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

On-Board Deep Learning for Payload Data Processing: Hardware Performance Comparison

The path towards a multi-planetary species passes through the implementation of disruptive technological innovation. Artificial Intelligence and autonomy on spacecraft will be a fundamental part of this future. Hence, leveraging on-the-edge AI accelerators, such as FPGAs, GPUs, VPUs, ASICs, will constitute an essential component of the spacecraft hardware of tomorrow. This work presents a comparative work, specifically targeted to the use of on-board satellites. The tested platforms are Intel Myriad X, Nvidia Jetson Nano, and CPU (x64 architecture

A tool for nano-satellite functional verification: comparison between different inthe-loop simulation configurations

This paper describes the simulator technology and the verification campaign for the e-st@r CubeSats family, developed at Politecnico di Torino. The satellites’ behavior has been investigated using a Model and Simulation Based Approach. One of the critical issue in the verification and validation of any space vehicle is the impossibility to fully test some features due to the particular and often un-reproducible environment in which it will operate. Simulations result as one of the best means for testing space system capabilities as it may help to overcome the abovementioned problem. In order to perform different simulation configurations for e-st@r CubeSats, an in-house simulator (named StarSim) has been developed. It is a unique infrastructure, modular and versatile, capable of supporting any desired configuration of the system under test, ranging from full algorithm in the loop simulations (AIL), and gradually inserting satellite hardware, until a complete hardware in the loop (HIL) simulation is performed. When a verification campaign is led on a real object, pure AIL computer based simulations (in which all the equipment and mission conditions are reproduced by virtual models) are not sufficient to test the actual software and hardware to a high degree of confidence since real systems can exhibit random and unpredictable dynamics difficult to be perfectly modeled (i.e. communication delays, uncertainties, and so on). For these reasons, Software In The Loop (SIL), Controller In The Loop (CIL) and HIL simulations were planned. SIL simulations foresee that algorithms are written in the final programming language and executed on ground hardware. In CIL simulations, the software runs on the flight processor while other system’s element are still kept virtual. In HIL simulation, the real hardware (i.e. sensors, actuators, and power sources) are included in the loop. In this paper, after the details of the simulator architecture and its characteristics are described, an exhaustive comparison between AIL and HIL simulations is presented, highlighting main differences and singularities: similar trends of the sensible system’s variables are reached but not identical performances (i.e. absolute and average pointing error and stability, attitude determination accuracy, battery charging and discharging duration) arose analyzing the values. Moreover, it is demonstrated how the technology here presented can effectively support and improve the verification and validation activities for a nano-satellite, by increasing the confidence level on the mission objectives achievement

Autonomous Neuro-Fuzzy Solution for Fault Detection and Attitude Control of a 3U Cubesat

In recent years, thanks to the increase of the know-how on machine-learning techniques and the advance of the computational capabilities of on-board processing, algorithms involving artificial intelligence (i.e. neural networks and fuzzy logics) have began to spread even in the space applications. Nowadays, thanks to these reasons, the implementation of such techniques is becoming realizable even on smaller platforms, such as CubeSats. The paper presents an algorithm for the fault detection and for the fault-tolerant attitude control of a 3U CubeSat, developed in MathWorks Matlab & Simulink environment. This algorithm involves fuzzy logic and multi-layer feed-forward online-trained neural network (percep- tron). It is utilized in a simulation of a CubeSat satellite placed in LEO, considering as available attitude con- trol actuators three magnetic torquers and one reaction wheel. In particular, fuzzy logics are used for the fault detection and isolation, while the neural network is employed for adapting the control to the perturbation introduced by the fault. The simulation is performed considering the attitude of the satellite known without measurement error. In addition, the paper presents the system, simulator and algorithm architecture, with a particular focus on the design of fuzzy logics (connection and implication operators, rules and input/output qualificators) and the neural network architecture (number of layers, neurons per layer), threshold and activation func- tions, offline and online training algorithm and its data management. With respect to the offline training, a model predictive controller has been adopted as supervisor. In con- clusion the paper presents the control torques, state variables and fuzzy output evolution, in the different faulty configurations. Results show that the implementation of the fuzzy logics joined with neural networks provide good ro- bustness, stability and adaptibility of the system, allowing to satisfy specified performance requirements even in the event of a malfunctioning of a system actuator

