Star trackers (STR) are optical sensors that are widely used on spacecraft for inertial attitude determination. The optical characteristics of STR hardware are optimized for the acquisition of star images. To enhance the accuracy of star centroiding algorithms, STRs intentionally operate with defocused images and with exposure times high enough to properly observe faint objects.In recent years, a number of space missions have explored the potential of vision-based navigation (VBN) strategies relying on images from dedicated navigation cameras. This paper presents a limb-based optical navigation strategy specifically adapted to use STR instead of typical navigation cameras. Indeed, using STRs to perform VBN could simplify spacecraft design and reduce costs. The image processing (IP) pipeline relies on portions of the scene captured by the sensor to detect the lunar limb and estimate the position of the Moon relative to the satellite. The full state of the satellite is then reconstructed using an extended Kalman filter. The study examines the variation in achievable performance with STR exposure time and assess the statistical robustness of the algorithm to various sources of uncertainty with a Monte Carlo campaign.
@inproceedings{balossi2024GNSki,title={Moon Limb-Based Autonomous Optical Navigation Using Star Trackers},author={Balossi, Claudia and Piccolo, Felice and Panicucci, Paolo and Pugliatti, Mattia and Topputo, Francesco and Capolupo, Francesco},booktitle={46th AAS Guidance, Navigation and Control Conference, Breckenridge, Colorado},pages={1--19},year={2024},month=feb,}
Resource-Constrained Vision-Based Relative Navigation About Small Bodies
Felice Piccolo, Claudia Balossi, Paolo Panicucci, Mattia Pugliatti, Francesco Topputo, and Francesco Capolupo
In 46th AAS Guidance, Navigation and Control Conference, Breckenridge, Colorado, Feb 2024
Vision-based navigation is a common technique for spacecraft operating in close proximity of small bodies. Past missions have consistently used it to navigate spacecraft in challenging and partially known dynamical environments. However, they relied extensively on ground-based and human-in-the-loop processing, meaning that communication delays limited spacecraft reactivity and that large amount of images needed to be transmitted from the spacecraft to Earth. In recent years a significant push towards autonomy has emerged in the space sector. This would decrease mission costs, increase scientific return and reduce the operational effort of deep-space communication assets. In this work, a resource-constrained visionbased navigation algorithm for on-board use is presented. By working only with patches or windows extracted from the input images, on-board computational and storage requirements are significantly reduced while keeping the same level of accuracy of a baseline implementation. The performance of the algorithm is verified through a Monte Carlo campaign in which various sources of uncertainty are considered, including camera calibration parameters. Furthermore, a comparison is carried out between the computational time required by a standard version of the algorithm and the window-based one.
@inproceedings{piccolo2024GNSki,title={Resource-Constrained Vision-Based Relative Navigation About Small Bodies},author={Piccolo, Felice and Balossi, Claudia and Panicucci, Paolo and Pugliatti, Mattia and Topputo, Francesco and Capolupo, Francesco},booktitle={46th AAS Guidance, Navigation and Control Conference, Breckenridge, Colorado},pages={1--18},year={2024},month=feb,}
2023
A Multi-Scale Labelled Dataset for Boulder Segmentation and Navigation on Small Bodies
Mattia Pugliatti, and Michele Maestrini
In 74th International Astronautical Congress, Baku, Azerbaijan, Oct 2023
The capability to detect boulders of multiple sizes on the surface of small bodies is beneficial for vision-based applications around minor bodies. Such ability, however, faces challenging implementations due to distinct and irregular shapes, properties, and distribution of the boulder’s population, rapid variability in the illumination conditions, and lack of publicly available datasets. This paper introduces a multi-scale labeled dataset for boulder segmentation and navigation tasks around small celestial bodies addressing the aforementioned challenges. The authors generate image-label pairs using an artificial environment through a comprehensive methodology, achieving domain randomization for robust data-driven image processing algorithms. The dataset comprises diverse camera positions, illumination conditions, and boulder distributions. Post-processing includes noise addition, data augmentation, and scale variation, resulting in a dataset of 47,502 samples. Statistical analyses of input and output labels provide insights into geometric conditions and boulders’ appearance. This open-access dataset offers valuable resources for developing and testing data-driven methods, facilitating access to a standardized benchmark that researchers can use to design and compare results
@inproceedings{pugliatti2023IAC,title={A Multi-Scale Labelled Dataset for Boulder Segmentation and Navigation on Small Bodies},author={Pugliatti, Mattia and Maestrini, Michele},booktitle={74th International Astronautical Congress, Baku, Azerbaijan},pages={1--12},year={2023},month=oct,dataset={https://zenodo.org/records/8406581},}
The image processing of Milani: challenges after DART impact
Mattia Pugliatti, Carmine Giordano, and Francesco Topputo
The Milani CubeSat mission aims to observe and study the Didymos binary system after deployment from the Hera mothercraft. The image processing of Milani uses optical observables of the primary body extracted from images to estimate its center of mass and enable autonomous onboard navigation. The algorithm employs a data-driven approach, with coefficients tuned based on observations and new data that can also be collected from other spacecraft. Using an updated version of the primary shape after the DART mission, a significant drop in performance has been observed due to the inability of the algorithm to establish a meaningful relationship between images and phase angles, challenging the original design of the image processing algorithm. In this work, this issue is investigated and proven to be caused by the oblate shape of the primary. Also, different alternative approaches are proposed considering additional parameters extracted from images, using polynomial chaos expansion, neural, and convolutional networks. The findings provide valuable insights for the adoption of data-driven methods in interplanetary missions, emphasizing the need for robust and adaptable algorithms to account for changes in target characteristics, especially when considering small-body missions
