AstroSim: A GNC simulation tool for small body environments

Peñarroya, Pelayo; Centuori, Simone; Hermosín, Pablo

doi:10.2514/6.2022-2355

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…These tools are often limited or kept confidential for strategic reasons because their development requires a lot of time and expertise, discouraging their implementation. To the best of the author's knowledge, other tools currently available in the literature encompass both the entire process of image generation from existing minor body shapes, such as SISPO [22], AstroSym [23], and the simulations tools illustrated in [24][25][26]. Alternatively, some tools focus on accurately reproducing surface morphologies, including craters and roughness, by incorporating noise functions, as demonstrated by AstroGen [27,28].…”

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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Section: Introductionmentioning

confidence: 99%

MONET: The Minor Body Generator Tool at DART Lab

Buonagura,

Pugliatti,

Topputo

2024

Sensors

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“…The tools can render high-fidelity imagery for close proximity applications, particularly about small bodies, focusing on the high-fidelity simulation of boulder fields over their surfaces. • AstroSym [22], developed in Python, provides a source of images for closed-loop simulation for Guidance Navigation and Control (GNC) systems for landing and close-proximity operations around asteroids. • SPyRender [23], also developed in Python, is used to generate high-fidelity images of the comet 67P for training data-driven IP methods for navigation applications.…”

mentioning

confidence: 99%

CORTO: The Celestial Object Rendering TOol at DART Lab

Pugliatti,

Buonagura,

Topputo

2023

Sensors

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The Celestial Object Rendering TOol (CORTO) offers a powerful solution for generating synthetic images of celestial bodies, catering to the needs of space mission design, algorithm development, and validation. Through rendering, noise modeling, hardware-in-the-loop testing, and post-processing functionalities, CORTO creates realistic scenarios. It offers a versatile and comprehensive solution for generating synthetic images of celestial bodies, aiding the development and validation of image processing and navigation algorithms for space missions. This work illustrates its functionalities in detail for the first time. The importance of a robust validation pipeline to test the tool’s accuracy against real mission images using metrics like normalized cross-correlation and structural similarity is also illustrated. CORTO is a valuable asset for advancing space exploration and navigation algorithm development and has already proven effective in various projects, including CubeSat design, lunar missions, and deep learning applications. While the tool currently covers a range of celestial body simulations, mainly focused on minor bodies and the Moon, future enhancements could broaden its capabilities to encompass additional planetary phenomena and environments.

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A LiDAR-less approach to autonomous hazard detection and avoidance systems based on semantic segmentation

Peñarroya

Centuori²,

Sanjurjo

et al. 2023

Celest Mech Dyn Astron

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In this paper, a passive hazard detection and avoidance (HDA) system is presented, relying only on images as observations. To process these images, convolutional neural networks (CNNs) are used to perform semantic segmentation and identify hazards corresponding to three different layers, namely feature detection, shadow detection, and slope estimation. The absence of active sensors such as light detection and ranging (LiDAR) makes it challenging to assess the surface geometry of a celestial body, and the training of the neural networks in this work is oriented towards coping with that drawback. The image data set for the training is generated using blender, and different body shape models (also referred to as meshes) are included, onto which stochastic feature populations and illumination conditions are imposed to produce a more diverse database. The CNNs are trained following a transfer learning approach to reduce the training effort and take advantage of previously trained networks. The results accurately predict the hazards in the images belonging to the data set, but struggle to yield successful predictions for the slope estimation, when images external to the data set are used, indicating that including the geometry of the target body in the training phase makes an impact on the quality of these predictions. The obtained predictions are composed to create safety maps, which are meant to be given as input to the guidance block of the spacecraft to evaluate the need for a manoeuvre to avoid hazardous areas. Additionally, preliminary hardware-in-the-loop (HIL) test results are included, in which the algorithms developed are confronted against images taken using real hardware.

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AstroSim: A GNC simulation tool for small body environments

Cited by 4 publications

References 8 publications

MONET: The Minor Body Generator Tool at DART Lab

MONET: The Minor Body Generator Tool at DART Lab

CORTO: The Celestial Object Rendering TOol at DART Lab

A LiDAR-less approach to autonomous hazard detection and avoidance systems based on semantic segmentation

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