The Milani mission: overview and architecture of the optical-based GNC system

Pugliatti, Mattia; Rizza, Antonio; Piccolo, Felice; Franzese, V.; Bottiglieri, Claudio; Giordano, Carmine; Ferrari, Fábio; Topputo, Francesco

doi:10.2514/6.2022-2381

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The obtained results are shown in Figure 23 and Figure 24 for the FRP and CRP, respectively. The CoF error is mostly below 20 pixels for both cases which is consistent with the 1-

measurement noise given to the navigation filter [ 43 ]. The CoF error is higher for the CRP as the spacecraft is closer to the binary system which implies that the primary appears larger in the image.…”

Section: Applicationssupporting

confidence: 68%

The TinyV3RSE Hardware-in-the-Loop Vision-Based Navigation Facility

Panicucci

Topputo

2022

Sensors

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The increase in number of interplanetary probes has emphasized the need for spacecraft autonomy to reduce overall mission costs and to enable riskier operations without ground support. The perception of the external environment is a critical task for autonomous probes, being fundamental for both motion planning and actuation. Perception is often achieved using navigation sensors which provide measurements of the external environment. For space exploration purposes, cameras are among the sensors that provide navigation information with few constraints at the spacecraft system level. Image processing and vision-based navigation algorithms are exploited to extract information about the external environment and the probe’s position within it from images. It is thus crucial to have the capability to generate realistic image datasets to design, validate, and test autonomous algorithms. This goal is achieved with high-fidelity rendering engines and with hardware-in-the-loop simulations. This work focuses on the latter by presenting a facility developed and used at the Deep-space Astrodynamics Research and Technology (DART) Laboratory at Politecnico di Milano. First, the facility design relationships are established to select hardware components. The critical design parameters of the camera, lens system, and screen are identified and analytical relationships are developed among these parameters. Second, the performances achievable with the chosen components are analytically and numerically studied in terms of geometrical accuracy and optical distortions. Third, the calibration procedures compensating for hardware misalignment and errors are defined. Their performances are evaluated in a laboratory experiment to display the calibration quality. Finally, the facility applicability is demonstrated by testing imageprocessing algorithms for space exploration scenarios.

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“…The obtained results are shown in Figure 23 and Figure 24 for the FRP and CRP, respectively. The CoF error is mostly below 20 pixels for both cases which is consistent with the 1-

Section: Applicationssupporting

confidence: 68%

The TinyV3RSE Hardware-in-the-Loop Vision-Based Navigation Facility

Panicucci

Topputo

2022

Sensors

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“…These bodies provide incomparable information on the solar system formation and evolution, pose a constant threat to Earth, and serve as a potential source of precious materials that could be exploited [ 1 ]. Up until now, several missions such as Osiris-Rex [ 2 ], Rosetta [ 3 ], Hayabusa 1 [ 4 ], and 2 [ 5 ], Lucy [ 6 ], and DART [ 7 ] have been launched towards these targets, while others, such as Hera [ 7 ] with its two CubeSats, Milani [ 8 ] and Juventas [ 9 ], and M-ARGO [ 10 ], are planned for the future.…”

Section: Introductionmentioning

confidence: 99%

MONET: The Minor Body Generator Tool at DART Lab

Buonagura,

Pugliatti,

Topputo

2024

Sensors

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Minor bodies exhibit considerable variability in shape and surface morphology, posing challenges for spacecraft operations, which are further compounded by highly non-linear dynamics and limited communication windows with Earth. Additionally, uncertainties persist in the shape and surface morphology of minor bodies due to errors in ground-based estimation techniques. The growing need for autonomy underscores the importance of robust image processing and visual-based navigation methods. To address this demand, it is essential to conduct tests on a variety of body shapes and with different surface morphological features. This work introduces the procedural Minor bOdy geNErator Tool (MONET), implemented using an open-source 3D computer graphics software. The starting point of MONET is the three-dimensional mesh of a generic minor body, which is procedurally modified by introducing craters, boulders, and surface roughness, resulting in a photorealistic model. MONET offers the flexibility to generate a diverse range of shapes and surface morphological features, aiding in the recreation of various minor bodies. Users can fine-tune relevant parameters to create the desired conditions based on the specific application requirements. The tool offers the capability to generate two default families of models: rubble-pile, characterized by numerous different-sized boulders, and comet-like, reflecting the typical morphology of comets. MONET serves as a valuable resource for researchers and engineers involved in minor body exploration missions and related projects, providing insights into the adaptability and effectiveness of guidance and navigation techniques across a wide range of morphological scenarios.