@inproceedings{pugliatti2023oblateness,title={The image processing of Milani: challenges after DART impact},author={Pugliatti, Mattia and Giordano, Carmine and Topputo, Francesco},booktitle={ESA-GNC conference, Sopot, Poland},pages={1--15},year={2023},month=jun,dataset={https://zenodo.org/record/7962714},}
The CubeSat Mission FUTURE: a Preliminary Analysis to Validate the On-Board Autonomous Orbit Determination
Carmine Buonagura, Salvatore Borgia, Mattia Pugliatti, Alessandro Morselli, Francesco Topputo, Filippo Corradino, Pierluigi Visconti, Luca Deva, Alberto Fedele, Giuseppe Leccese, and Silvia Natalucci
FUTURE is a 6U CubeSat whose aim is to make advancements in the autonomy of spacecraft in providing position knowledge and reducing reliance on operators and ground support services and facilities. The mission aims at flying a set of sensors on a singe satellite in LEO, and use the data generated to feed different artificial intelligence algorithms to identify features on the Earth’s surface and to elaborate position knowledge. The outcome of the project will be directed toward future applications beyond LEO, such as in missions about planets and moons, enhancing autonomous operation and navigation in the proximity of different celestial bodies. The purpose of this work is to provide an updated overview of the FUTURE mission, and to illustrate the architecture and some preliminary results of the autonomous navigation strategy.
@inproceedings{buonagura2023Future,title={The CubeSat Mission FUTURE: a Preliminary Analysis to Validate the On-Board Autonomous Orbit Determination},author={Buonagura, Carmine and Borgia, Salvatore and Pugliatti, Mattia and Morselli, Alessandro and Topputo, Francesco and Corradino, Filippo and Visconti, Pierluigi and Deva, Luca and Fedele, Alberto and Leccese, Giuseppe and Natalucci, Silvia},booktitle={ESA-GNC conference, Sopot, Poland},pages={1--15},year={2023},month=jun,}
The Hera Milani CubeSat mission
Carmine Giordano, Fabio Ferrari, Vittorio Franzese, Mattia Pugliatti, Felice Piccolo, Antonio Rizza, Tomas Kohout, Fabrizio Dirri, A Longobardo, Gisellu C, Ernesto Palomba, Margherita Cardi, and
3 more authors
@inproceedings{giordano2023Milani,title={The Hera Milani CubeSat mission},author={Giordano, Carmine and Ferrari, Fabio and Franzese, Vittorio and Pugliatti, Mattia and Piccolo, Felice and Rizza, Antonio and Kohout, Tomas and Dirri, Fabrizio and Longobardo, A and C, Gisellu and Palomba, Ernesto and Cardi, Margherita and Perez-Lissi, Franco and Martino, Paolo and Carnelli, Ian},booktitle={5th COSPAR Symposium, 2023},year={2023},month=apr,}
2022
Boulders identification on small bodies under varying illumination conditions
Mattia Pugliatti, and Francesco Topputo
In 3rd Space Imaging Workshop, Georgia, Atlanta, Oct 2022
The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as navigation and hazard detection during critical operations. This task is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. The authors address this challenge by designing a multi-step training approach to develop a data-driven image processing pipeline to robustly detect and segment boulders scattered over the surface of a small body. Due to the limited availability of labeled image-mask pairs, the developed methodology is supported by two artificial environments designed in Blender specifically for this work. These are used to generate a large amount of synthetic image-label sets, which are made publicly available to the image processing community. The methodology presented addresses the challenges of varying illumination conditions, irregular shapes, fast training time, extensive exploration of the architecture design space, and domain gap between synthetic and real images from previously flown missions. The performance of the developed image processing pipeline is tested both on synthetic and real images, exhibiting good performances, and high generalization capabilities.
@inproceedings{pugliatti2022boulders,title={Boulders identification on small bodies under varying illumination conditions},author={Pugliatti, Mattia and Topputo, Francesco},booktitle={3rd Space Imaging Workshop, Georgia, Atlanta},pages={1--12},year={2022},month=oct,dataset={https://zenodo.org/record/7107409#.ZF5GS-xBxQI},}
Enhanced Vision-Based Algorithms about Small Bodies: Lessons learned from the Stardust-R experience
Mattia Pugliatti, and Francesco Topputo
In 2nd International Stardust conference, STARCON2, Oct 2022
In this work, the author collects lessons learned and future challenges identified in the field of enhanced vision-based navigation algorithms. These points are collected after 3 years of research experience within the H2020 Stardust-R network and pose points of discussion for future trends within the space community
@inproceedings{pugliatti2022enhanced,title={Enhanced Vision-Based Algorithms about Small Bodies: Lessons learned from the Stardust-R experience},author={Pugliatti, Mattia and Topputo, Francesco},booktitle={2nd International Stardust conference, STARCON2},volume={1},pages={1--2},year={2022},}
Enhanced Vision-Based Algorithms about Small Bodies: Lessons learned from the Stardust-R experience
Mattia Pugliatti, Fabio Ferrari, Felice Piccolo, Antonio Rizza, Claudio Bottiglieri, Vittorio Franzese, Carmine Giordano, and Francesco Topputo
In 2nd International Stardust conference, STARCON2, Oct 2022
In this work, we present an overview of the design pipeline adopted for the Milan mission. The pipeline is presented as a generalized methodology that can be used to design any close-proximity mission about a small body. Its key elements are also addressed for the specific case of the Milani mission in relation to the changes that will need to be addressed following the updates from the DART mission.
