This work presents MONSTER, a Moon optical navigation robotic facility on simulated terrain, i.e., an experimental facility that can be used to simulate lunar navigation problems. The facility consists of a 3D Cartesian manipulator that, once fully operative, will be equipped with a spherical joint allowing to simulate both attitude and orbital dynamics. All the experiments that have been performed so far and are planned to be performed in the future are based on innovative and disruptive approaches using artificial intelligence (AI) algorithms. Indeed, a crater detection algorithm based on a fully convolutional neural network has been implemented, and a reinforcement learning approach is under development for prescribing the control policy of the simulated system. MONSTER enables hardware-in-the-loop simulations of landers and spacecrafts using AI hardware accelerators such a graphics processing unit (GPU), visual processing unit (VPU) and field programmable gate arrays (FPGA).

A Moon Optical Navigation Robotic Facility on Simulated TERrain: MONSTER / Latorre, Francesco; Carbone, Andrea; THOTTUCHIRAYIL SASIDHARAN, Sarathchandrakumar; Ciabatti, Giulia; Spiller, Dario; Curti, Fabio; Capobianco, Roberto. - In: THE JOURNAL OF THE ASTRONAUTICAL SCIENCES. - ISSN 2195-0571. - (2022). (Intervento presentato al convegno 2022 AAS/AIAA Astrodynamics Specialist Conference tenutosi a Charlotte, NC, USA).

A Moon Optical Navigation Robotic Facility on Simulated TERrain: MONSTER

Francesco Latorre
;
Sarathchandrakumar Thottuchirayil Sasidharan;Giulia Ciabatti;Dario Spiller;Fabio Curti;Roberto Capobianco
2022

Abstract

This work presents MONSTER, a Moon optical navigation robotic facility on simulated terrain, i.e., an experimental facility that can be used to simulate lunar navigation problems. The facility consists of a 3D Cartesian manipulator that, once fully operative, will be equipped with a spherical joint allowing to simulate both attitude and orbital dynamics. All the experiments that have been performed so far and are planned to be performed in the future are based on innovative and disruptive approaches using artificial intelligence (AI) algorithms. Indeed, a crater detection algorithm based on a fully convolutional neural network has been implemented, and a reinforcement learning approach is under development for prescribing the control policy of the simulated system. MONSTER enables hardware-in-the-loop simulations of landers and spacecrafts using AI hardware accelerators such a graphics processing unit (GPU), visual processing unit (VPU) and field programmable gate arrays (FPGA).
2022
2022 AAS/AIAA Astrodynamics Specialist Conference
aerospace engineering; robotics; artificial intelligence; autonomous landing
04 Pubblicazione in atti di convegno::04c Atto di convegno in rivista
A Moon Optical Navigation Robotic Facility on Simulated TERrain: MONSTER / Latorre, Francesco; Carbone, Andrea; THOTTUCHIRAYIL SASIDHARAN, Sarathchandrakumar; Ciabatti, Giulia; Spiller, Dario; Curti, Fabio; Capobianco, Roberto. - In: THE JOURNAL OF THE ASTRONAUTICAL SCIENCES. - ISSN 2195-0571. - (2022). (Intervento presentato al convegno 2022 AAS/AIAA Astrodynamics Specialist Conference tenutosi a Charlotte, NC, USA).
File allegati a questo prodotto
File Dimensione Formato  
Latorre_A -moon-optica-navigation_2022.pdf

solo gestori archivio

Note: articolo
Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 4.16 MB
Formato Adobe PDF
4.16 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1700251
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact