipads require an accurate localization system. A time difference of arrival (TDOA) based acoustic localization system which is based on planar microphone arrays with at least four microphones is proposed. The system can be embedded into the landing platform and provides the accuracy necessary to land the UAV on a helipad of the size of 1m by 1m. A review of existing TDOA-based approaches is given. Simulations show that the developed approach outperforms its direct competitors for the targeted task. Furthermore, experimental results with the developed UAV confirm the feasibility of the introduced method. The effects of the sensor arrangement onto the quality of the calculated position estimates are also discussed. In order to combine robotic-assisted monitoring solutions and other monitoring strategies (e.g. sensor networks and individual sensors) into a single solution, it is necessary to have a framework which allows next to the measurement data analysis also the management (path changes, robot behavior changes, monitoring of internal robot state) of possibly multiple heterogeneous mobile robotic systems. A modular user interface (UI) framework is proposed which allows robots from different vendors and with various configurations next to individual sensors and sensor networks to be managed from a single application. The software system introduces a strict separation between the robot control software and UIs. UI implementations inside the UI framework can be reused across robot platforms, which can reduce the integration time of new robots significantly. The end user benefits by being able to manage a fleet of robots from various vendors and being able to analyze all the measurement data together in a single solution

    Autonomous Navigation System for a Delivery Drone

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    The use of delivery services is an increasing trend worldwide, further enhanced by the COVID pandemic. In this context, drone delivery systems are of great interest as they may allow for faster and cheaper deliveries. This paper presents a navigation system that makes feasible the delivery of parcels with autonomous drones. The system generates a path between a start and a final point and controls the drone to follow this path based on its localization obtained through GPS, 9DoF IMU, and barometer. In the landing phase, information of poses estimated by a marker (ArUco) detection technique using a camera, ultra-wideband (UWB) devices, and the drone's software estimation are merged by utilizing an Extended Kalman Filter algorithm to improve the landing precision. A vector field-based method controls the drone to follow the desired path smoothly, reducing vibrations or harsh movements that could harm the transported parcel. Real experiments validate the delivery strategy and allow to evaluate the performance of the adopted techniques. Preliminary results state the viability of our proposal for autonomous drone delivery.Comment: 12 pages, 15 figures, extended version of an paper published at the XXIII Brazilian Congress of Automatica, entitled "Desenvolvimento de um drone aut\^onomo para tarefas de entrega de carga

    Towards Precise Positioning and Movement of UAVs for Near-Wall Tasks in GNSS-Denied Environments

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    Abstract: UAVs often perform tasks that require flying close to walls or structures and in environments where a satellite-based location is not possible. Flying close to solid bodies implies a higher risk of collisions, thus requiring an increase in the precision of the measurement and control of the UAV’s position. The aerodynamic distortions generated by nearby walls or other objects are also relevant, making the control more complex and further placing demands on the positioning system. Performing wall-related tasks implies flying very close to the wall and, in some cases, even touching it. This work presents a Near-Wall Positioning System (NWPS) based on the combination of an Ultra-wideband (UWB) solution and LIDAR-based range finders. This NWPS has been developed and tested to allow precise positioning and orientation of a multirotor UAV relative to a wall when performing tasks near it. Specific position and orientation control hardware based on horizontal thrusters has also been designed, allowing the UAV to move smoothly and safely near walls.Ministerio de Ciencia, Innovación y Universidades; RTI2018-101114-B-I00), Xunta de Galicia; ED431C2017/12)

    Exploiting Redundancy for UWB Anomaly Detection in Infrastructure-Free Multi-Robot Relative Localization

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    Ultra-wideband (UWB) localization methods have emerged as a cost-effective and accurate solution for GNSS-denied environments. There is a significant amount of previous research in terms of resilience of UWB ranging, with non-line-of-sight and multipath detection methods. However, little attention has been paid to resilience against disturbances in relative localization systems involving multiple nodes. This paper presents an approach to detecting range anomalies in UWB ranging measurements from the perspective of multi-robot cooperative localization. We introduce an approach to exploiting redundancy for relative localization in multi-robot systems, where the position of each node is calculated using different subsets of available data. This enables us to effectively identify nodes that present ranging anomalies and eliminate their effect within the cooperative localization scheme. We analyze anomalies created by timing errors in the ranging process, e.g., owing to malfunctioning hardware. However, our method is generic and can be extended to other types of ranging anomalies. Our approach results in a more resilient cooperative localization framework with a negligible impact in terms of the computational workload

    Drone heading calculation indoors

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    Abstract. Aim of this master’s thesis was to study drone flying indoors and propose a drone-implemented system that enables the drone heading calculation. In the outdoors, the heading is calculated effectively with a drone’s sensors but using them indoors is limited. Indoor positioning currently has not both low-cost and reliable solution for drone heading calculating. The differences between indoor flying principles and outdoor flying principles of the drone are described in the beginning of the thesis. Then different ways to determine the drone’s heading indoors and how they compare with one another are discussed. Finally, two different heading calculation methods are implemented and tested. The methods are based on using multiple location measurements on the drone and using machine vision together with machine learning. Both methods are affordable and are evaluated to see if they could enable drone flying indoors. First method gives out potential results based on testing results, but it needs further development to be able to always provide reliable heading. Second method shows poor results based on verification.Dronen lentosuunnan laskenta sisätiloissa. Tiivistelmä. Työn tavoitteena oli tutkia dronen lentämistä sisätiloissa ja ehdottaa sitä varten droneen implementoitavaa systeemiä, joka mahdollistaa dronen suunnan laskennan. Ulkona suuntatieto saadaan dronen sensorien avulla, mutta sisätiloissa niiden tarkkuus ei riitä samalla tavalla. Sisätilapaikannuksessa ei ole olemassa sekä edullista että luotettavaa ratkaisua dronen suunnan laskentaan. Työssä perehdytään aluksi dronen lentämisen periaatteisiin sisätiloissa ja miten ne eroavat ulkona lentämisestä. Sitten kerrotaan erilaisista keinoista määrittää dronen suunta sisätiloissa ja niiden keskinäisestä vertailusta. Lopuksi testataan kahta erilaista suunnan-laskenta-menetelmää, jotka perustuvat paikkatiedon käyttöön ja konenäköön yhdessä koneoppimisen kanssa. Menetelmät ovat edullisia ja niiden sopivuutta dronen sisälennätykseen arvioidaan. Ensimmäinen menetelmä antaa hyviä testituloksia mutta tarvitsee lisää jatkokehitystä, jotta se voisi antaa aina luotettavaa suuntatietoa. Toinen menetelmä antaa heikkoja tuloksia verifioinnin perusteella
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