@inproceedings{pugliatti2022pipeline,title={Enhanced Vision-Based Algorithms about Small Bodies: Lessons learned from the Stardust-R experience},author={Pugliatti, Mattia and Ferrari, Fabio and Piccolo, Felice and Rizza, Antonio and Bottiglieri, Claudio and Franzese, Vittorio and Giordano, Carmine and Topputo, Francesco},booktitle={2nd International Stardust conference, STARCON2},volume={1},pages={1--2},year={2022},}
Hardware-In-the-loop Simulation Framework for CubeSats Proximity Operations: Application to the Milani Mission
Antonio Rizza, Felice Piccolo, Mattia Pugliatti, Paolo Panicucci, and Francesco Topputo
In 73rd International Astronautical Congress, Paris, France, Oct 2022
Milani is a 6U CubeSat that will be released by Hera in proximity of the Didymos binary asteroid. The spacecraft will demonstrate autonomous Guidance Navigation and Control (GNC) capability for CubeSats in deep space, enhancing the scientific outcome of the mission. The Deep-space Astrodynamics Research and Technology (DART) Group at Politecnico di Milano is responsible for Milani Mission Analysis (MA), GNC and Image Processing (IP) design. Operations in proximity of minor bodies demand high levels of autonomy to achieve cost-effective, safe, and reliable solutions. The on-board software has a central role in these applications, thus it must be extensively tested and validated to satisfy mission requirements and to guarantee robustness to uncertainties. A robust and standardized methodology to design, validate, and test vision-based Attitude and Orbit Control Systems (AOCS) algorithms is fundamental to achieve fast prototyping while facing at the same time limited availability of resources and time. This paper presents a modular and flexible approach, developed at DART lab, to test GNC algorithms with camera- and processor-in-the-loop simulations. This framework is characterized by three elements: 1) a functional engineering simulator for six-degrees-of-freedom closed-loop analyses, 2) a vision-based navigation test-bench for camera-in-the-loop simulations, and 3) a single-board computer to test the algorithm in a representative computational environment. The first element is the modular CUBesat ORbit and GNC (CUBORG) tool, developed in MATLAB/Simulink. This contains a high-fidelity model of the environment suitable for simulating different operative scenarios, and a prototype of the spacecraft AOCS. Camera-in-the loop simulations are performed thanks to the in-house developed Tiny Versatile 3D Reality Simulation Environment (TinyV3RSE). This is composed of a high-resolution screen which displays synthetic images as they would be acquired from the probe during the mission, and stimulates, through a collimator, the camera mounted in the facility. The third element is a Raspberry Pi which is selected as external board to run processor-in-the-loop simulations. The proposed approach is tested on the Milani case simulating the GNC and IP subsystems in a real-hardware environment, doing a step forward towards the hardware-in-the-loop validation and verification of them.
@inproceedings{rizza2022hardware,title={Hardware-In-the-loop Simulation Framework for CubeSats Proximity Operations: Application to the Milani Mission},author={Rizza, Antonio and Piccolo, Felice and Pugliatti, Mattia and Panicucci, Paolo and Topputo, Francesco},booktitle={73rd International Astronautical Congress, Paris, France},pages={1--15},year={2022},month=oct,}
Object Recognition Algorithms for the Didymos Binary System
Mattia Pugliatti, Felice Piccolo, and Francesco Topputo
In 2nd International Conference on Applied Intelligence and Informatics, Oct 2022
Optical-based navigation in a binary system such as the Didymos one poses new challenges in terms of image processing capabilities, in particular for what concerns the recognition between the primary and secondary bodies. In this work, the baseline object recognition algorithm used in the Milani mission to distinguish between Didymos and Dimorphos is evaluated against alternative image processing pipelines which use convolutional pooling architectures and machine learning approaches. The tasks of the proposed alternatives is to detect the secondary in the image and to define a bounding box around it. It is shown that these algorithms are capable of robustly predicting the presence of the secondary albeit performing poorly at predicting the components of the bounding box, which is a task that is performed quite robustly by the baseline algorithm. A new paradigm is therefore proposed which merges the strengths of both approaches into a unique pipeline that could be implemented on-board Milani.