An AI-Based Goal-Oriented Agent for Advanced On-Board Automation

In the context of fierce competition arising in the space economy, the number of satellites and constellations that will be placed in orbit is set to increase considerably in the upcoming years. In such a dynamic environment, raising the autonomy level of the next space missions is key to maintaining a competitive edge in terms of the scientific, technological, and commercial outcome. We propose the adoption of an AI-based autonomous agent aiming to fully enable spacecraft’s goal-oriented autonomy. The implemented cognitive architecture collects input starting from the sensing of the surrounding operating environment and defines a low-level schedule of tasks that will be carried out throughout the specified horizon. Furthermore, the agent provides a planner module designed to find optimal solutions that maximize the outcome of the pursued objective goal. The autonomous loop is closed by comparing the expected outcome of these scheduled tasks against the real environment measurements. The entire algorithmic pipeline was tested in a simulated operational environment, specifically developed for replicating inputs and resources relative to Earth Observation missions. The autonomous reasoning agent was evaluated against the classical, non-autonomous, mission control approach, considering both the quantity and the quality of collected observation data in addition to the quantity of the observation opportunities exploited throughout the simulation time. The preliminary simulation results point out that the adoption of our software agent enhances dramatically the effectiveness of the entire mission, increasing and optimizing in-orbit activities, on the one hand, reducing events\u27 response latency (opportunities, failures, malfunctioning, etc.) on the other. In the presentation, we will cover the description of the high-level algorithmic structure of the proposed goal-oriented reasoning model, as well as a brief explanation of each internal module’s contribution to the overall agent’s architecture. Besides, an overview of the parameters processed as input and the expected algorithms\u27 output will be provided, to contextualize the placement of the proposed solution. Finally, an Earth Observation use case will be used as the benchmark to test the performances of the proposed approach against the classical one, highlighting promising conclusions regarding our autonomous agent’s adoption

Universal Constraints on Low-Energy Flavour Models

It is pointed out that in a general class of flavour models one can identify certain universally present FCNC operators, induced by the exchange of heavy flavour messengers. Their coefficients depend on the rotation angles that connect flavour and fermion mass basis. The lower bounds on the messenger scale are derived using updated experimental constraints on the FCNC operators. The obtained bounds are different for different operators and in addition they depend on the chosen set of rotations. Given the sensitivity expected in the forthcoming experiments, the present analysis suggests interesting room for discovering new physics. As the highlights emerge the leptonic processes, $\mu\rightarrow e\gamma$, $\mu\rightarrow eee$ and $\mu\rightarrow e$ conversion in nuclei.Comment: 18 pages, 3 figures; v2 matches published versio

From Flavour to SUSY Flavour Models

If supersymmetry (SUSY) will be discovered, successful models of flavour not only have to provide an explanation of the flavour structure of the Standard Model fermions, but also of the flavour structure of their scalar superpartners. We discuss aspects of such "SUSY flavour" models, towards predicting both flavour structures, in the context of supergravity (SUGRA). We point out the importance of carefully taking into account SUSY-specific effects, such as 1-loop SUSY threshold corrections and canonical normalization, when fitting the model to the data for fermion masses and mixings. This entangles the flavour model with the SUSY parameters and leads to interesting predictions for the sparticle spectrum. We demonstrate these effects by analyzing an example class of flavour models in the framework of an SU(5) Grand Unified Theory with a family symmetry with real triplet representations. For flavour violation through the SUSY soft breaking terms, the class of models realizes a scheme we refer to as "Trilinear Dominance", where flavour violation effects are dominantly induced by the trilinear terms.Comment: 44 pages, 10 figures, version published in Nuclear Physics

Combined explanations of B-physics anomalies: the sterile neutrino solution

In this paper we provide a combined explanation of charged- and neutral-current B-physics anomalies assuming the presence of a light sterile neutrino NR which contributes to the B \u2192 D(*)\u3c4\u3bd processes. We focus in particular on two simplified models, where the mediator of the flavour anomalies is either a vector leptoquark U1\u3bc 3c (3, 1, 2/3) or a scalar leptoquark S1 3c (3\uaf , 1, 1/3). We find that U1\u3bc can successfully reproduce the required deviations from the Standard Model while being at the same time compatible with all other flavour and precision observables. The scalar leptoquark instead induces a tension between Bs mixing and the neutral-current anomalies. For both states we present the limits and future projections from direct searches at the LHC finding that, while at present both models are perfectly allowed, all the parameter space will be tested with more luminosity. Finally, we study in detail the cosmological constraints on the sterile neutrino NR and the conditions under which it can be a candidate for dark matter

NEURAL NETWORKS TO INCREASE THE AUTONOMY OF INTERPLANETARY NANOSATELLITE MISSIONS

To accomplish more ambitious scientific goals of interplanetary nanosatellite missions, a certain set of technological challenges need to be addressed to enhance systems performance. An area of particular interest is mission autonomy. Increasing the degree of mission autonomy might help overcoming the limitations imposed by the typical low data rate and the simplified ground segment of small missions. This research aims at supporting the development of more autonomous spacecraft by exploiting the potentialities of artificial intelligence. An artificial neural network is proposed to enable a set of autonomous operations, for example to select what payload data are useful for the mission and must be sent to Earth, and what data can be discarded. The algorithm is developed and tested on a case study represented by a CubeSat mission to a near Earth asteroid that requires the autonomous detection of an impact event on the asteroid surface. The proposed algorithm demonstrates the feasibility of a novel training approach based on optimized datasets created directly in-situ using images taken by the spacecraft on-board camera. The validity of the algorithm is demonstrated through several simulations, considering different scenarios and disturbances. The research presented in this paper can be extended to other applications of the artificial neural networks, such as autonomous failure detection and isolation, also in conjunction with other artificial intelligence approaches