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“…These can be either synthetically generated or acquired from real sensors. A detailed description of the IP is out of the scope of this work but can be found in [18], [20]. Only a brief overview is given hereafter.…”

Section: Introductionmentioning

confidence: 99%

The image processing of Milani: challenges after DART impact

Pugliatti,

Giordano,

Topputo

2023

Papers of ESA GNC-ICATT 2023

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Milani is a 6U CubeSat that is designed to visit the Didymos binary system in early 2027 as part of the Hera mission. Milani is expected to provide new insights into the Didymos system as well as demonstrate the capabilities of CubeSat technology for interplanetary missions. Its objectives are both scientific and technological: to study and characterize the asteroid environment, estimate its gravity field, characterize the dust environment, demonstrate inter-satellite communication capabilities with Hera, and demonstrate the use of CubeSat technologies in deep space. The image processing pipeline of Milani is a crucial part of the mission as it allows to extract valuable information from the images of the system, in particular about the primary body called Didymos, that is used as the main target for navigation purposes while the secondary body, Dimorphos, acts as disturbing effect on the images. The image processing algorithm is supported in its most advanced configuration by a global data-driven method that updates itself through new datasets of images. The image processing pipeline uses coefficients that need tuning through data obtained at different phases but also from different missions. The pipeline is designed to extract low-level and high-level observables such as the center of figure, range, and phase angle, which will be used by the on-board guidance, navigation, and control subsystem. This design has been chosen to allow the algorithm to accommodate variations throughout the mission duration without the need for algorithm updates. This particular choice has been made since the Didymos system will be visited twice before Milani's release, a unique opportunity that leaves some space for non-invasive improvements of the image processing algorithms before deployment. First, the system has been preliminarily investigated by the DART spacecraft in late 2022, which impacted Dimorphos, successfully altering its orbital motion around Didymos. Second, the system will be characterized by Hera itself, the mothercraft carrying Milani and Juventas CubeSat, which will study the asteroids for some months before Milani's release. Both data and images from these missions as well as from Milani itself can be used to generate meaningful datasets. The former can be used to define an artificial environment and generate synthetic images, the latter could be used directly, making sure proper image transformations are applied to fit with Milani camera's properties. In this work, we analyze the challenges of the image processing algorithm of Milani after the impact of the DART spacecraft on Dimorphos. In particular, the focus is set on the differences between the expected shape models of the asteroids used during the design phase of the algorithm and the observed ones and how these will impact the performance of the image processing. Indeed, it has been observed that both bodies are more flattened than previously thought and this seems to put an important stress on the current design. One of the preliminary results indicates that the oblateness of Dimorphos exceeds the range previously considered during the design, invalidating some of the assumptions of the fitting function used to estimate the phase angle with the eccentricity of the blobs of pixel of Didymos. Such an issue would require a new fitting function or a change of features considered for the evaluation. Both tasks however are not trivial and different strategies exist that can be used to perform the change. Among the strategy, the possibility to use feature-based neural networks and symbolic neural networks is explored to find meaningful relationships that can estimate the phase angle with the updated shape of Didymos.

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The Milani mission: overview and architecture of the optical-based GNC system

Cited by 4 publications

References 15 publications

The TinyV3RSE Hardware-in-the-Loop Vision-Based Navigation Facility

The TinyV3RSE Hardware-in-the-Loop Vision-Based Navigation Facility

MONET: The Minor Body Generator Tool at DART Lab

The image processing of Milani: challenges after DART impact

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