@inproceedings{pugliatti2022object,title={Object Recognition Algorithms for the Didymos Binary System},author={Pugliatti, Mattia and Piccolo, Felice and Topputo, Francesco},booktitle={2nd International Conference on Applied Intelligence and Informatics},volume={2},pages={1--20},year={2022},publisher={AII},}
Deep Learning for Navigation of Small Satellites About Asteroids: an Introduction to the Deepnav Project
Carmine Buonagura, Mattia Pugliatti, Vittorio Franzese, Francesco Topputo, Aurel Zeqaj, Marco Zannoni, Mattia Varile, Ilaria Bloise, Federico Fontana, Francesco Rossi, Lorenzo Feruglio, and Mauro Cardone
In 2nd International Conference on Applied Intelligence and Informatics, Oct 2022
CubeSats represent the new frontier of space exploration, as they provide cost savings in terms of production and launch opportunities by being able to be launched as opportunity payloads. In addition, interest in minor bodies is gradually increasing because of the richness and exploitability of the materials present throughout their surface, the scientific return they could yield, and their dangerousness. Moreover, they are characterized by a highly harsh environment. These are the reasons why greater autonomous capabilities are desirable for future space missions. Optical navigation is one of the most promising technique for retrieving spacecraft state, enabling navigation autonomy. Unfortunately, most of these methods cannot be implemented on-board because of their computational burden. This paper presents the “Deep Learning for Navigation of Small Satellites about Asteroids” project, in short “DeepNav”, whose aim is to change the current navigation paradigm by exploiting artificial intelligence algorithms for on-board optical navigation. As a result, DeepNav will evaluate the performance of fast and light artificial intelligence-based orbit determination for the proximity operations phase around asteroids.
@inproceedings{buonagura2022deep,title={Deep Learning for Navigation of Small Satellites About Asteroids: an Introduction to the Deepnav Project},author={Buonagura, Carmine and Pugliatti, Mattia and Franzese, Vittorio and Topputo, Francesco and Zeqaj, Aurel and Zannoni, Marco and Varile, Mattia and Bloise, Ilaria and Fontana, Federico and Rossi, Francesco and Feruglio, Lorenzo and Cardone, Mauro},booktitle={2nd International Conference on Applied Intelligence and Informatics},volume={2},pages={1--20},year={2022},publisher={AII},}
Trajectory design and orbit determination of Hera’s Milani CubeSat
Claudio Bottiglieri, Felice Piccolo, Antonio Rizza, Carmine Giordano, Mattia Pugliatti, Vittorio Franzese, Fabio Ferrari, and Francesco Topputo
In Advances in the Astronautical Sciences, Oct 2022
Hera is the European contribution to the ESA-NASA collaboration AIDA. During the mission, two CubeSats will be released in proximity of the binary asteroid 65803-Didymos: Milani and Juventas. In this work, some challenging aspects of the mission analysis of Milani are presented. Original trajectory design solutions are devised as a response to demanding scientific and operational requirements in a low-gravity environment. Then, a navigation strategy based on a combination of radiometric and optical measurements is presented and results of the knowledge analysis are shown for the main phases of the mission.
@inproceedings{bottiglieri2022trajectory,title={Trajectory design and orbit determination of Hera’s Milani CubeSat},author={Bottiglieri, Claudio and Piccolo, Felice and Rizza, Antonio and Giordano, Carmine and Pugliatti, Mattia and Franzese, Vittorio and Ferrari, Fabio and Topputo, Francesco},booktitle={Advances in the Astronautical Sciences},volume={177},pages={81--82},year={2022},publisher={Univelt},}
On-board Small-Body Semantic Segmentation Based on Morphological Features with U-Net
Mattia Pugliatti, Michele Maestrini, Pierluigi Di Lizia, Francesco Topputo, and others
In Advances in the Astronautical Sciences, Oct 2022
Small-bodies such as asteroids and comets exhibit great variability in surface morphological features. These are often unknown beforehand but can be exploited for hazard avoidance during landing, autonomous planning of scientific observations, and for navigation purposes. The detection and classification of such features is a laborious task that requires extensive manual work done by experts in the field. This step renders online usage of images unfeasible for these applications. Such limitation could be overcome thanks to the recent advances in the field of neural networks, which allow to recognize features automatically from an acquired image. However, to train such networks, an annotated dataset needs to be generated with care by field experts, thus requiring once again extensive work and human-in-the-loop. In this work, a methodology that exploits an open-source rendering software, ray-tracing masking, and simple image processing techniques is illustrated, which allows to automatize the segmentation process and build up a robust database of labeled features (i.e. background, surface, craters, boulders, and the terminator region) for small-bodies. A procedural code is designed to generate images and their labels over 7 different small-body shapes for a total of 12,550 images that are used to train a Convolutional Neural Network with a U-Net architecture in the task of semantic segmentation. The performances of the network are then analyzed in 4 different scenarios. First, the network is evaluated on a test set composed of 1,050 new images belonging to bodies seen during training. Secondly, the network is evaluated on 3,000 synthetic images from 2 models that have not been encountered in training. Afterward, one of these latter models is tested in a flyby trajectory scenario consisting of 56 images. The results of the first three tests show state of the art performances and the capability of this method to generalize features across synthetic data. Finally, the network’s performances are qualitatively assessed with a set of 59 real images from previously flown missions, highlighting the current limits of this approach. These shortcomings suggest possible directions for future improvement, which are discussed in this work.
