An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

Background discrimination of EDELWEISS-III cryogenic Ge-detectors for dark matter search

The EDELWEIS-III experiment is a direct dark matter search experiment using cryogenic Ge detectors with dual signal readout in ionization and heat for particle identification and background suppression. Within this thesis, a new data analysis framework has been developed. It features an automatic database driven processing and improved ionization signal filtering. A verification of the processing has been done and the focus of the thesis has been set on the analysis of surface backgrounds

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin

An adaptive autopilot design for an uninhabited surface vehicle

An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.EPSRC (Engineering and Physical Sciences Research Council

Back-end Design of the Readout System for Cryogenic Particle Detectors

Diese Arbeit widmet sich dem Design und der Entwicklung des digitalen Back-Ends (D-BE) für Raumtemperatur-Ausleseelektronik, die in kryogenen Quantendetektoren verwendet wird. Der Schwerpunkt liegt auf Anwendungen im Zusammenhang mit Experimenten zur Kosmischen Hintergrundstrahlung (CMB, im Englischen \textit{Cosmic Microwave Background radiation} genannt), jedoch ist die Technologie anpassbar für Partikeldetektionsexperimente. Zwei Schlüsselprojekte stehen im Mittelpunkt dieser Forschung: das QUBIC-Projekt zur Erkennung der B-Mode-Polarisation des CMB und das ECHo-Experiment, das darauf abzielt, eine neue Obergrenze für die Bestimmung der Neutrinomasse im Sub-eV-Bereich festzulegen. In diesen Projekten werden Übergangskanten-Sensoren (TES) und magnetische Mikrokalorimeter (MMCs) eingesetzt. Im Fall des QUBIC-Projekts werden die TES unter Verwendung von Zeitaufteilungsmultiplexing (TDM) gemultiplext. Es wurde jedoch ein Vorschlag für einen neuen Bolo\-meter-Typ namens Magnetischer Mikrobolometer (MMB) in der QUBIC-Kollaboration vorgestellt, der die Implementierung eines Frequenzaufteilungsmultiplexing (FDM)-Sys\-tems ermöglicht. Dies könnte durch die Verwendung eines Mikrowellen-Supraleiter-Quan\-teninterferenzgerät (SQUID)-Multiplexers ($\mu$MUX) erreicht werden, ähnlich wie bei den MMCs im ECHo-Experiment. Zur Erleichterung der Auslese der gemultiplexten Detektoren wird ein mehrtoniges Signal erzeugt, wobei jede Frequenztonkomponente einen $\mu$MUX-Kanal innerhalb des Kryostaten überwacht. Dieses Signal passiert dann einen rauscharmen Verstärker (LNA, im Englischen \textit{Low-Noise Amplifier} genannt), der in der Regel in der 4 K-Stufe liegt, bevor es das Hochfrequenz-Front-End (RF-FE) erreicht. Das RF-FE umfasst Hochfrequenzelektronik, die sowohl mit dem D-BE als auch mit der Elektronik im Kryostaten verbunden ist. Diese Arbeit stellt eine neuartige Anwendung des Goertzel-Filters zur Kanalisierung von mehrtonigen Signalen vor. Durch Simulationen, die mit einem in dieser Arbeit entwickelten auf Python basierenden Softwarepaket durchgeführt wurden, wurde die optimale Konfiguration für die Signalgenerierung und -erfassung in Bezug auf Rauschleistung, Abschirmung gegen Übersprechen und Systemlinearität ermittelt. Diese Arbeit zeigt, wie dieser Ansatz effizient in einem Field Programmable Gate Array (FPGA) implementiert werden kann, was die Skalierbarkeit bei der Auslese mehrerer Sensoren ermöglicht. Diese Skalierung is im Besonderen in Anwendungen wie Radioteleskopen für CMB-Messungen, kryogenen Kalorimetern für die Partikeldetektion und Quantencomputing entscheidend. Umfangreiche Validierungsexperimente zeigen, wie die Implementierung dieses Filtersatzes die Kanalisierung des mehrtonigen Eingangssignals zur Wiederherstellung der von den Detektoren aufgezeichneten Daten ermöglicht

Proceedings of the NASA Conference on Space Telerobotics, volume 5

Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotics technology to the space systems planned for the 1990's and beyond. Volume 5 contains papers related to the following subject areas: robot arm modeling and control, special topics in telerobotics, telerobotic space operations, manipulator control, flight experiment concepts, manipulator coordination, issues in artificial intelligence systems, and research activities at the Johnson Space Center