Innovations in the Field of On-Board Scheduling Technologies

Space missions are characterized by long distances, difficult or unavailable communication and high operating costs. Moreover, complexity has been constantly increasing in recent years. For this reason, improving the autonomy of space operators is an attractive goal to increase the mission reward with lower costs. This paper proposes an onboard scheduler, that integrates inside an onboard software framework for mission autonomy. Given a set of activities, it is responsible for determining the starting time of each activity according to their priority, order constraints, and resource consumption. The presented scheduler is based on linear integer programming and relies on the use of a branch-and-cut solver. The technology has been tested on an Earth Observation scenario, comparing its performance against the state-of-the-art scheduling technology

Pressurized 1U CubeSat Propulsion System

Since the advent of CubeSats, the demand to push mission capabilities of the form-factor has steadily increased. Propulsion systems are one of the driving factors pushing CubeSats towards increasingly complex missions. One propulsion device, an electrothermal plasma thruster known as Pocket Rocket strikes a balance between performance and cost efficiency, in line with the spirit of the CubeSat standard. The thruster has previously been integrated and tested within a 1U CubeSat form factor. However, while functional, the design lacked sufficient propellant storage capability for most missions. To increase propellant storage capability, a 1U CubeSat form factor where the structure itself is a pressure vessel is developed. The Pocket Rocket thruster is embedded into the structure, with batteries, power processing unit (PPU), and propellant regulation and delivery system contained within the pressure vessel. Containing electronic components inside the pressure vessel assists with radiation and thermal protection systems. When used as part of a generic 3U CubeSat mission, the pressurized 1U form factor is capable of producing between 5 and 50 m/s of Δv

1U CubeSatでのバイナリ画像分類用に設計された畳み込みニューラルネットワーク

As of 2020, more than a thousand CubeSats have been launched into space. The nanosatellite standard allowed launch providers to utilize empty spaces in their rockets while giving educational institutions, research facilities and commercial start-up companies the chance to build, test and operate satellites in orbit. This exponential rise in the number of CubeSats has led to an increasing number of diverse missions. Missions on astrobiology, state-of-art technology demonstration, high revisit-time earth observation and space weather have been implemented. In 2018, NASA’s JPL demonstrated CubeSat’s first use in deep space by launching MarCO A and MarCO B. The CubeSats successfully relayed information received from InSight Mars Lander in Mars to Earth. Increasing complexity in missions, however, require increased access to data. Most CubeSats still rely on extremely low data rates for data transfer. Size, Weight and Power (SWaP) requirements for 1U are stringent and rely on VHF/UHF bands for data transmission. Kyushu Institute of Technology’s BIRDS-3 Project has downlink rate of 4800bps and takes about 2-3 days to reconstruct a 640x480 (VGA) image on the ground. Not only is this process extremely time consuming and manual but it also does not guarantee that the image downlinked is usable. There is a need for automatic selection of quality data and improve the work process. The purpose of this research is to design a state-of-art, novel Convolutional Neural Network (CNN) for automated onboard image classification on CubeSats. The CNN is extremely small, efficient, accurate, and versatile. The CNN is trained on a completely new CubeSat image dataset. The CNN is designed to fulfill SWaP requirements of 1U CubeSat so that it can be scaled to fit in bigger satellites in the future. The CNN is tested on never-before-seen BIRDS-3 CubeSat test dataset and is benchmarked against SVM, AE and DBN. The CNN automatizes images selection on-orbit, prioritizes quality data, and cuts down operation time significantly.九州工業大学博士学位論文 学位記番号：工博甲第510号 学位授与年月日：令和2年12月28日1 Introduction|2 Convolutional Neural Networks|3 Methodology|4 Results|5 Conclusion九州工業大学令和2年

Artificial Intelligence for Small Satellites Mission Autonomy

Space mission engineering has always been recognized as a very challenging and innovative branch of engineering: since the beginning of the space race, numerous milestones, key successes and failures, improvements, and connections with other engineering domains have been reached. Despite its relative young age, space engineering discipline has not gone through homogeneous times: alternation of leading nations, shifts in public and private interests, allocations of resources to different domains and goals are all examples of an intrinsic dynamism that characterized this discipline. The dynamism is even more striking in the last two decades, in which several factors contributed to the fervour of this period. Two of the most important ones were certainly the increased presence and push of the commercial and private sector and the overall intent of reducing the size of the spacecraft while maintaining comparable level of performances. A key example of the second driver is the introduction, in 1999, of a new category of space systems called CubeSats. Envisioned and designed to ease the access to space for universities, by standardizing the development of the spacecraft and by ensuring high probabilities of acceptance as piggyback customers in launches, the standard was quickly adopted not only by universities, but also by agencies and private companies. CubeSats turned out to be a disruptive innovation, and the space mission ecosystem was deeply changed by this. New mission concepts and architectures are being developed: CubeSats are now considered as secondary payloads of bigger missions, constellations are being deployed in Low Earth Orbit to perform observation missions to a performance level considered to be only achievable by traditional, fully-sized spacecraft. CubeSats, and more in general the small satellites technology, had to overcome important challenges in the last few years that were constraining and reducing the diffusion and adoption potential of smaller spacecraft for scientific and technology demonstration missions. Among these challenges were: the miniaturization of propulsion technologies, to enable concepts such as Rendezvous and Docking, or interplanetary missions; the improvement of telecommunication state of the art for small satellites, to enable the downlink to Earth of all the data acquired during the mission; and the miniaturization of scientific instruments, to be able to exploit CubeSats in more meaningful, scientific, ways. With the size reduction and with the consolidation of the technology, many aspects of a space mission are reduced in consequence: among these, costs, development and launch times can be cited. An important aspect that has not been demonstrated to scale accordingly is operations: even for small satellite missions, human operators and performant ground control centres are needed. In addition, with the possibility of having constellations or interplanetary distributed missions, a redesign of how operations are management is required, to cope with the innovation in space mission architectures. The present work has been carried out to address the issue of operations for small satellite missions. The thesis presents a research, carried out in several institutions (Politecnico di Torino, MIT, NASA JPL), aimed at improving the autonomy level of space missions, and in particular of small satellites. The key technology exploited in the research is Artificial Intelligence, a computer science branch that has gained extreme interest in research disciplines such as medicine, security, image recognition and language processing, and is currently making its way in space engineering as well. The thesis focuses on three topics, and three related applications have been developed and are here presented: autonomous operations by means of event detection algorithms, intelligent failure detection on small satellite actuator systems, and decision-making support thanks to intelligent tradespace exploration during the preliminary design of space missions. The Artificial Intelligent technologies explored are: Machine Learning, and in particular Neural Networks; Knowledge-based Systems, and in particular Fuzzy Logics; Evolutionary Algorithms, and in particular Genetic Algorithms. The thesis covers the domain (small satellites), the technology (Artificial Intelligence), the focus (mission autonomy) and presents three case studies, that demonstrate the feasibility of employing Artificial Intelligence to enhance how missions are currently operated and designed

High-brightness lasers with spectral beam combining on silicon

Modern implementations of absorption spectroscopy and infrared-countermeasures demand advanced performance and integration of high-brightness lasers, especially in the molecular fingerprint spectral region. These applications, along with others in communication, remote-sensing, and medicine, benefit from the light source comprising a multitude of frequencies. To realize this technology, a single multi-spectral optical beam of near-diffraction-limited divergence is created by combining the outputs from an array of laser sources. Full integration of such a laser is possible with direct bonding of several epitaxially-grown chips to a single silicon (Si) substrate. In this platform, an array of lasers is defined with each gain material, creating a densely spaced set of wavelengths similar to wavelength division multiplexing used in communications.Scaling the brightness of a laser typically involves increasing the active volume to produce more output power. In the direction transverse to the light propagation, larger geometries compromise the beam quality. Lengthening the cavity provides only limited scaling of the output power due to the internal losses. Individual integrated lasers have low brightness due to combination of thermal effects and high optical intensities. With heterogeneous integration, many lasers can be spectrally combined on a single integrated chip to scale brightness in a compact platform. Recent demonstrations of 2.0-µm diode and 4.8-µm quantum cascade lasers on Si have extended this heterogeneous platform beyond the telecommunications band to the mid-infrared.In this work, low-loss beam combining elements spanning the visible to the mid-infrared are developed and a high-brightness multi-spectral laser is demonstrated in the range of 4.6–4.7-µm wavelengths. An architecture is presented where light is combined in multiple stages: first within the gain-bandwidth of each laser material and then coarsely between each spectral band to a single output waveguide. All components are demonstrated on a common material platform with a Si substrate, which lends feasibility to the complete system integration. Particular attention is focused on improving the efficiency of arrayed waveguide gratings (AWGs), used in the dense wavelength combining stage. This requires development of a refined characterization technique involving AWGs in a ring-resonator configuration to reduce measurement uncertainty. New levels of low-loss are achieved for visible, near-infrared, and mid-infrared multiplexing devices. Also, a multi-spectral laser in the mid-infrared is demonstrated by integrating an array of quantum cascade lasers and an AWG with Si waveguides. The output power and spectra are measured, demonstrating efficient beam combining and power scaling. Thus, a bright laser source in the mid-infrared has been demonstrated, along with an architecture and the components for incorporating visible and near-infrared optical bands