@inproceedings{pugliatti2022board,title={On-board Small-Body Semantic Segmentation Based on Morphological Features with U-Net},author={Pugliatti, Mattia and Maestrini, Michele and Di Lizia, Pierluigi and Topputo, Francesco and others},booktitle={Advances in the Astronautical Sciences},volume={176},pages={603--622},year={2022},publisher={Univelt},}
Navigation about irregular bodies through segmentation maps
Mattia Pugliatti, and Francesco Topputo
In Advances in the Astronautical Sciences, Oct 2022
Optical navigation about small-bodies can be performed at different scales and with different techniques during proximity operations. Traditional methods however are influenced by pixel intensity due to illumination conditions and often provide a navigation solution only when coupled with filtering techniques. In this work, a navigation method for small-body applications is presented that makes use of segmentation maps. By converting a grayscale image into its segmented equivalent the pixel content is highly reduced but at the same time its meaning is enriched since the pixel value is providing direct information on feature type and distribution across space. This is exploited in an autonomous navigation method in two steps. A Convolutional Neural Network is designed to generate a rough estimate of the position of a spacecraft in a small-body fixed reference frame, whose surrounding has been divided into 1176 classes. A Normalized Cross-Correlation technique is then applied to the reduced search space to generate a precise position estimate. The methodology proposed is trained and validated on a database of segmented synthetic maps of 49716 samples of Didymos and Hartley each, while a series of 5 scenarios are tested. The CNN is capable to predict the correct class with an accuracy of 75.94% and 68.60% respectively for Didymos and Hartley, while the overwhelming majority of the other cases are predicted just next to the correct classes. The CNN is robust to various illumination conditions, is capable to work outside the range of distances considered during training, performs well when predicted masks are used, and also selects independently the type of features to rely on for classification depending on the body. When coupled with NCC, a position estimate with a relative error below 5-8% the range from the asteroid can be achieved.
@inproceedings{pugliatti2022navigation,title={Navigation about irregular bodies through segmentation maps},author={Pugliatti, Mattia and Topputo, Francesco},booktitle={Advances in the Astronautical Sciences},volume={176},pages={1169--1187},year={2022},publisher={Univelt},}
Mission analysis and navigation assessment for hera’s milani cubesat
Claudio Bottiglieri, Felice Piccolo, Antonio Rizza, Mattia Pugliatti, Vittorio Franzese, Carmine Giordano, Fabio Ferrari, and Francesco Topptuo
Hera is the European contribution to the ESA-NASA collaboration AIDA. During the mission, two CubeSats will be released in proximity of the binary asteroid 65803-Didymos: Milani and Juventas. In this work, an updated overview about the Milani mission analysis and navigation assessment are presented. The mission profile and the trajectories are shown. Then, a navigation assessment is presented, and the results are shown for the main phases of the mission.
@inproceedings{bottiglieri2022mission,title={Mission analysis and navigation assessment for hera’s milani cubesat},author={Bottiglieri, Claudio and Piccolo, Felice and Rizza, Antonio and Pugliatti, Mattia and Franzese, Vittorio and Giordano, Carmine and Ferrari, Fabio and Topptuo, Francesco},booktitle={4S Symposium},pages={1--20},year={2022},}
Procedural Minor Body Generator Tool for Data-Driven Optical Navigation Methods
Carmine Buonagura, Mattia Pugliatti, and Francesco Topputo
In 6th CEAS Specialist Conference on Guidance, Navigation and Control-EuroGNC, May 2022
Minor bodies show great variability in shape and surface morphological features. Since in the proximity of these bodies the dynamic is highly non-linear, communication windows with Earth could not be sufficient to navigate around them. Moreover, there is a growing demand for the reduction of costs deriving from ground-stations operations. As a result, the need for spacecraft with autonomous navigation capabilities both in far and close ranges. The most promising navigation technique is the optical one which allows the estimation of state information by exploiting optical observables extracted from images. To assess the robustness of optical navigation methods, it is required to perform tests on a variety of body shapes and with different surface morphological features. The importance of these tests arises from the shape and surface morphology estimation errors of ground-based estimation techniques. In this work, a procedural minor body generator tool implemented with an open-source 3D computer graphics software is described. The starting point of the tool is the three–dimensional mesh of a generic minor body which is procedurally modified by introducing craters, boulders, and surface roughness to obtain a photorealistic model in a real-looking environment. Two families of models can be generated by default: rocky ones, characterized by a large number of different-sized boulders, and cometary ones characterized by the typical morphology of comets, consisting of alternating rough and smooth regions, with the presence of small boulders.
@inproceedings{buonagura2022procedural,title={Procedural Minor Body Generator Tool for Data-Driven Optical Navigation Methods},author={Buonagura, Carmine and Pugliatti, Mattia and Topputo, Francesco},booktitle={6th CEAS Specialist Conference on Guidance, Navigation and Control-EuroGNC},year={2022},month=may,}
Design of the on-board image processing of the Milani mission
Mattia Pugliatti, Vittorio Franzese, Antonio Rizza, Felice Piccolo, Claudio Bottiglieri, Carmine Giordano, Fabio Ferrari, and Francesco Topputo
In 44th AAS Guidance, Navigation and Control Conference, May 2022
Milani is a 6U CubeSat that will visit the Didymos binary system as part of the Hera mission. Its objectives are both scientific and technological: to study and characterize the asteroid environment, and to demonstrate the use of CubeSat technologies for interplanetary missions. The latter includes optical-based autonomous navigation algorithms in a close-proximity environment, which are enabled by robust image processing functions. In this work, for the first time, the design of the image processing of Milani is described in detail. Its algorithmic core is divided among two blocks: the blobs characterization and the observables extraction. The former one extracts low-level optical observables while distinguishing the primary from the secondary of the Didymos system. The latter processes the input of the previous block to generate higher-level observables such as the center of figure, the range, and the phase angle. These estimates are generated thanks to data-driven functions which are tuned on a global dataset representative of the geometric conditions which Milani would encounter during its mission. After a detailed description of its functionalities, the image processing is tested on two datasets representative of the nominal mission phases: the far range phase and the close range phase. After the characterization of the various algorithms, it is demonstrated that Milani’s image processing is capable of robustly generating a set of optical observables to be used on-board by the GNC and the rest of the CubeSat.
