Edoardo Ciccarelli
About
My research project
Autonomous navigation techniques for deep space missionMy research wants to explore the possible benefits of adopting autonomous navigation techniques for deep space mission. At the state of the art, the navigation of most of the deep space mission relies on data from the Deep Space Network ground stations only. The main problem is that ground-based navigation becomes more and more stressful and work-demanding for ground operation teams as much as the number of missions increases. Moreover, the light-time required by signals to travel from spacecraft to Earth does not often allow autonomy for navigation operations once in deep space. Therefore, several different techniques of Optical navigation earned heritage in the last 20 years gaining the trust of the operators too. My research job explores the capabilities of these techniques and whether there is a chance to adopt artificial intelligence to leave autonomy to the spacecraft in the navigation process.
My research wants to explore the possible benefits of adopting autonomous navigation techniques for deep space mission. At the state of the art, the navigation of most of the deep space mission relies on data from the Deep Space Network ground stations only. The main problem is that ground-based navigation becomes more and more stressful and work-demanding for ground operation teams as much as the number of missions increases. Moreover, the light-time required by signals to travel from spacecraft to Earth does not often allow autonomy for navigation operations once in deep space. Therefore, several different techniques of Optical navigation earned heritage in the last 20 years gaining the trust of the operators too. My research job explores the capabilities of these techniques and whether there is a chance to adopt artificial intelligence to leave autonomy to the spacecraft in the navigation process.
News
Asteroid's 3D shape model from images.
Publications
Despite the advantages of very-low altitude retrograde orbits around Phobos, questions remain about the efficacy of conventional station-keeping strategies in preventing spacecraft such as the Martian Moons eXploration from escaping or impacting against the surface of the small irregular moon. This paper introduces new high-fidelity simulations in which the output of a sequential Square-Root Information Filter is combined with recently developed orbit maintenance strategies based on differential algebra and convex optimization methods. The position and velocity vector of the spacecraft are first estimated using range, range-rate, and additional onboard data types such as LIDAR and camera images. This information is later processed to assess the necessity of an orbit maintenance maneuver based on the estimated relative altitude of MMX about Phobos. If a maneuver is deemed necessary, the state of the spacecraft is fed to either a successive convex optimization procedure or a high-order target phase approach capable of providing sub-optimal station-keeping maneuvers. The performance of the two orbit maintenance approaches is assessed via Monte Carlo simulations and compared against work in the literature so as to identify points of strength and weaknesses.
The Martian Moons eXploration mission will be the first of its kind to sample and study Mars's moon Phobos for a prolonged period of time. The aim of this work is to show that the adoption of periodic and quasi-periodic retrograde trajectories would be beneficial for the scientific return of MMX. A consider covariance analysis is hereby implemented in order to compare the estimation of high-order gravitational field coefficients from different orbital geometries and processing different sets of observables. It is shown that low-altitude non-planar quasi-satellite orbits would refine the knowledge of the moon's gravity field.