About the lab

Deep-space Astrodynamics Research & Technology

Featured projects (1)

Project
Understand to what extent can AI-enhanced IP methods work for optical navigation and context understanding around small bodies

Featured research (41)

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.
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.
In this work, an orbit determination algorithm suitable for CubeSats onboard implementation is developed, which simulates optical autonomous navigation accomplished by a stand-alone platform. An extended Kalman filter featuring line-of-sight acquisitions of planets is selected as the state estimator, and its performances are tested on a Raspberry Pi, whose characteristics are comparable to a miniaturized onboard computer. An improvement of the solution accuracy is performed by correcting the planetary light-time and aberration effects as well as by exploiting the optimal beacons selection strategy to acquire the external observations. Moreover, the numerical precision of the estimator is improved through the implementation of factorization techniques and nondimensionalization strategies. The results are presented for a sample Earth–Mars transfer, where the time slot for the navigation campaign involves 2 h every 10 days. At final time, the probe position and velocity are estimated with a 3σ accuracy of 360 km and 0.04 m/s, respectively.
Ballistic capture corridors allow a spacecraft to be temporarily captured about a planet without any thrust firing. They represent a promising approach for future deep-space small-satellites missions, where only limited fuel can be carried onboard. In an effort to enable autonomous interplanetary CubeSats, a guidance algorithm based on convex optimization is exploited to design low-thrust minimum-fuel space trajectories which target ballistic capture corridors at Mars. An Hermite-Legendre-Gauss-Lobatto scheme with nonlinear control interpolation is used to discretize the trajectory. A variable time of flight version of the algorithm is developed and tested in closed-loop guidance simulations, where multiple reference trajectories need to be computed during a simulated interplanetary transfer. The variable time of flight algorithm is of paramount importance when closed-loop guidance is considered to avoid that no feasible solutions are found when the spacecraft is too close to the target celestial body. We show the effectiveness of the variable time of flight algorithm compared to the fixed time of flight one.

Lab head

Francesco Topputo
Department
  • Department of Aerospace Engineering
About Francesco Topputo
  • Prof. Topputo is a Full Professor of Space Systems at Politecnico di Milano, Italy, and holds a position as Visiting Professor at TU Delft, The Netherlands. His core research activities involve spacecraft flight dynamics, interplanetary CubeSat mission and system design, autonomous guidance, navigation, and control. Dr. Topputo is an ERC laureate (CoG 2019) and has been PI in 14 research projects, with over €4.1M research grants allocated to work under his direction. He leads the Deep- space Ast

Members (16)

Vittorio Franzese
  • Politecnico di Milano
Mattia Pugliatti
  • Politecnico di Milano
Alessandro Morselli
  • Politecnico di Milano
Carmine Giordano
  • Politecnico di Milano
Yang Wang
  • Nanjing University
Paolo Panicucci
  • Politecnico di Milano
Gianmario Merisio
  • Politecnico di Milano
Christian Hofmann
  • Politecnico di Milano

Alumni (4)

Diogene Alessandro Dei Tos
  • Politecnico di Milano
Simone Ceccherini
  • Politecnico di Milano
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Kenta Oshima
  • Hiroshima Institute of Technology