@inproceedings{pugliatti2022design,title={Design of the on-board image processing of the Milani mission},author={Pugliatti, Mattia and Franzese, Vittorio and Rizza, Antonio and Piccolo, Felice and Bottiglieri, Claudio and Giordano, Carmine and Ferrari, Fabio and Topputo, Francesco},booktitle={44th AAS Guidance, Navigation and Control Conference},pages={1--21},year={2022},}
Toward verification and validation of the Milani Image Processing pipeline in the hardware-in-the-loop testbench TinyV3RSE
Felice Piccolo, Mattia Pugliatti, Paolo Panicucci, and Francesco Topputo
In 44th AAS Guidance, Navigation and Control Conference, May 2022
Verification and validation is an essential step for image processing and vision based navigation algorithms. Typically these algorithms are designed mainly with synthetic images, which implies a urgent need to study their behaviour under realistic environmental conditions. To achieve this task, DART Lab has designed the hardware-in-the-loop testbench TinyV3RSE, which can be used both to support fundamental research and to increase the maturity of mission-specific algorithms. Among its projects, DART Lab is responsible for the mission analysis and the GNC subsystem of Milani, one of the two CubeSats of ESA’s Hera mission. A crucial component of its semi-autonomous vision-based GNC subsystem is the image processing, which, to this date, has been designed and tested only with synthetic images. In this work the weighted center of brightness algorithm, an essential part of Milani’s image processing, is tested using TinyV3RSE. In particular, the robustness of the algorithm to challenging geometrical conditions, different exposure times, and blur levels is investigated. The results allowed to prove the robustness of the algorithm and to gather insight into the effect of camera hardware on the image processing software: a preliminary but essential step towards the full validation and verification of Milani’s image processing.
@inproceedings{piccolo2022toward,title={Toward verification and validation of the Milani Image Processing pipeline in the hardware-in-the-loop testbench TinyV3RSE},author={Piccolo, Felice and Pugliatti, Mattia and Panicucci, Paolo and Topputo, Francesco},booktitle={44th AAS Guidance, Navigation and Control Conference},pages={1--21},year={2022},}
Improvements and Applications of the DART Vision-Based Navigation Test Bench TINYV3RSE
Paolo Panicucci, Mattia Pugliatti, Vittorio Franzese, and Francesco Topputo
In 44th AAS Guidance, Navigation and Control Conference, May 2022
Image processing and vision-based navigation algorithms require images for design, testing, and validation. For space exploration purposes, it is complex if not impossible to retrieve realistic images. To mitigate this, two approaches can be used: high-fidelity rendering of celestial bodies or hardware-in-the-loop testing. In this work, we focus on the latter by elaborating on the design, implementation, validation, and calibration of a vision-based navigation test bench called TinyV3RSE. The design of such facility has been a collaborative effort at the Deep-space Astrodynamics Research & Technology (DART) group, which will benefit from its usage in various projects and missions in which is involved. In this work the facility design, the current calibration procedure, and also some preliminary results are presented. These are focused on the image processing in a small-body mission and on the performance of an optical navigation algorithm about the Moon.
@inproceedings{panicucci2022improvements,title={Improvements and Applications of the DART Vision-Based Navigation Test Bench TINYV3RSE},author={Panicucci, Paolo and Pugliatti, Mattia and Franzese, Vittorio and Topputo, Francesco},booktitle={44th AAS Guidance, Navigation and Control Conference},pages={1--19},year={2022},}
TINYV3RSE: The DART Vision-Based Navigation Test-bench
Mattia Pugliatti, Vittorio Franzese, Paolo Panicucci, and Francesco Topputo
Image processing and vision-based navigation algorithms require images for design, testing, and validation. For space exploration purposes, it is complex if not impossible to retrieve realistic images. To mitigate this, two approaches can be used: high-fidelity rendering of celestial bodies or hardware-in-the-loop testing. In this work, we focus on the latter by elaborating on the design, implementation, validation, and calibration of a vision-based navigation test bench called TinyV3RSE . The design of such facility has been a collaborative effort at the Deep-space Astrodynamics Research & Technology (DART) group, which will benefit from its usage in various projects and missions in which is involved. In this work, for the first time, we present the facility design, the current calibration procedure, and also some preliminary results. These are focused on the image processing in a small-body mission and on the performance of a traditional and well-known optical navigation algorithm about the Moon.
@inproceedings{pugliatti2022tinyv3rse,title={TINYV3RSE: The DART Vision-Based Navigation Test-bench},author={Pugliatti, Mattia and Franzese, Vittorio and Panicucci, Paolo and Topputo, Francesco},booktitle={AIAA Scitech 2022 Forum},pages={1193},year={2022},doi={10.2514/6.2022-1193},}
The Milani mission: overview and architecture of the optical-based GNC system
Mattia Pugliatti, Antonio Rizza, Felici Piccolo, Vittorio Franzese, Claudio Bottiglieri, Carmine Giordano, Fabio Ferrari, and Francesco Topputo
Milani is a 6U CubeSat that will be part of the Hera mission around the Didymos binary system. Its objectives are both scientific and technological: to study and characterize the Didymos environment, and to demonstrate the use of CubeSat technologies for interplanetary missions. The latter includes the usage of autonomous navigation algorithms in a close-proximity environment. The purpose of this work is twofold. First, to provide an updated overview of the Milani mission. Second, for the first time, to illustrate the architecture and some preliminary results of the semi-autonomous optical-based GNC system.
@inproceedings{pugliatti2022milani,title={The Milani mission: overview and architecture of the optical-based GNC system},author={Pugliatti, Mattia and Rizza, Antonio and Piccolo, Felici and Franzese, Vittorio and Bottiglieri, Claudio and Giordano, Carmine and Ferrari, Fabio and Topputo, Francesco},booktitle={AIAA Scitech 2022 Forum},pages={2381},year={2022},doi={10.2514/6.2022-2381},}
2021
Small-body shape recognition with convolutional neural network and comparison with explicit features based method
Mattia Pugliatti, and Francesco Topputo
In Advances in the astronautical sciences, Aug 2021
Small-bodies such as asteroids and comets exhibit a wide variety of shapes and surface characteristics that are often unknown beforehand. Because of that, traditional exploration approaches do not make use of shape information on-board the spacecraft. This work would like to propose an approach based on Convolutional Neural Networks (CNN) to provide such type of information for on-board image processing and compare it with three more traditional approaches based on explicit image features such as Hu invariant moments, Fourier descriptors and polar outlines. A group of 8 different small-body shapes is chosen as archetype set and a database of images is generated to train these 4 techniques in the classification task. Their performances are then analyzed in three different scenarios. First, they are analyzed on the test set split from the database. In the second one the CNN is used to classify the shape of new objects that are not part of the archetype set. Lastly, all techniques are used under varying illumination conditions on some models from the archetype set. The CNN classifier outperforms the other methods, reaching an accuracy of 98.52%, meaningful classification on new models and a robust behaviour under varying illumination conditions. The latter property can be used for efficient training of the CNN with a smaller database. Given the promising results, the CNN classifier is proposed for onboard implementation to provide shape information. Other important results of this work are the identification of an irregularity index for small-bodies and the definition of a shape profile as a fingerprint of the 3D object under varying perspective.
@inproceedings{pugliatti2021shape,title={Small-body shape recognition with convolutional neural network and comparison with explicit features based method},author={Pugliatti, Mattia and Topputo, Francesco},booktitle={Advances in the astronautical sciences},year={2021},month=aug,volume={175},pages={2539--2258},publisher={Univelt},}
The Hera Milani CubeSat Mission
Francesco Topputo, Fabio Ferrari, Vittorio Franzese, Mattia Pugliatti, Carmine Giordano, Antonio Rizza, Daniele Calvi, Giorgio Ammirante, Fabrizio Stesina, Antonio Esposito, Sabrina Corpino, Pierluigi Visconti, and
8 more authors
The Hera mission is the European component of the Asteroid Impact and Deflection Assessment (AIDA) international cooperation between ESA and NASA. Upon its arrival and rendezvous with binary asteroid Didymos in early 2027, Hera plans to deploy two CubeSats in the close-proximity of the binary system. Interplanetary CubeSats provide low-cost opportunities to extend the scientific and technological return of exploration missions. Hera’s CubeSats, named Milani and Juventas, will be the first nanosatellites to orbit in the close proximity of a small celestial body and to perform scientific and technological operations around a binary asteroid.
@inproceedings{topputo2021hera,title={The Hera Milani CubeSat Mission},author={Topputo, Francesco and Ferrari, Fabio and Franzese, Vittorio and Pugliatti, Mattia and Giordano, Carmine and Rizza, Antonio and Calvi, Daniele and Ammirante, Giorgio and Stesina, Fabrizio and Esposito, Antonio and Corpino, Sabrina and Visconti, Pierluigi and Diaz de Cerio Goenaga, Rainer and Corradino, Filippo and Santoni, Alessandro and Cardi, Margerita and Kohout, Tomas and Perez-Lissi, Franco and Martino, Paolo and Carnelli, Ian},booktitle={7th IAA Planetary Defense Conference},pages={163},year={2021},}
Using Blender As Contact Dynamics Engine For Cubesat Landing Simulations Within Impact Crater On Dimorphos
Pelayo Peñarroya, Mattia Pugliatti, Simone Centuori, and Francesco Topputo
@inproceedings{pelayo2021blender,title={Using Blender As Contact Dynamics Engine For Cubesat Landing Simulations Within Impact Crater On Dimorphos},author={Peñarroya, Pelayo and Pugliatti, Mattia and Centuori, Simone and Topputo, Francesco},booktitle={7th IAA Planetary Defense Conference},year={2021},volume={2},issue={22662.91206},}
2017
NEOT\omegaIST–Design Study of a Kinetic Impactor Demonstration Mission Featuring NEO Spin Change and Observer Sub-spacecraft
Kilian Engel, Mattia Pugliatti, Line Drube, L Cano Juan, Eggl Siegfried, Daniel Hestroffer, Albert Falke, Ulrich Johann, and Alan Harris
Near Earth Object (NEO) deflection for the purpose of Earth impact prevention is recognized as a valid and valuable endeavour. NEOTωIST stands for Near-Earth Object Transfer of angular momentum (ω) Spin Test. This describes a demonstration mission intended to develop the capabilities required to execute an effective kinetic impactor NEO deflection mission. The mission concept features several novel aspects. The most important of these are a new technique to quantify momentum transfer from the Impactor spacecraft to the NEO, and the option to use small sub-spacecraft for observation purposes. For momentum transfer measurement, the NEO is struck off-center which changes its spin rate. This rate change, which can be measured from Earth via light curve measurements, allows quantification of the transferred momentum without the need for a rendezvousing observer spacecraft. To gain additional information about the impact geometry and impact physics, the NEOTωIST Impactor may deploy one or several sub-spacecraft from the main Impactor spacecraft shortly before impact. These sub-spacecraft allow observation of the impact event from multiple vantage points some of which are unique because their destruction is accepted. Different mission variations exist depending on whether the mission is implemented with only the Impactor or with a complement of observing sub-spacecraft. The paper discusses these options. Overall, the concept promises comparatively low cost and features capabilities that are unique and valuable for an operational deflection or reconnaissance mission. The overall mission concept and measurement principle are described in detail in a different abstract by L. Drube et al. This paper focuses on the results of a preliminary mission design study with the objective of demonstrating mission feasibility. It analyses the key technical challenges of the mission, and describes the concept of operations as well as geometry design for the impact and fly-by phase of the mission. Further, the communications architecture between the different spacecraft and Earth is discussed, and the preliminary design of the individual spacecraft is presented.
@article{engel2017neotomegaist,title={NEOT$\omega$IST--Design Study of a Kinetic Impactor Demonstration Mission Featuring NEO Spin Change and Observer Sub-spacecraft},author={Engel, Kilian and Pugliatti, Mattia and Drube, Line and Juan, L Cano and Siegfried, Eggl and Hestroffer, Daniel and Falke, Albert and Johann, Ulrich and Harris, Alan},year={2017},booktitle={Planetary Defence Conference 2017, Tokyo, Japan},}
2016
NEOT\omegaIST-An Asteroid Impactor Mission Featuring Sub-spacecraft for Enhanced Mission Capability
Kilian Engel, Mattia Pugliatti, Line Drube, L Cano Juan, Eggl Siegfried, Daniel Hestroffer, Albert Falke, Ulrich Johann, and Alan Harris
In 67th International Astronautical Congress, Guadalajara, Mexico, Aug 2016
Near Earth Object (NEO) deflection for the purpose of planetary defense has become increasingly recognized as a valid and valuable endeavour. NEOTωIST stands for Near-Earth Object Transfer of angular momentum (ω) Spin Test. This describes a demonstration mission intended to develop the capabilities required to execute an effective kinetic impactor NEO deflection mission. The chosen measurement technique and employment of small subspacecraft for observation purposes represent a novel approach to achieving the main goals of such a demonstration mission. The approach promises comparatively low cost and features capabilities that are unique and valuable for an operational deflection mission. Most standard deflection demonstration missions propose to quantify momentum transfer from the impactor spacecraft to the target object by measuring a change in its heliocentric orbit. The change is typically so small that it must be performed via radio-science from a second observer spacecraft which rendezvous with the NEO prior to impact. In our case the NEO is struck off-center which changes its spin rate. This rate change, which can be measured from Earth via light curve measurements, allows quantification of the transferred momentum. Using this measurement method the need for an observer spacecraft for the purpose of NEO orbit measurement is eliminated. The second function of the observer spacecraft is the close-up observation of the impact event for improvement of impact effectiveness modelling. The NEOTωIST mission achieves this observation by deploying several small sub-spacecraft from the main impactor spacecraft shortly before impact. These subspacecraft allow observation of the impact event from multiple vantage points some of which are unique because their destruction is accepted. At least one sub-spacecraft trajectory is planned such that survival is guaranteed, which enables it to receive observation data from the other spacecraft for delayed transmission to Earth. We present the overall mission concept as well as preliminary design work on the key technical challenges, in particular those associated with the highly dynamic operation of the small sub-spacecraft that are a key feature of the NEOTωIST mission.
@inproceedings{engel2016neotomegaist,title={NEOT$\omega$IST-An Asteroid Impactor Mission Featuring Sub-spacecraft for Enhanced Mission Capability},author={Engel, Kilian and Pugliatti, Mattia and Drube, Line and Juan, L Cano and Siegfried, Eggl and Hestroffer, Daniel and Falke, Albert and Johann, Ulrich and Harris, Alan},year={2016},booktitle={67th International Astronautical Congress, Guadalajara, Mexico},}
